Appendix 1 Definition and explanation of term
279
Appendix１ Definition and explanation of term
The explanation of the important key word judged that describing clearly and the
explanation of the definition are necessary among terms used in this report is
individually added. The one thought that the explanation of the one used in a meaning
different from a usage (a) general and the limited meaning and the (b) supplementation
was usefulness on the business was published though the one published in "Drainage
glossary 2000 editions (corporation Japan Sewage Works Association)" was basically
omitted though it was a special term. As for the one judged that the generality of the
usage was not enough among these, the effect that was the term that fixed "*" to finding,
and was used in the peculiarity by this report was clear. Moreover, after the explanation,
this pertinent page of [shi] exhibit materials was fixed to the tail about the one that the
explanation is in the report.
≪ alphabet ≫
ASM
Abbreviation of "Activated Sludge Model (activated sludge model)".
In general, it is used generically of the activated sludge model of
IWA. This report is used generically of ASM series (ASM1-3).
(→ pp. 8, 10)
CODCr
Oxygen demand with dichromate potassium. The consumption of
the dichromate potassium when the oxide in the sample is oxidized
by using the dichromate potassium as an oxidant by one of the
water quality parameters that express the amount of the organism
in water is measured, and this is converted into the oxygen
consumption. Because the oxidizing power of the dichromate
potassium is strong, the catching rate of the organism is higher
than that of CODMn. It is used from obtaining measurements near
the absolute magnitude of the organism as a carbon-estimating
parameter by the activated sludge model of IWA.
(→ pp. 30, 323)
CODMn
Oxygen demand with overmanganese potassium. The consumption
of potassium permanganate when potassium permanganate is made
an oxidant by one of the water quality parameters that express the
amount of the organism in water and the oxide in the sample is
280
Appendix 1 Definition and explanation of term
oxidized is measured, and this is converted into the oxygen
consumption. The catching rate of the organism is smaller than that
of CODCr, and it is more unsuitable though it is an index COD
generally used in our country as the revenue and expenditure index.
Because a part of the organism such as the amino-acid, organic
acids, and the fatty acids biologically used easily is hardly oxidized,
it is usual to indicate the small value from BOD5 in the domestic
wastewater.
(→ pp. 55, 324)
Appendix 1 Definition and explanation of term
281
Expression of Monod
In the empirical equation that expresses the substrate
concentration dependency of the proliferation rate of the
microorganism proposed by Monod, it is the most general as the
mathematical expression of the proliferation rate. It is
characterized by the maximum specific growth rate and two
constants of saturation factor, and the speed decreasing rate to the
maximum specific growth rate is calculated by the magnitude
correlation of the substrate concentration and saturation factor. To
come extremely ..greatly.. ..the zero-order reactions.. more extreme
than saturation factor the substrate concentration with the small, it
approximates as an expression of first order reaction. Both are
essentially different though it is the same type as the expression of
Michaelis-Menten that is mathematically the theoretical formula of
the enzyme reaction rate.
(→ pp. 21)
OUR
Abbreviation of "Oxygen Uptake Rate (oxygen utilization speed)".
The one that use speed of oxygen by living being in samples such as
inflow drainage and activated sludge was measured. Measuring
changing DO, and calculating again as a decline in oxygen for each
amount of the unit solid for each unit volume are general. In the
field of the activated sludge model, it is used as one of the
experimental techniques to presume parameter value of the amount
of the organism element and the model in influent.
(→ pp. 116)
PHA
Abbreviation of "Polyhydroxyalkanoate (polyhydroxyalkanoate)".
Generically
of
two
or
more
compounds,
PHB
(polybeta-hydroxybutyric acid) is typical. It is synthesized by a lot of
microorganisms as a store organism, and it is stored to intracellular.
It plays an important role as a store organism by phosphorus
accumulation living being (PAO) in the biological phosphorus
removal process. It is not identical with PHA that is contained other
store organisms such as glycogens in this XPHA defined by ASM2d
is modeling this, and is measured by chemical analysis.
(→ pp. 32)
-t
Width of time interval of numerical calculation. It becomes base
unit when the amount of the time variance of each variable is
calculated by the simulation. Computing time becomes long a small
setting of this though it is desirable to set a small value enough to
the variable ratio of a function of change of variable value. There is
a simulator that uses the computational algorithm to adjust this by
the automatic operation though it is basically a numerical value
that the user should set, too.
(→ pp. 157)
282
Appendix 1 Definition and explanation of term
It is ≪ or ..doing..
≫.
Hydrolysis
Activated
model
Stoichiometry
Complete
Generic name of reaction that unites with water organic/inorganic
compound and is converted into another compound. When the
organism under drain is converted, the key role is borne as a
reaction from which high molecular compound that cannot take the
bacillus directly into intracellular in the biological waste water
treatment process is made a low molecular weight. It is thought
that the enzyme reaction caused on the cell surface is a subject. It is
known to progress from reluctance to the conceit on a wide
condition. The finding concerning a kinetics characteristic is
insufficient though it is thought that the speed decreases under the
anoxic condition reluctance/Though it is defined as a process that
XS converts into SF in ASM2d, the hydrolysis process will bear a
part of a lot of phenomena involved in living being's such as the use
of the intracellular store organism and preying done by the
protozoan autolysis for the model structure (concept of
autolysis-reproduction) that XS generated in the autolysis process
of the living being similarly contributes to the consumption of
oxygen and the nitrate through hydrolysis, too, and it is different
from a pure hydrolysis reaction. Because the concept of endoecism
breath is adopted in ASM3 and the store and the their use of the
organism were modeled separately, the contribution of the
hydrolysis process is decreased.
(→ pp. 32)
sludge Biological response model by whom internal structure of various
phenomena in activated sludge mainly composed of biological
response is mathematically described. The simulation of the
activated sludge process becomes possible incidental models such as
the sedimentation ponds and the diffused air because it models the
entire combination activated sludge process this. On the business,
the activated sludge process model including the incidental model
might be only called an activated sludge model.
(→ pp. 6)
It uses it in this report though the entire study that handles a
quantitative character and the relation of the compound is indicated
to the wide sense by the wording in the narrow sense that means a
relative relation of the variation of the compound by the process of
the reaction. It is an important corner of the content of the
description of ASM, and it is displayed by the matrix type. Refer to
"Amount of theory coefficient".
(→ pp. 14)
mixing Reactor where all materials in tank exist uniformly. The material
Appendix 1 Definition and explanation of term
tank
283
that flows in is always uniformly instantaneously distributed to the
entire tank, and the composition of the discharge becomes in the
tank and identical. It is difficult to actually achieve such a state,
and has an intermediate characteristic of the complete mixing tank
and the plug flow tank by a reactive tank of the active sludge
process. It is used as base unit that composes a reactive tank in the
activated sludge process model do not to have to consider the spatial
gradient of the element in the tank and to excel in mathematical
handling.
(→ pp. 139)
284
Appendix 1 Definition and explanation of term
Sensitivity analysis
It is indicated to examine the response of the output to the model to
the logical input value. When the data collection, the calibration,
and the simulation result are evaluated by examining the influence
that parameter value, the facilities condition, the inflow condition,
and the operating condition, etc. in the model give the simulation
result, useful information can be obtained when the business of
ASM is used.
(→ pp. 157, 367)
Calibration
Work to adjust parameter value etc. of model and to improve
accuracy of expectation. The entire work to improve the
reproducibility of the processing results of the object facilities
including the facilities condition, the influent quality, and the
adjustment of the input data of the operating condition etc. and the
verification of the adjustment result is located with the calibration
in this report though the optimization of parameter value in the
model is typical.
(→ pp. 146)
Ultimate BOD
BOD value when BOD examination is executed for a long term, and
state that all organisms of possible biodegradation in sample (The
amount of the organism of the living being is included) are oxidized
is assumed. It might be written as BODu or UBOD, etc.In general,
the model assumed to be progress of the oxidation of the organism
to remaining BOD by first order reaction for the BOD value in
which the same sample was measured at two or more periods is
applied and calculated. When the organism element of influent is
drawn in the amount as an index that reflects the gross weight of
the organism that can be biologically resolved, it is possible to use it
when the activated sludge model is used. However, because part
remains finally as an inert organism and doesn't contribute to BOD
measurements, it is necessary to correct this though it was used for
biosynthesis among organisms of a possible biodegradation that
exists in the sample while culturing it.
(→ pp. 119)
Glycogen
It is wide in the organism and exists by a kind of the polysaccharide
as an energy storage material. When do the metabolism of PAO that
bears the biological phosphorus removal, it has the key role as a
reducing power source of supply for the PHA synthesis under the
anoxic in waste water treatment (However, the accumulated dose of
the glycogen ..behavior of PAO.. doesn't usually become a phase rule
[suru] condition easily). In addition, glycogen accumulation living
being (GAO) that makes not the polyphosphate but the glycogen an
energy source and stores the organism under the anoxic is one
factor of the deterioration of the biological phosphorus removal. The
metabolism of the glycogen by PAO is not modeled in ASM2d, and
the glycogen is not defined as an independent element (It is
included in XPHA as an amount of the organism).
Appendix 1 Definition and explanation of term
285
(→ pp. 43, 50)
286
Appendix 1 Definition and explanation of term
Glycogen
accumulation
being
living
The living being that has the ability to convert calles it generically
to ..organic substrate such as acetates.. intracellular as an energy
source and a reducing power source of supply of an intracellular
glycogen under the anoxic the reserve substance such as uptakes
and PHA. It is abbreviated with "GAO(Glycogen Accumulating
Organisms)". In aerobic condition, regeneration and the
proliferation of the glycogen using intracellular PHA are done as
well as PAO. The polyphosphate is made an energy source and the
behavior seen in PAO like Rin's discharge and uptake, etc. doesn't
accompany the place because it doesn't require it. The phosphorus
removal deteriorates when it is in the relation that competes with
PAO in the biological phosphorus removal process, and the ratio of
GAO increases. The contribution of GAO can indirectly be expressed
as an element by parameter value of XPAO because it is included in
XPAO though this living being is not modeled in ASM2d.
(→ pp. 44, 50, 176)
Work to confirm the predictive accuracy of the model is said. It is
important to use, to verify a different data set because in the
calibration of the model, there is danger of falling into the result of
specializing in the data used there, and to confirm the generality of
the calibration result.
(→ pp. 177)
Verification
..doing.. ≫ ≪
Intracellular
material
Oxygen
speed
store Generic
name of compound stored to intracellular. The
polyphosphate is typical as PHA, the glycogen, and inorganic
compound as organic compound. The intracellular store material is
properly resolved, and used , saying that the energy source and the
carbon source. In the biological phosphorus removal process, Rin is
removed by being exhausted outside the faction as an excess sludge
with PAO shows feature behavior through PHA, the glycogen, and
the polyphosphate, and the polyphosphate accumulated in
intracellular in a high density finally. To model this, two
intracellular store materials named XPHA and XPP are defined in
ASM2d. Moreover, it is assumed that heterotrophs XH other than
PAO proliferate through the store, and has been changed the
approach that expresses the use of the organism greatly in ASM3.
(→ pp. 46)
utilization Refer to "OUR".
Appendix 1 Definition and explanation of term
Autolysis
287
Generic name of phenomenon of weight and revitalization of
bacillus decreasing. There are the one by an inner factor and the
one by the external factor, and consumption, the resolution, and the
latter of an intracellular compound like the reserve substance etc.
include preying by the protozoan and bacteriolysis by the chemical
substance and the pH, etc. in the former. It is more important to
express "Endoecism breath" phenomenon observed from the
accurate expression of this from a microbiology aspect when the
activated sludge process is modeled as a phenomenon, adopts
"Concept of autolysis-reproduction" as an approach for that in
ASM1-2d, and "Concept of endoecism breath" is adopted in ASM3.
In ASM2d, the autolysis process is defined respectively about
intracellular store material XPHA and XPP of another and PAO
from which an independent autolysis process is defined in three
kinds of living being elements respectively. The process where the
intracellular store material is used is separately defined in XPAO
though the autolysis process of XH includes all phenomena of the
biomass's like preying by the protozoan and the use of the
intracellular store material, etc. decreasing.
(→ pp. 32)
Concept
of It is adopted with ASM1-2d by one of the modeling methodologies of
autolysis-reproducti "Endoecism breath" phenomenon. Living being's autolysis generates
the organic substrate and the modeling is done by assumption that
on
oxygen (It is a nitrate in the anoxic condition) is consumed when
accompanying (Part becomes an inert organism) and this are used
again. As for "Autolysis" process of the living being, when it is
defined as a mere transformation process from the living being to
XS and XI, and caused XS is used again through the hydrolysis
process, oxygen and the first nitrate are consumed in ASM2d. The
point that the loop of the organism is formed in a series of process
from hydrolysis to autolysis is a big feature. Because a part of the
amount of autolysis is used to proliferate again about XH and
XPAO, it is necessary to use a value that is bigger than the
endoecism respiratory rate experimentally observed for the
autolysis rate constant (bH and bPAO, etc.). Moreover, because a
part of the organism of the autolysis origin is converted into XI
when self-resolved again after it proliferates, yield fXI of XI is set
smaller than usual endoecism breath models. On the other hand,
because XS after autolysis is not used for XAUT about XAUT,
autolysis rate constant (bAUT ) becomes the decline and equal the
living being (revitalization) experimentally observed. "Concept of
endoecism breath" is adopted in ASM3, and a big change point of
the model concerned (Refer to "Endoecism breath").
(→ pp. 32, 35)
Scenario *
Generic name of condition setting it as examination object by
simulation. For instance, when there is a target of the simulation
"Processing status when the amount of the oxygen supply is
decreased more than the current state is expected" in the given
activated sludge process, an individual calculation condition of
288
Appendix 1 Definition and explanation of term
decreasing the amount of the oxygen supply is considered to be one
scenario respectively (The usage of the former is used in this report
though the usage that considers one scenario bringing the polarity
of the discussion "It is decreased the amount of the oxygen supply"
together is done, too).
(→ pp. 95)
Appendix 1 Definition and explanation of term
289
Simulation
It is said to expect and to analyze it on the computer on the basis of
the model of the system of the reality. Numerical calculation under
the logical input value and parameter value appropriate after the
process model that builds this in is constructed on the computer for
the activated sludge model is done, and corresponding to work to
express and to expect the behavior of the object facilities.
(→ pp. 6)
Simulator
Generic name of application program to execute simulation on
computer. It is indispensable to execute the simulation, except when
the program can be made by oneself. It is usual to comprise the
construction of the process model, condition setting support facility,
and the result output function in various methods though the
subject of the function is numerical calculation under the logical
input value given of the process model. Moreover, there are a lot of
one to install various presumption, analysis, and optimization tools
(influent quality setting supporting tool, sensitivity analysis tool,
and parameter optimization tool, etc.) in a simulator on the market.
(→ pp. 9)
Heterotroph
Generic name of living being that needs organic compound as
carbon source for proliferation. Biosynthesis is done by generating
energy from taken organic compound by fermentation and breath,
and using organic compound taken similarly. The heterotroph of
higher-order also influences the clarification etc. of the flocculation
and the treated water from the protozoan etc. though the one
(heterotrophic bacterium) classified into the bacillus bears the
leading part of conversion in the biological waste water treatment
process (The denitrificans and PAO are included). "Heterotroph XH"
corresponds to these in ASM2d. Though this element chiefly
modeled a typical function of the heterotrophic bacterium of the
facultative aerobic (The one according to PAO is excluded) on the
basis of an average and kinetics and stoichiometry characteristics,
the activities of the living being other than the bacillus such as
constructional of the model and preying done by the protozoan are
partially contained, and it doesn't correspond to a specific bacillus
and the living being of the reality (Therefore, the name
"Heterotrophic bacterium" is not used).
(→ pp. 29)
Nitrifying bacteria
Generic name of bacillus that bears nitrification. The ammonia is
composed in the nitrite and oxidizing ammonium oxidizing bacteria
and nitrite are composed of bacterial groups of second classes of
oxidizing nitrite-oxidizing bacterium by the ion nitrate. Both
majority are autotrophic bacterias of [katayosaga] aerobic (The
living being that is the nitrification of the heterotrophy type exists,
too), and the proliferation rate and the growth yield are smalls in
the carbon fixation because it requires big energy compared with
the heterotrophic bacterium. In ASM1-3, the nitrification function is
290
Appendix 1 Definition and explanation of term
modeled by living being element XAUT of the kind, and the
ammonia does a big simplification of being oxidized (The nitrite is
not
considered)
directly
to
the
ion
nitrate
there.
Organism..amount..ammonium oxidizing bacteria..nitrite-oxidizing
bacterium..both..contain..model..growth
yield..maximum..specific
growth
rate..individual..bacillus..one..directly..comparable..attention..neces
sary.
(→ pp. 29)
Appendix 1 Definition and explanation of term
291
State variable
Generic name of variable that expresses state of system in model.
"Element" is corresponding in ASM in this. Initial condition and the
boundary condition are set to each state variable, and the value of
each state variable becomes an immediate output in the simulation.
(→ pp. 19)
Initial condition
Generic name of value of each state variable when beginning to
calculate at numerical calculation by simulation (initial value). In
the simulation using ASM, should it correspond to the initial value
of the all component in the calculated all divisions (reactive each
tank division, final sedimentation tank, sending back dirt, and
internal circulation liquid, etc.), and the user set it. If it is a purpose
to obtain the stationary solution after a long term is simulated, the
necessity for paying set big attention is a no bur (However, because
it influences computing time to reaching regularly by each element,
the stationary solution to which the simulation is executed on a
certain condition is often given as a initial value).
Because initial
condition of a floating element has a big influence on the calculation
result when simulating it for a short term, an enough discussion is
necessary for the set point.
(→ pp. 150)
Switch function
In the reaction rate equation of ASM, the expression paragraph to
express the dependency to the density of electron acceptor
(SO2 ,SNO3 ) is called generically. An individual process is enabled
to be conceited and to express the progression rate in the hypoxia
and reluctance each condition by the same rate equation in
combining the function of the Monod type and the obstruction type
concerning the density of each electron acceptor.
(→ pp. 24)
Numerical
calculation
The model expressed by the differential equation etc. is said and the
numeric is said under parameter value given of initial condition and
the boundary condition solving. Requesting approximation solution
from the numeric because it is analytical this impossible to solve it
becomes the substance of the simulation ("Solution of the equation"
is a value of the state variable (= element) here at all divisions and
the total times). As a numerical calculation method though ASM is
described as a mathematical simultaneous ordinary differential
equations though the Kutta method is Runge ..Euler
method..-typical, * Various computational algorithms that attempt
shortening computing time as t is assumed to be changeable or each
element is changed are used, too.
(→ pp. 157)
Accuracy *
It uses in the meaning that the expectation by the simulation is
near "Accuracy of the expectation" how to be adequately expressible
of the phenomenon of the reality, and this is not made an index
292
Appendix 1 Definition and explanation of term
strictly in this report in the statistical though it means the
difference of data. For instance, it only has to use the sum of
squared deviation etc. of predictor and observed value when making
it to a numeric index.
(→ pp. 62)
Appendix 1 Definition and explanation of term
293
Element
Generically of the water quality item defined in ASM, the one that
is called "State variable" in general hits this. It is substance of the
simulation to calculate the time variance of the all component
density after the influent quality of the all component and the
initial value of each division are set. It is arranged in line writing
direction in the stoichiometry matrix.
(→ pp. 19)
Kinetics constant
Many of parameters used in ASM come under this by the constant
used in the reaction rate equation. Saturation factor (In general, it
is written by "Ki (I: component name)") that provides for the
dependency to the maximum ratio speed (It might be called the rate
constant; maximum specific growth rate μ and ratio autolysis speed
b, etc.) that provides for the maxima at the speed and the element
densities is typical.
When the simulation is executed, it is treated
as a constant, and information (As for this, the effect is described
clearly in the technical report in ASM though the default value is
given to each constant no part of ASM it) that the user should set.
(→ pp. 20)
..doing.. ≫ ≪
Store
Phenomenon that converts compound that living being took into
intracellular into reserve substance (PHA, glycogen, and
polyphosphate, etc.) and accumulates temporarily. In the
environment to which the substrate supply and the environmental
condition are steady, the living being that has the storage capacity
might have a predominance in the advantage in which the
metabolism like this in the condition that a no bur and these change
greatly too much. The organism can be temporarily stored because
the means to oxidize this in the reluctance tank where the biological
phosphorus removal process , for example, a conceited
reluctance-method is typical, and the organic substrate is supplied
and to generate energy is limited, energy be generated by breath
using this in a continuing conceited tank, and the living being that
does the metabolic activity of proliferation etc. use the organic
substrate preferentially. In this case, it is PAO to be necessary also
for the store with the reluctance tank energy for the incorporation
of the substrate and conversion into the reserve substance, and to
obtain this because an inorganic energy storage material
polyphosphate is hydrolyzed. In ASM2d, the store process of XPHA
and XPP is defined only about XPAO to express the function of PAO.
It is clarified that a lot of heterotrophic bacteriums excluding this
also store the organic substrate, and modeled the proliferation also
of heterotrophs XH other than PAO through the store in ASM3.
(→ pp. 46)
294
Subsidence model
Appendix 1 Definition and explanation of term
Generic name of the model by whom phenomenon of floating
material's subsiding in primary sedimentation tank and final
sedimentation tank, etc. is expressed. Various dirt subsidence
models to express the solid-liquid separation in the final
sedimentation tank are typical.
(→ pp. 67)
Appendix 1 Definition and explanation of term
295
Stationary solution
Value of element when value of all component (state variable) is
steady (Do not change timewise) by the process of the simulation
calculation. In a regular calculation into which the input condition
doesn't change timewise, the state when the value of all elements
settles to a constant (It is possible to consider it) value is considered
to be regular. Moreover, the state that the same time variance
pattern during a day came to be repeated might be considered to be
regular in the case given repeating the time varying during a day in
the non-stationary calculation. The influence with big SRT is given
in the operating condition though the calculation period to obtaining
the stationary solution depends on initial condition.
(→ pp. 151)
Regular calculation
Simulation calculation in condition that logical input value of
simulation like influent quantity, water quality, and operating
condition, etc. doesn't change timewise. It might be called, "Regular
simulation" and "Static simulation", etc.In the simulation using
ASM, the corresponding of the condition etc. to use the day mean for
all logical input values.
(→ pp. 152)
Technical report *
The report of ASM that "Task group concerning mathematical
model to support the design and driving the biological waste water
treatment" of IWA made public is indicated and it uses it in this
report. "Activated Sludge Models ASM1, ASM2, ASM2d and ASM3"
(chapter of this task group, Scientific and Technical Report No.9,
and IWA Publishing) that arranges these is published in 2000
though each version of ASM has been made public independently
severally in the form of the report and the thesis.
The one
published in this as a technical report is indicated in this report as
long as there is especially no refusal (There is a part where the
correction was added, too and the content is not identical though
this report is bringing of a basically original thesis etc. together).
The Japanese translation book on this report is published in 2005.
(→ pp. 11)
Electron acceptor
Generic name of compound that receives electron in redox reaction
(It is reduced). The compound that discharges the electron is called
"Electron donor" etc.Oxygen and the ion nitrate in breath as the
terminal electron acceptor are important (It is called, "Oxygen
breathing" and "Nitrate respiration", etc. respectively; The
denitrification is latter one form), and in the activated sludge
process, these are indicated and it uses it in this report.
(→ pp. 29)
296
Tracer experiment
Appendix 1 Definition and explanation of term
In general, the one having aimed to examine flowing and problem
data conversion such as a waterway in the sewage plant, a
distribution tank, reactive tanks, and the final sedimentation tanks
is only called a tracer experiment in this report though it is a
generic name of the examination that examines the behavior of the
material with a tracer (material that can be pursued). When the
activated sludge model is used, it is possible to use it to obtain
necessary information when the distribution situation of influent is
confirmed, a reactive tank is evaluated problem data conversion,
and the process model is constructed chiefly. Adsorption and the
resolution in the research objective part are few, the measurement
at high frequency is possible, and the requirement such as without
the adverse effect on facilities and the processing function is
requested, and the lithium chloride is used for the material used as
a tracer comparatively well in our country. Besides, another of
sodium chloride and dyestuff such as rhodamine are used.
(→ pp. 106, 138)
..doing.. ≫ of ≪
Endoecism breath
The bacillus etc. do not consume the extracellular organic substrate,
the done breathing activity is indicated to the narrow sense, and
because an intracellular organic substrate (reserve substance etc.)
is oxidized, the energy (It is called the maintenance energy etc. ; It
is thought the energy to maintain the density gradient in the inside
and outside of the cell membrane and to reproduce various
enzymes) to maintain the cell is obtained. However, the amount of
breath that originates in other phenomena like the use of the
organism generated with the activity and the bacteriolysis of the
protozoan in addition to this etc. joins the respiratory rate observed
on the condition that the organic substrate doesn't exist in liquor in
the activated sludge process, and this is called endoecism breath to
the wide sense. There are a concept etc. of the concept of ①
autolysis-reproduction, the concept of ② endoecism breath, and ③
self-maintenance as an approach to model this, and ① is used in
ASM1-2d (Refer to "Concept of autolysis-reproduction"). In ②
adopted with ASM3, the consumption of living being's decrease and
electron acceptor (oxygen and nitrate) corresponds directly, and
correspondence with the endoecism respiratory rate experimentally
obtained is clear. However, it is necessary to note it no confusion
because the meaning of the rate constant (bH etc.) and fXI is greatly
different from the model before ASM2d the numerical value of both.
(→ pp. 34, 35)
Input data *
It uses it generically of the data that the user should set when the
simulation using ASM is done. Various parameters (reactive tank
pump composition and capacity etc. of the tank) for the process
Appendix 1 Definition and explanation of term
297
model construction and various flow rate, the influent qualities,
water temperatures, and the control values are included (In the
wide sense, all parameter values are added to this; The setting of
parameter value is user's responsibility matter).
(→ pp. 96)
298
Appendix 1 Definition and explanation of term
≪ is ..doing.. ≫.
Fermentation
Generic name of reaction that oxidizes organism in reluctance by
microorganism of heterotrophy type and generates energy.
Intracellular organic compound is used as an electron acceptor, and
it is converted into another organic compound. The acid
fermentation and the methane fermentation in the anaerobic
digester tank are typical in the sewage plant. Moreover, it has the
meaning to PAO in the reluctance tank as the substrate supply
process so that PAO that bears the biological phosphorus removal
may use organic acid etc. generated by fermentation as an organic
substrate. A great simplification is performed to the fermentation
phenomenon of the reality as this doesn't accompany the
proliferation of XH in the one that the substrate supply function to
XPAO was modeled though the fermentation process is defined as a
conversion process from SF by XH to SA in ASM2d.
(→ pp. 32, 47)
Parameter
Generic name of various constant and coefficients used in model. In
ASM, it is divided into three kinds (the kinetics constant, amount of
theory coefficient, and the conversion factor). These are the
illustration values, and the setting of parameter value has been
entrusted to them by the user though the numerical value (default
value) is presented in all parameters by the technical report. Work
(narrow sense) to presume and to optimize parameter value on the
basis of the processing results data and observed data is called a
calibration.
(→ pp. 19)
Reaction
equation
Kinetics
rate It is the one that the reaction rate was expressed by the expression,
and substance of the kinetics of ASM. In general, it is described as a
differential equation that shows the transformation rate of a certain
element (state variable). It is composed of the kinetics constant and
the element. In ASM, the impact combination bur of various
elements is expressed by the mathematical expression based on the
expression of Monod, and the factor that influences the speed of the
process in seeing the reaction rate equation can be understood.
(→ pp. 20)
It uses it as a term that shows modeling and the mathematical
expression of the reaction rate in this report though the study whole
concerning the reaction rate is indicated to the wide sense. It has
the frame of the content of the description of ASM with the
stoichiometry. Substance is a procession of "Reaction rate equation"
in each process. The term "Kinetic" that faithfully translates this
into Japanese might be used though this term corresponds to
"kinetics" of the original of the technical report.
(→ pp. 14)
Appendix 1 Definition and explanation of term
299
300
Appendix 1 Definition and explanation of term
Water quality profile In this report, it uses it as a term that shows the change in the
water quality along direction where a reactive tank flows. In
* of reactive tank
general, it is important to understand the change of an ammoniacal
nitrogen, nitrate-nitrogen, and phosphoric acid Rin, etc. by the
process (flowing process) in a reactive tank in the insufficiency, and
to presume the phenomenon caused by each division only by
referring to the treated effluent quality to understand processing
status in a reactive tank. Even if it is data "Snap
shot ..adoption.." ..do not consider [mizu] interval.. based on the
obtaining water, the utility value is large though data
that ..adoption.. did [mizu] from such an intention one by one on the
basis of a mean of the day of the water quality density of each
division and an actual flowing speed of a reactive tank (HRT of each
division) is desirable.
(→ pp. 109)
Non-stationary
calculation
Simulation calculation in condition that logical input value of
simulation like influent quantity, water quality, and operating
condition, etc. changes timewise. It might be called, "Unsteady
simulation" and "Dynamic simulation", etc.In the simulation using
ASM, the corresponding of the simulation by real time that assumes
a condition of giving the logical input value the time varying and a
specific period etc.
(→ pp. 152)
Surface limiting type It is used by the method of expressing the rate equation of the
reaction caused on the cell surface in ASM to express the substrate
rate equation
concentration dependency of the hydrolysis process. The substrate
concentration doesn't influence the direct reaction speed like the
Monod type but the substrate and amount of ratio of the living
being have the functional with which the matrix part of the Monod
type is replaced by "Ratio of substrate/living being" (XS /XH ratio in
case of the hydrolysis process) on the basis of the idea with essential
(Such a rate equation is called "Expression of Contois").
(→ pp. 23)
Fitting
Work to adjust parameter value and the input data of the model so
that the simulation result may agree with the measurement data is
indicated. The criteria of "Agreement" is various from the level to
the case to use a numeric index of the error sum of squares etc. by
which the shown in the figure both data is compared. Moreover, the
method is also various from the trial and error by people to an
automatic fitting using mathematical optimization.
(→ pp. 147)
Incidental model *
It uses it generically of the model who is combined with the
activated sludge model as a component of the activated sludge
process model and used. In this report, it introduces the reactor
Appendix 1 Definition and explanation of term
301
[mizukotowari] model, the oxygen transfer model, the
sedimentation pond model, the pump flow model, the aerification
style amount model, and the influent quality model, etc.It might be
called, "Submodel".
(→ pp. 8, 10)
302
Appendix 1 Definition and explanation of term
Material kind
Amount of substances such as elements and charges that become
objects that take revenue and expenditure in ASM. Five kinds of
material kinds of organism (COD), nitrogen (N), Rin (P), charge
(mole+), and suspended solid (TSS) are defined for ASM2d. One
kind of the material that is basic is allocated in all elements among
models, and the content of other material kinds is defined by the
conversion factor. As for the stoichiometry of ASM, it descends
strictly on the basis of the material conservation law, and the gross
weight of each material kind is constant in all the reaction
processes.
(→ pp. 18)
Mass balance
Quantitative revenue and expenditure to change by inflow going out
and reaction, etc. of material (element on element, compound, and
model etc.). When a certain area is defined, element of revenue and
expenditure, (Each breakdown is included), and these whole is a
mass balance. the outflow to the inlet flow from the outside of the
material that becomes an object and the outside and the variations
in thatWhen the revenue and expenditure relation has adjusted, it
is said, "Revenue and expenditure is taken" etc.Moreover, the state
that all the revenue and expenditure elements are understood from
the measurement data etc. might be called "Revenue and
expenditure shuts". It is widely used to confirm the validity of the
measurement data and the condition setting, and to presume a
difficult element to measure. ASM is a model based on the mass
balance, all the revenue and expenditure elements are clear, and
revenue and expenditure of all the material kinds and elements
adjusts. Therefore, neither the calculation result nor the
measurement data will agree fundamentally with that when data
that there is unmatch in the mass balance is used as a logical input
value. Therefore, it is assumed that the correspondence of the mass
balance of the data used is confirmed before the work of the
calibration etc. in this report. Moreover, measuring it directly by
using the mass balance like the amount of breath and the amount
etc. of the denitrification can presume the results value of a difficult
variation of the material.
(→ pp. 16, 127, 130)
Process
Generic name of reaction defined in ASM. 21 kinds of processes are
defined in ASM2d. It is arranged in columnwise direction in the
stoichiometry matrix, and the variation of the element by an
individual process is shown in the each line.
(→ pp. 18)
Process model *
Various incidental models are combined with the activated sludge
model, and it uses it as a name of the model by whom the
simulation as the activated sludge process is enabled. It means
work to define and to put these incidental models when saying,
"Construction of the process model", and it becomes work like the
Appendix 1 Definition and explanation of term
303
composition, the capacity of a reactive tank and the final
sedimentation tank, and the oxygen supplies in various ducts and
conceited tanks, etc. to set the physical parameters of facilities
chiefly.
(→ pp. 8)
304
Appendix 1 Definition and explanation of term
Conversion factor
The coefficient to convert a different material cross-species is called
generically by a kind of the parameter of ASM. It is used to describe
the stoichiometry of the process, and it is written (c: material
specific name and i: component name) as "ic,i ". There is "iTSS,i " to
calculate TSS(XTSS ) from the floatage element in addition to a
series of "iN,i " and "iP,i " that defines nitrogen and the phosphorus
containing rate of each organism element.
(→ pp. 20)
Saturation factor
It is used by the function of the Monod type by one of the kinetics
constants. The sign of "K" is allotted usually. Mathematically, it is
corresponding in the substrate concentration when the value of
clause Monod becomes 0.5. Because the value of clause Monod is
decided by the magnitude correlation of the substrate concentration
and saturation factor, it is considered the constant that expresses
intimacy of the living being to the substrate. For instance, the
influence (If you do not model the floc structure) that the diffusion
situation of the substrate to the inside of the activated sludge floc
gives the reaction rate is typically reflected in the value of
saturation factor.
(→ pp. 21)
Polyphosphate
Generic name of polymer of phosphoric acid shown by
H2PO3(HO3P) nPO4H2. Intracellular of the microorganism is
generated from the phosphate ion, and accumulated as an energy
storage material in the state of the granule though is industrially
synthesized. The accumulated polyphosphate is used for the
substrate intake, use from which the energy generation means of
breath etc. is limited under the condition etc. , and the energy
generation for the self-maintenance (It is converted into the
phosphate ion by hydrolysis). The biological phosphorus removal is
a method of removing Rin more than a usual process as a
polyphosphate that accumulates PAO. Intracellular store material
XPP of PAO corresponds in ASM2d, and it is modeled as an element
besides XPAO.
(→ pp. 29, 50)
≪ ..rolling.. [gyou
≫]
Matrix
The stoichiometry matrix that is the method of writing ASM is
indicated and used. In the matrix, the process is arranged in
columnwise direction, the element is arranged in line writing
direction, and the variation of the element by the process is
displayed in each cell. Therefore, a quantitative relation of the
element conversion in each process is shown in the each line and
Appendix 1 Definition and explanation of term
305
the element change according to all the processes will be shown in
each row (Because it is a relative relation of the element variation
in each process that is filled in to each cell, it is necessary to
multiply the reaction rate to calculate the variation). The content of
the model is understood and when modifying it, it is very useful
because it is possible to have a look at the structure of the model.
(→ pp. 11, 14, 27)
Modeling
Generic name of act making model of system of reality. It is called,
"Model" etc.It is corresponding to the description of this as the
expression for mathematical model on the basis of the characteristic
of the system of the reality.
(→ pp. 6)
306
Appendix 1 Definition and explanation of term
≪ and ..doing.. ≫
Volatile organic acid (VFA) seen in the influent of the sewage plant
is only called organic acid in this report though it is defined in
general as "Generic name of organic compound that shows acidity".
The acetate is a subject in influent, and the propionate is often
detected. Moreover, there is a case where the formic acid is detected
by the inflow of the industrial effluent etc. , too. It is not included in
the domestic wastewater immediately after the exhaust so much,
and it is thought that it is generated by fermentation in the sewer.
The density in influent greatly influences the behavior of the
denitrification and the biological phosphorus removal because it is
promptly used for a lot of heterotrophic bacteriums including PAO.
The meaning of this element is in division with "Organism (SF )
that can use only XH", and is not strictly identical with organic acid
though is modeled as SA in ASM2d.
(→ pp. 31)
Organic acid
..doing.. ≫ ≪
Influent
segmentation *
Amount of
coefficient
The work whole to measure and to presume the amount (density) in
influent of the element defined in ASM is indicated. The
presumption work of the organism element might be indicated
because the center is presumption of the organism element and it be
used (When it is emphasized to limit it to the organism element, it
is said, "Segmentation of the influent organism element" etc.). The
presumption act that uses a simple method in addition to the case
to presume the OUR measurement etc. experimentally is included.
When the model is used because the organism element used in ASM
doesn't correspond a usual water quality parameter and directly, it
is necessary to do some influent segmentations, and to define the
influent quality for the simulation. The reactive tank influent
quality is used as an influent quality, except when the
sedimentation pond model is used the beginning.
(→ pp. 113, 309, 323)
theory Generic name of parameter used to describe stoichiometry in ASM.
It is called, "Stoichiometric coefficient". The one to show the weight
ratio rate of the element consumed by the process of the reaction
and the generated element is a subject, and growth yield
(YH ,YPAO ,YAUT ) in which the living being formation per amount
of the substrate is defined is typical. Besides, there is coefficient
(fSI ,fXI ) in which the yield of coefficient (YPO4 ,YPHA ) and an
inert organism that discharges by PAO by Rin and takes and
defines the relation of amount of theory of [toki] is defined. The
variation of each element is shown in each cell of the stoichiometry
Appendix 1 Definition and explanation of term
307
matrix by using these amount of theory coefficient and conversion
factor.
(→ pp. 20)
308
Phosphorus
accumulation
being
Appendix 1 Definition and explanation of term
living
Loess pyrogram
It is called "PAO(Phosphate Accumulating Organisms)" by the living
being that becomes the subject of the biological phosphorus
removal. It has the ability to take the organic substrate such as
organic acids promptly by using energy that hydrolysis of the
polyphosphate stored to intracellular and obtains it (The glycogen is
consumed as a reducing power) under the anoxic and to store ,
saying that PHA (The phosphate ion is discharged into the
extracellular by this process). Under aerobic condition moreover, the
polyphosphate and the glycogen ..resynthesis.. proliferate by breath
using this PHA (The phosphate ion in liquor is taken by this
process, and Rin will be removed only as for the amount of
proliferating PAO). Therefore, the state that PAO proliferates
preferentially than other heterotrophs can be formed by repeating
the anoxic and aerobic condition. PAO is distinguished from these in
the point to show the above-mentioned behavior under aerobic
condition of reluctance though a lot of living beings that accumulate
the polyphosphate in intracellular as an energy storage material are
known, and "Poly-P accumulation living being (Poly-P organisms)"
etc.
are
called
as
for
these.
Living
being..element..define..accumulation..polyphosphate..accumulation..
proliferation..autolysis..behavior..model..glycogen..use..reproduction
..incorporate.Moreover, the intracellular store material is defined as
element (XPHA ,XPP ) besides XPAO. It is known that a part of
PAO has the denitrification ability (Poly-P accumulation and
proliferate by the denitrification under the anoxic condition), and
the point to have modeled this is the main improvement point from
ASM2 to ASM2d.
(→ pp. 29, 38)
In the one that the time variance of the respiratory rate
(consumption speed of the electron acceptor) was graphed, the time
variance curve of OUR is typical. The area enclosed by the
respiratory rate curve and the time base on the loess pyrogram is
corresponding in the amount of breath in the range during that
time. It is a sole method of the influent segmentation and the model
and the parameter estimation to discuss the model structure and
parameter value that can reproduce the loess pyrogram obtained for
the respiratory rate measurement.
(→ pp. 310)
309
Appendix 2Sign list and translation with the original
Appendix２Sign list and translation with the original
The list of the sign used in the sign used with ASM2d, the list, the translation with the
original of the process name, and this report is published. As for a Japanese translation,
it is a Japanese translation basically book on the technical report.
found.It
Error! Reference source not
drinks, it conforms to the term, and there is the one that the partial modification
was added, too.
１．Sign and process name used with ASM2d
（１）Element
Element
Unit
English mark
Soluble Components (S? )
Japanese mark
Dissolubility
element
SO2
gO2/m3
Dissolved oxygen
Dissolved oxygen
SF
gCOD/m3
Fermentable,
readily
biodegradableorganic sabstrates
Fermentative
organism
SA
gCOD/m3
Fermentation products
Fermentation product (acetate
etc.)
SNH4
gN/m3
Ammonium plus ammonia nitrogen
Ammoniacal nitrogen
SNO3
gN/m3
Nitrate plus nitrite nitrogen
Nitrate-nitrogen
nitrogen
SPO4
gP/m3
Inorganic soluble phosphorus
Dissolubility inorganic phosphate
SI
gCOD/m3
Inert soluble organic matterial
Dissolubility inert organism
SALK
moleHCO3-/m3
Alkalinity
Alkalinity
SN2
gN/m3
Dinitrogen, N2
Chokedamp
easy-degradable
and
nitrite
310
Element
Appendix2 Sign list and translation with the original
Unit
English mark
Particulate Components (X? )
Japanese mark
Floating element
XI
gCOD/m3
Inert particulate organic material
Floating inert organism
XS
gCOD/m3
Slowly biodegradable substrate
Slow, resolved organism
XH
gCOD/m3
Heterotrophic organisms
Heterotroph
XPAO
gCOD/m3
Phosphorus-accumulating
organisms: PAO
Phosphorus accumulation living
being
XPP
gCOD/m3
Poly-phosphate
Polyphosphate
XPHA
gCOD/m3
A cell internal storage product of
PAO
Phosphorus accumulation living
being's
intracellular
store
material (PHA etc.)
XAUT
gCOD/m3
Nitrifying organisms
Nitrifying bacteria
XMeOH
gFe(OH)3/m3
Metal-hydroxides
Metal hydroxide (soluble metals)
XMeP
gFePO4/m3
Metal-phosphate
Metallic phosphate compound
XTSS
gTSS/m3
Total suspended solids, TSS
Suspended solid
311
Appendix 2Sign list and translation with the original
（２）Parameter
Parameter
Unit
English mark
Conversion Factors
Japanese mark
Conversion factor
IN,SI
gN/gCOD
N content of intert soluble COD SI
Nitrogen content in SI
IN,SF
gN/gCOD
N content of soluble substrate SF
Nitrogen content in SF
IN,XI
gN/gCOD
N content of intert particulate COD
XI
Nitrogen content in XI
IN,XS
gN/gCOD
N content of particulate substrate XS
Nitrogen content in XS
IN,BM
gN/gCOD
N content of biomass XH , XPAO ,
XAUT
Nitrogen content of living being
(XH , XPAO , XAUT )
IP,SI
gP/gCOD
P content of intert soluble COD SI
Phosphorus containing rate in SI
IP,SF
gP/gCOD
P content of soluble substrate SF
Phosphorus containing rate in
SF
IN,XI
gP/gCOD
P content of intert particulate COD
XI
Rin materials content in XI
IN,XS
gP/gCOD
P content of particulate substrate XS
Phosphorus containing rate in
XS
IN,BM
gP/gCOD
P content of biomass XH , XPAO ,
XAUT
Phosphorus containing rate of
living being (XH , XPAO , XAUT )
ITSS,XI
gTSS/gCOD
TSS to XI ratio
Equivalent of TSS in XI
ITSS,XS
gTSS/gCOD
TSS to XS ratio
Equivalent of TSS in XS
ITSS,BM
gTSS/gCOD
TSS to biomass ratio for XH , XPAO ,
XAUT
Equivalent of TSS of living being
(XH , XPAO , XAUT )
Stoichiometric Coefficients
Stoichiometric
coefficient
fSI
gCOD/gCOD
Fraction of inert COD in particulate
substrate
Ratio from which XS is
converted into SI by hydrolysis
YH
gCOD/gCOD
Yield coefficient
Growth yield of XH
fXI
gCOD/gCOD
Fraction of inert COD generated in
biomass lysis
Ratio from which XH is
converted into XI by autolysis
YPAO
gCOD/gCOD
Yield coefficient (biomass/PHA)
Growth
XPHA
YPO4
gP/gCOD
Yield requirement (SPO4 release) for
PHA storage
SPO4 burst size necessary for
store of XPHA
yield
of
XPAO)(per
312
Parameter
Appendix2 Sign list and translation with the original
Unit
English mark
Japanese mark
YPHA
gCOD/gP
PHA requirement for PP storage
XPHA necessary for store of
XPP
YAUT
gCOD/gN
Yield coefficient (biomass/nitrate)
Growth
SNO3
yield
of
XAUT)(per
Kinetics constant
(hydrolysis relation)
Kinetic Parameters (Hydrolysis)
The maximum ratio hydrolysis
rate
Kh
1/d
Hydrolysis rate constant
ηNO3 (ηNO3,S )
-
Anoxic hydrolysis reduction factor
ηfe
-
Anaerobic
factor
KO2 (KO2,S )
gO2/m3
Saturation/inhibition coefficient for
oxygen
Saturation
and
constant to SO2
inhibition
KNO3 (KNO3,S )
gN/m3
Saturation/inhibition coefficient for
nitrate
Saturation
and
constant to SNO3
inhibition
KX
gXS/gXH
Saturation coefficient for particulate
COD
Saturation factor in hydrolysis to
XS /XH
hydrolysis
reduction
The hydrolysis rate decrease
coefficient
under
anoxic
condition
The hydrolysis rate decrease
coefficient under anoxic
Kinetics constant
(XH relation)
Kinetic Parameters (XH )
μH
gXS/(gXH･d)
Maximum growth rate of substrate
The maximum specific growth
rate of XH
qfe
gSF/(gXH･d)
Maximum rate for fermentation
The maximum ratio fermentation
speed
ηNO3 (ηNO3,H )
-
Reduction factor for denitrification
The proliferation rate decrease
coefficient of XH under anoxic
condition
bH
1/d
Rate constant for lysis and decay
Ratio autolysis speed of XH
KO2 (KO2,H )
gO2/m3
Saturation/inhibition coefficient for
oxygen
Saturation
and
constant to SO2
KF
gCOD/m3
Saturation coefficient for growth on
SF
Saturation factor in proliferation
of XH to SF
Kfe
gCOD/m3
Saturation
conefficient
fermentatin of SF
Saturation factor in fermentation
to SF
KA (KA,H )
gCOD/m3
Saturation coefficient for growth on
SA
Saturation factor to SA
KNO3 (KNO3,H )
gN/m3
Saturation/inhibition coefficient for
nitrate
Saturation
and
constant to SNO3
KNH4 (KNH4,H )
gN/m3
Saturation coefficient for ammonium
(nutrient)
Saturation factor to SNH4
for
inhibition
inhibition
313
Appendix 2Sign list and translation with the original
Parameter
Unit
English mark
Japanese mark
KP (KP,H )
gP/m3
Saturation coefficient for phosphate
(nutrient)
Saturation factor to SPO4
KALK (KALK,H )
moleHCO3-/m3
Saturation coefficient for alkalinity
Saturation factor to SALK
Kinetics constant
(XPAO relation)
Kinetic Parameters (XPAO )
qPHA
gXPHA/
(gXPAO･d)
Rate constant for storage of XPHA
The maximum ratio store speed
of XPAO
qPP
gXPP/(gXPAO
･d)
Rate constant for storage of XPP
The maximum ratio store speed
of XPP
μPAO
1/d
Maximum growth rate of PAO
The maximum specific growth
rate of XPAO
ηNO3 (ηNO3,P )
-
Reduction factor for anoxic activity
Proliferation of XPAO under
anoxic condition and store rate
decrease coefficient of XPP
bPAO
1/d
Rate for lysis of XPAO
Ratio autolysis speed of XPAO
bPP
1/d
Rate for lysis of XPP
Ratio autolysis speed of XPP
bPHA
1/d
Rate for lysis of XPHA
Ratio autolysis speed of XPHA
KO2 (KO2,P )
gO2/m3
Saturation/inhibition coefficient for
oxygen
Saturation
and
constant to SO2
inhibition
KNO3 (KNO3,P )
gN/m3
Saturation/inhibition coefficient for
nitrate
Saturation
and
constant to SNO3
inhibition
KA (KA,P )
gCOD/m3
Saturation coefficient for SA
Saturation factor to SA
KNH4 (KNH4,P )
gN/m3
Saturation coefficient for ammonium
(nutrient)
Saturation factor to SNH4
KPS
gP/m3
Saturation
coefficient
phosphorous in storage of PP
for
Saturation factor in store of XPP
to SPO4
KP
gP/m3
Saturation coefficient for phosphate
(nutrient)
Saturation factor in proliferation
of XPAO to SPO4
KALK (KALK,P )
moleHCO3-/m3
Saturation coefficient for alkalinity
Saturation factor to SALK
KPP
gXPP/(gXPAO
･d)
Saturation
coefficient
poly-phosphate
KMAX
gXPP/(gXPAO
･d)
Maximum ratio of XPP /XPAO
Maximum XPP content of XPHA
KIPP
gXPP/(gXPAO
･d)
Inhibition coefficient for PP storage
Inhibition constant in store of
XPP
KPHA
gXPHA /
(gXPAO･d)
Saturation coefficient for PHA
XPHA/
XPAO
for
XPP in store of XPHA/
Saturation factor to XPAO
Saturation
factor
to
314
Appendix2 Sign list and translation with the original
Parameter
Unit
English mark
Kinetic Parameters (XAUT )
Kinetics constant
(XAUT relation)
μAUT
1/d
Maximum growth rate of XAUT
The maximum specific growth
rate of XAUT
bAUT
1/d
Decay rate of XAUT
Ratio autolysis speed of XAUT
KO2 (KO2,A )
gO2/m3
Saturation coefficient for oxygen
Saturation factor to SO2
KNH4 (KNH4,A )
gN/m3
Saturation coefficient for ammonium
(substrate)
Saturation factor to SNH4
KALK (KALK,A )
moleHCO3-/m3
Saturation coefficient for alkalinity
Saturation factor to SALK
KP (KP,A )
gP/m3
Saturation coefficient for phosphate
(nutrient)
Saturation factor to SPO4
Kinetic Parameters (precipitation)
-
Japanese mark
Kinetics constant
(cohesion relation of
Rin)
kPRE
m 3/
(gFe(OH)3･d)
Rate constant for P precipitation
SPO4 insoluble
constant
kRED
1/d
Rate constant for redissolution
XMeP
constant
KALK (KALK,M )
moleHCO3-/m3
Saturation coefficient for alkalinity
Saturation factor to SAL K
making
solubilization
In the technical report, the sign that shows the type of the process in the suffix writing if
necessary is used in this report in addition because saturation factor in each process might be
actually discussed, a different numerical value be set, and it is confusing though the same sign
(KO2 ,KNO3 ,KNH4 ,KP ,KALK ) is used to write saturation and the inhibition constant in the
whole process (S: hydrolysis, H:XH, P:XPAO, A:XAUT, and M: Rin's coagulating sedimentation).
These are shown by parenthesis writing in the above table.
rate
rate
Appendix 2Sign list and translation with the original
315
（３）Process
Process name (English mark)
Process name (Japanese mark)
Hydrolysis processes
Hydrolysis relation
1 Aerobic hydrolysis
Hydrolysis under aerobic condition
2 Anoxic hydrolysis
Hydrolysis under anoxic condition
3 Anaerobic hydrolysis
Hydrolysis under anoxic)
XH relation
Heterotrophic organisms: XH
4 Growth on fermentable substrates SF
Proliferation under aerobic condition of XH of
which substrate is SF
5 Growth on fermentaion products, SA
Proliferation under aerobic condition of XH of
which substrate is SA
Denitrification
with
fermentable Proliferation under anoxic condition of XH of
which substrate is SF (denitrification)
Denitrification
with
fermentaion Proliferation under anoxic condition of XH of
which substrate is SA (denitrification)
6 substrates, S
F
7 products, S
A
8 Fermentation
Fermentation
9 Lysis
Autolysis of XH
Phosphorous-accumulatingorganisms:
XPAO
XPAO relation
10 Storage of XPHA
Store of XPHA
11 Aerobic storage of XPP
Store of XPP under aerobic condition
12 Anoxic storage of XPP
Store of XPP under anoxic condition
13 Aerobic growth on XPHA
Proliferation of XPAO under aerobic condition
14 Anoxic growth on XPHA
Proliferation of XPAO under anoxic condition
15 Lysis of XPAO
Autolysis of XPAO
16 Lysis of XPP
Autolysis of XPP
17 Lysis of XPHA
Autolysis of XPHA
316
Appendix2 Sign list and translation with the original
Process name (English mark)
Process name (Japanese mark)
Nitrifying organisms: XAUT
XAUT relation
18 Aerobic growth of XAUT
Proliferation of XAUT
19 Lysis of XAUT
Autolysis of XAUT
Simultaneous precipitation of
phosphorus with ferric hydroxide
Rin's chemical precipitation
20 Precipitation
Making of SPO4 insoluble
21 Redissolution
Solubilization of XMeP
Appendix 2Sign list and translation with the original
317
２．Other sign and abbreviation, etc.
（１）Sign
Sign
Unit
Content
ak
-
Step inflow ratio to k steps
bH'
1/d
Ratio autolysis speed of XH that corrects regeneration
of XH
CCOD,degrad
gCOD/m3
Amount of CODCr that XH uses (influent concentration
conversion)
CCOD,in
gCOD/m3
CODCr density of influent
CCOD,out
gCOD/m3
CODCr density of treated water
CN,nit
gN/m3
Nitrogen concentration of influent for nitrification
CNO3,in
gN/m3
Density of influent (nitrate-nitrogen + nitrite nitrogen)
CNO3,out
gN/m3
Density of treated water (nitrate-nitrogen + nitrite
nitrogen)
COrgN,in
gN/m3
Organic nitrogen concentration of influent
COrgP,in
gP/m3
Organic phosphorus concentration of influent
CTKN,in
gN/m3
Kjeldahl character nitrogen concentration of influent
CTKN,out
gN/m3
Kjeldahl character nitrogen concentration of treated
water
CTN,in
gN/m3
Density of total nitrogen of influent
CTN,out
gN/m3
Density of total nitrogen of treated water
CTP,in
gP/m3
Density of total phosphorus of influent
CTP,out
gP/m3
Density of total phosphorus of treated water
CODdenit
gCOD/d
Amount of denitrification breath
CODO2resp
gCOD/d
Amount of oxygen breathing
fCOD,X
gCOD/gSS
CODCr content of dirt
318
Appendix2 Sign list and translation with the original
Sign
Unit
Content
fN,X
gN/gSS
Nitrogen content of dirt
fP,X
gP/gSS
Phosphorus containing rate of dirt
H
m
Depth of reactive tank
L
m
The total extension of reactive tank (direction of flowing)
N
Tank
Tank number of partitions
Ndenit
gN/d
Amount of denitrification
Nnit
gN/d
Amount of nitrification
Q
m3/d
Flow rate and quantity of water to be treated
QW
m3/d
Excess sludge flow rate
r
-
Dirt sending back ratio)(per influent quantity
R
-
Internal circulation ratio)(per influent quantity
dSO2,aer /dt
gO2 /d
Oxygen supply speed
SO2,s
gO2 /m3
Saturation dissolved oxygen concentration
V
m3
Capacity of reactive tank
W
m
Width of reactive tank
X
gSS/m3, gTSS/m3
Solid concentration
XSS,ave
gSS/m3
Average SS density of reactive tank
XSS,in
gSS/m3
SS density of influent
XSS,k
gSS/m3
SS density of k in reactive tank steps
XSS,r
gSS/m3
SS density of sending back dirt
XSS,T1
gSS/m3
SS density of the first division in reactive tank
Appendix 2Sign list and translation with the original
Sign
Unit
Content
XSS,W
gSS/m3
SS density of excess sludge
ΘX
d
SRT
τ
d
HRT
319
320
Appendix2 Sign list and translation with the original
（２）Abbreviation
Abbreviation
English mark
Japanese mark
ASRT
Aerobic solids retention time
Aerobic solid dwell time
ASM
Activated Sludge Model
Activated sludge model
BODu
Ultimate BOD
Ultimate BOD
CFD
Computational fluid dynamics
Computational fluid dynamics
GAO
Glycogen accumulating organisms
Glycogen
being
HRT
Hydraulic retention time
Hydraulic residence time
IWA
International Water Association
International water society
PAO
Phosphorous accumulating organisms
Phosphorus accumulation living
being
PHA
Polyhydroxy alkanoate
Polyhydroxyalkanoate
polyhydroxy fatty acid
PHB
Poly-Β-hydroxy butyrate
Poly-Β hydroxybutyrate
SRT
Solids retention time
Solid dwell time
VFA
Volatile fatty acid
Volatile organic acid
accumulation
Bibliography
2A)
"Activated sludge model ASM1, ASM2, ASM2d, and ASM3" [**] Mino ([**yaku]):
Environmental newspaper and 2005.
living
and
Appendix 3Experimental method for influent organic matter fractuation
321
Appendix３Experimental fractionation of influent organism element
１．Outline
In ASM2d, the organism is classified into index (SA ,SF ,SI ,XS ,XI ,XH ) based on
peculiar biodegradability and it handles it. Therefore, when the influent quality for the
simulation is set, the density of each organism element will be set. The composition
ratio of these elements is ("because it differs greatly according to the waste treatment
facility. Error! Reference source not found. Error! Reference source not found.Refer to")
It is important to do the setting that reflects the realities of the object facilities (It is
based on = measurement data).
The method of presuming the organism element density in influent can be divided
roughly into the method of (a) experimental presumption and the method of converting
and converting (b) from other water quality parameters. Here, it introduces the one
executed as an example of the method mainly composed of the measurement of typical
oxygen utilization speed (OUR) in JS as an experimental estimation method. When the
activated sludge model is used in the business, it is necessarily profitable to understand
the outline because the necessity for adopting such a complex method is an estimation
method that comparatively agrees with the definition of the organism element of a no
bur and the model inside according to the purpose. Moreover, it is general for the
targeted influent to characterize influent when general is different from the municipal
sewage (The industrial effluent of the amount that cannot be disregarded mixes) by the
method of evaluating such "Biodegradability", and to add the correction to the structure
of the model if necessary.
２．Principle of presumption using OUR
OUR measures the oxygen utilization speed of the supply water sample as its name
suggests. Two NH4-N as the substrate of the organism of (a) possible biodegradation
and the (b) nitrification is a subject for general influent of the sewage plant assuming
that with a biological oxygen consumption of the elements. Therefore, if OUR is
measured on the condition of the addition of allylthio urea (ATU) etc. and controlling
nitrification, OUR according to the consumption of the organism of a possible
322
Appendix 3Experimental method for influent organic matter fractuation
biodegradation is chiefly appreciable. Here, because OUR reflects the consumption
speed of the organism by the living being, the difference of the degradation rate of the
organism can be evaluated by the difference of the OUR level. Because the degradation
rate of the organism is decided depending on a biomass in which it is borne and the
kinetics characteristic, these can be presumed from measurements of OUR. Moreover,
because the time integration value of OUR in a specific section corresponds to the
quantity of oxygen consumed there when OUR of the sample to which the consumption
of the organism progresses is successively measured, presuming the amount of the
organism consumed in the section becomes possible.
Appendix 3Experimental method for influent organic matter fractuation
323
The one that the time variance curve of OUR when the dirt that exists in the endoecism
respiratory condition is added to the drainage sample including organisms of possible
biodegradations of second classes named SS and XS as a typical example, and OUR is
measured successively (This is called "Loess pyrogram" etc.) was shown typicalFigure
A3. 1..going out.. .Then, "Interpretation" is as follows on the basis of the description of
ASM2d. this
・ OUR increases rapidly after adding influent. This reflects the oxygen
consumption according to the proliferation of XH using SS in influent.
・ This high OUR continues until SS becomes a low concentration and this becomes the rate
limiting factor of the proliferation process. OUR has increased gradually for this period
because XH increases by proliferation.
・ OUR decreases rapidly because the supply of SS by hydrolysis of XS becomes the limiting of
the proliferation of XH when SS becomes a low concentration.
・ It arrives at a first endoecism breath level when OUR decreases as XS is consumed, and this
dries up.
Among these, (of the oxygen consumption in the section where first OUR is high. Figure
A3. 1..It corresponds to ..drinking.. area of [ko] ..saying.. [mou] multiplication
part).. ..oxygen consumption according to consumption of SS of the influent origin.. (of
the oxygen consumption in the hydrolysis limiting section afterwards. Figure A3.
1Drink..say..multiply..area..correspond..influent..origin..cause..consumption..oxygen
consumption..consider..with..each..section..consume..organism..amount..that
is..influent..amount..calculation..for instance.Figure A3. 1If the oxygen consumption
according to the consumption of SS of the influent or more origin is calculated, the
amount of SS in influent can be presumed from following equation.
S S ,in 
1
 OURS S  dt
1  YH 
Expression (A3. 1)
SS and in ..here..: Growth yield - of OUR mgO2 /L/h according to the consumption
of SS density mgCOD/L of the influent origin and OURSs :SS and in and YH:XH
and ..going out.. ..
In this presumption, YH is necessary as already-known information. Because
appearance YH rises according to the contribution when the store process of SS
324
Appendix 3Experimental method for influent organic matter fractuation
described with ASM3 progresses at the same time, this can be corrected though it is
natural to use the default value of ASM2d.
Appendix 3Experimental method for influent organic matter fractuation
①下水添加後の高いOURの挙動
基質制限が無いXHの増殖を反映
→ μH、XHの推定
⑤高OUR区間の面積
325
②OUR急減時の挙動
SS枯渇による増殖速度低下を反映
→ KSの推定
⑥底部OURの面積
下水中のXS由来のSS を利用した
増殖に伴う酸素消費量に相当
→ 下水中のXSの推定
下水中のSSを利用した増殖に伴う
酸素消費量に相当
→ 下水中のSSの推定
→ YHの推定（SS既知の場合）
③SS 枯渇後のOURの挙動
加水分解速度の変化を反映
→ Kh、KX、XSの推定
④SS およびXS枯渇後のOURの挙動
OUR
XHの自己分解を反映
→ bHの推定
OURの時間変化曲線
下水添加
時間
SSを利用した増殖
XS の加水分解
XHの自己分解
OURを律している反応プロセス
Figure A3. 1Conceptual diagram of the interpretation method based on OUR measurement result
that uses influent and dirt and ASM2d
On the other hand, high OUR(OUR0 ) immediately after addition of influent is ..(..
expression in which the organic substrate reflects proliferation rate (μH ･XH ) of XH in
the condition of not becoming a limiting.
OUR0 
1  YH
 H  X H
YH
Expression (A3. 2))｡
Expression (A3. 2)
Therefore, if YH and μH are assumed (default value etc.), the expression.
Expression (A3. 2)The amount of XH in the sample or more can be presumed. It
326
Appendix 3Experimental method for influent organic matter fractuation
is also possible to presume the amount of XH in influent if a similar measurement is
done by not adding dirt, and using only the living being in influent.
327
Appendix 3Experimental method for influent organic matter fractuation
XH 
OUR0
YH

1  YH  H
Expression (A3. 3)
Therefore, SS, XS, and the XH density of influent can fundamentally be presumed by
using the OUR measurement. However, because the dirt when a long measuring time
(for instance, several days) begins to be needed, and to measure it should be strictly
assumed to be "Endoecism respiratory condition" to presume XS by the above-mentioned
method,
application
is
actually
difficult.
Therefore,
presumption
like
the
above-mentioned limits to SS, and presumes the density where the model can reproduce
the loess pyrogram with the fitting about XS.
Because any inactive ingredients (SI ,XI ) not biologically used at all do not influence the
measurement result of OUR as a definition, it is fundamentally impossible to presume
these. OUR single measurement it
As for "Interpretation" of the OUR measurement result, like the above-mentioned, it is
necessary to note a point different depending on the assumed model. For instance, in the
model including the store process like ASM3Figure A3. 1Because proliferation
progresses at the same time there, too presuming the oxygen consumption that
originates in an individual process becomes very difficult though it is interpreted that
high initial OUR in [noyouna] [re] spiro gram strongly reflected the store process from
proliferation. It is in fact impossible that use and hydrolysis of the reserve substance
individually similarly handle these of the behavior after SS dries up because
simulataneous progress. Moreover, the expression. Expression (A3. 3)Then, there is no
paragraph according to autolysis because it assumes "Concept of autolysis-reproduction"
adopted with ASM2d as a description of living being's autolysis. When "Concept of
endoecism breath" like ASM3 is assumed, it is necessary to add the paragraph where
OUR according to autolysis is expressed.
On the other hand, when the living being that has the storage capacity in the dirt of the
measuring object excels, and OUR of measuring initial depends on the store process
strongly actually, a part of SS used will be considered to be XS in the above-mentioned
method based on ASM2d that doesn't consider the store by XH.
Error! Reference source not found.｡
It is undesirable to undervalue the amount of the organism promptly used from the
aspect that describes the amount of the organism actually used by XH in a specific
section (It is included to express the competition over the organic substrate between XH
and XPAO) though the use of the store organism by XH becomes such presumption from
the standpoint of dividing SS and XS according to the difference of the respiratory rate
328
Appendix 3Experimental method for influent organic matter fractuation
because it is included in the hydrolysis process. It is necessary to temper with the
oxygen consumption when the store organism in the section after OUR decreases is used
to proliferate to understand the amount of the organism truly used here, and it is in fact
impossible to distinguish this from the oxygen consumption according to the use of the
organism of the hydrolysis origin on the loess pyrogram when XS coexists.
The measurement that adds the acetate of the known amount is concurrently executed,
and the process of presuming yield used to calculate SS from the difference of an initial
oxygen consumption by the presence of the acetate addition is built in from such
circumstances in the following estimation methods. In this, corresponding is "to the
correction of the contribution of the store of measuring initial in the high OUR section.
Error! Reference source not found. Error! Reference source not found.In investigation
of actual conditions intended for 16 places in the sewage plant shown in", estimate value
in yield is a value whose YH of 0.69-0.84(0.77 on the average) and ASM2d is obviously
higher than that of default value (0.625). Actually, simulataneous progress to
proliferation that uses an external substrate directly and proliferation, etc. that use the
store and the store organism in the high OUR section of (b) initial with a possibility
different from time when the growth yield when the (a) acetate is used uses other
organisms in drainage.
Because it doesn't have the rationality that the proper move
method is enough as there is no guarantee that can do the extrapolation of yield when
the acetate is added because each contribution is different depending on the amount of
the organism of experimenting initial, it proposes to consider the estimated result of SF
to be the uncertain one, and to add to the adjustment candidate at the calibration in
report.
329
Appendix 3Experimental method for influent organic matter fractuation
３．Fractionation procedure
（１）Estimation method of each element
The estimation method of each organism elementTable A3. 1It is as [ni] is brought
together.
Table A3. 1Estimation method of influent organism element
Element
Necessary
sample
Estimation method
SA
(fermentation product)
Measurements
propionate).
SF
(easily
decomposable
organic matter that can be
fermented)
After SS is presumed from OUR (influent + dirt)
measurement result, SA is subtracted.
SI
(dissolubility inert organism)
XI
(floating inert organism)
XS
(slow, resolved organism)
Fitting to OUR (influent + dirt) measurement result.
Or, it presumes from ultimate BOD.
Influent
sending back
dirt
XH
(heterotroph)
Presumption
from
measurement result.
Influent
Other living being
(XPAO , XPHA , XAUT )
elements
of
organic
acid
(acetate
+
Dissolubility CODCr measurements of treated
water.
(Dissolubility BOD5 and NO2-N are corrected if
necessary.
)
Another
element
is subtracted from total CODCr of
influent.
OUR
(influent
single)
Influent
Influent
sending back
dirt
Treated water
Influent
Disregard (In the simulation, the low concentration
is set).
（２）Collection of sample
The following samples are collected. All, it is basic to use the one of facilities for the
simulation. Because the density may change into elements such as SA and SF even if it
is the refrigeration condition of 4℃ under when time has passed since it collectedError!
Reference source not found., Error! Reference source not found.､The
following measuring water qualities and
the OUR measurement operations are promptly executed.
・ Influent
・ Sending back dirt
・ Treated water
330
Appendix 3Experimental method for influent organic matter fractuation
（３）Measuring water quality
A at least necessary water analyses are as follows for the organism element
segmentation.
・ Influent: CODCr and organic acid (acetate and propionate)
・ Treated water: Dissolubility CODCr, dissolubility BOD5, and NO2-N
（４）OUR measurement
Device
(..device measured successively DO density of attendant test solution to monitor OUR
necessary..
Appendix 3Experimental method for influent organic matter fractuation
331
Table A3. 2)｡For the unit that measures the DO density, the near sealing up structural
one is used to avoid the dissolution of oxygen from the atmosphere.
Measurement condition
332
Appendix 3Experimental method for influent organic matter fractuation
Table A3. 3The measurement on four [ni] conditions of showing (condition 1-4) is
assumed to be basic. However, condition 2 is omitted when it is inside to be able to
disregard the amount the organism dirt (An empty aerification enough for prior is done)
and when YH is not corrected, condition 3 can be omitted.
Additionally, the one for which each condition is used as a common measurement
condition in JS is as follows.
・ Mixing ratio of influent and sending back dirt: 10:1 (capacitor ratio)
・ Water temperature: 20±1℃
・ Range of measurement DO: 3-6 Mg/L
・ Uptake of DO data: 0.5s pitch (Averaging what of each 10s is output).
Appendix 3Experimental method for influent organic matter fractuation
333
Table A3. 2Example of device for OUR measurement3C)
Example of device
Feature
･Culture and the measurement are done in the
airtight container that inserts the DO
electrode.
The
aerification
under
the
measurement accomplishes the reaction by not
doing, and using only first stage DO ([Mizu]
and ..supply.. ..trial.. diluent are aerated
beforehand).
(a) One tank type (There is no DO supply).
ﾊﾟｿｺﾝ
DO計
- The device is easy (The respiratory rate
measurement unit of DO meter on the market
can be used as it is).
- The DO monitor is not interrupted.
To make all react in a narrow DO area of
×(Even if it is the maximum, it is saturation
DO level), the absolute value of OUR becomes
small. The performance of the DO electrode
and the influence of the stir are received
easily, and data varies easily.
Because it is difficult to expect best dilution
ratio
for
×
prior,
the
preliminary
measurement is needed.
The influence with × dilution water might be
received.
The collection of the sample for the measuring
water quality is impossible while × is
measured.
DO電極
ｽﾀｰﾗ
(b) One tank type (DO supply and exist).
ｴｱﾎﾟﾝﾌﾟ
DO計
DO電極
ｽﾀｰﾗ
制御
ﾕﾆｯﾄ
ﾊﾟｿｺﾝ
･ (a)The one that [ni] aeration function was
added. ON/OFF of the aerification is repeated
so that DO may enter the setting range. It is
convenient to turn the aerification on and off
by the automatic operation by the output from
the DO meter.
- It is not necessary to dilute the sample.
- It is possible to measure it for a long time.
The escape of the air at × is needed, and it is
difficult to make it to a complete encapsulated
type.
The data under aerating becomes invalid as for
×.
The device is needed to collect the sample for
the measuring water quality while measuring
it in × the device.
334
Appendix 3Experimental method for influent organic matter fractuation
(c) Two tank type
ｴｱﾎﾟﾝﾌﾟ
DO計
ﾊﾟｿｺﾝ
DO電極
送液ﾎﾟﾝﾌﾟ
ｽﾀｰﾗ
･ The chamber that measures OUR is
independently set up, and culture and the DO
measurement are done in another tank. The
supply water sample in the chamber is
replaced at regular intervals, and the DO
monitor for set time is done (Or, when DO
becomes below the definite value, the supply
tries and replaces [mizu]). In a respiratory rate
meter on the market, there are a lot of one of
this type.
- It is not necessary to dilute the sample.
- The measurement of a long term is possible.
- Because DO is measured in the chamber of the
small capacity, the influence of the stir
condition is not received easily.
- The sample for the measuring water quality
can be easily collected.
- It is possible to correspond also to the
substrate addition continuous running and
measuring it.
× device becomes complex.
Appendix 3Experimental method for influent organic matter fractuation
335
Table A3. 3Condition of OUR measurement for influent organism element segmentation
Conditio
n
Influent
1
-
2
Ion
Exchang
e water
-
3
-
4
-
Sending
back
Dirt
One ..ac
etate *..
ATU*2
Intention
-
-
Presumption of SS and XS.
-
-
Correction of amount
organism of dirt.
-
-
YH correction.
--3
-
Presumption of XH.
-
of
- 1 It is 20 as for the sodium acetate liquor About mgCOD/L (influent concentration conversion) addition.
- 2 It is 8 for the nitrification inhibition About mgATU/L is added.
- 3 Addition when SS of influent is low concentration.
Two influent with different SS density were targeted as an example of the measurement
result of condition 1-3.
336
Appendix 3Experimental method for influent organic matter fractuation
Table A3. 3[Ni] is shown. In sample ①, the section where OUR is high appears clearly
after beginning to measure condition 1, and it is clear that this originates in SS of
influent in condition 3 of adding the acetate because the duration is long. On the other
hand, it is a result that changing OUR is not so seen in condition 1 in sample ②. The
section that clearly seems that SS originating it appears in condition 3, and I
understand the main stream beginning of irrigation did not truly contain SS only from
this result because it becomes similar behavior when the mixing ratio of influent and
dirt is too large though unclearness whether SS exists really remains. Thus, condition 3
might provide not only the purpose to correct YH but also useful information to interpret
the behavior of condition 1.
Calculation of OUR
The decline (OUR) is calculated from the collecting DO data. Interval time of the
calculation comes to receive the influence of the noise of the measurement easily though
should reduce enough to changing OUR if it sets it small too much. In JS, the
above-mentioned uptake data is basically used and 1 Average OUR between min is
calculated.
Appendix 3Experimental method for influent organic matter fractuation
337
OUR [mg/(L·h)]
30
条件３（流入水＋酢酸＋返送汚泥）
条件１（流入水＋返送汚泥）
20
条件２（イオン交換水＋汚泥）
10
0
0
1
2
3
4
Time [h]
(a) Sample ①(case with high SS density of influent)
OUR [mg/(L·h)]
30
条件３（流入水＋酢酸＋返送汚泥）
20
条件１（流入水＋返送汚泥）
条件２（イオン交換水＋汚泥）
10
0
0
1
2
3
4
Time [h]
(b) Sample ②(case with low SS density of influent)
Figure A3. 2Example of OUR measurement result (condition 1-3)
Presumption of each element density
Each organism element density of influent is presumed from the above-mentioned
measuring water quality and the OUR measurement result.
・ SA: The organic acid measurement result of influent is considered to be SA.
・ (..the calculation of the oxygen consumption considered to be dependence on the consumption
of SS from the loess pyrogram of SS:OUR measurement condition 1... Figure A3.
1Reference) Expression
Expression (A3. 1)[Niyori] SS density is calculated.
However, the corresponding oxygen consumption is corrected from the one of
condition 1 to this by the balance Lycium chinense when judged that the
338
Appendix 3Experimental method for influent organic matter fractuation
organism that was able to be used in dirt remained from the OUR measurement
result of single dirt (condition 2). Moreover, when YH is corrected from the
measurement result of condition 3 adding the acetate of the known amount,
following equation is used.
Appendix 3Experimental method for influent organic matter fractuation
YH  1 
OU S S ,3  OU S S ,1
CODac
339
Expression (A3. 4)
OUSs and 1 ..here..: oxygen consumption mgO2 /L according to the SS
consumption in condition 1 and OUSs and 3: oxygen consumption mgO2 /L
according to the SS consumption in condition 3 and CODac: Dosage mgCOD/L of
the acetate and ..going out.. ..
・ SA is and is calculated from SF:SS subtracting.
S F ,in  S S ,in  S A,in
Expression (A3. 5)
・ SI: Dissolubility CODCr of the treated water is considered to be SI. However, when
dissolubility BOD5 and NO2-N of the amount that cannot be disregarded in the treated water
remain, it corrects it by following equation.
S I ,in  S  CODCr ,out  S  BOD 5 ,out  1.14  NO2 ,out 
Expression (A3. 6)
SI and in ..here..: SI density mgCOD/L of influent and S-CODCr and out:
dissolubility CODCr mgCOD/L of the treated water and S-BOD5 and out:
dissolubility BOD5 mgBOD/L of the treated water and NO2 and out: NO2-N
density mgN/L of the treated water and ..going out.. ..
・ XI:Another element is and is calculated from total CODCr of influent subtracting.
X I ,in  CODCr ,in  S A,in  S F ,in  S I ,in  X S ,in  X H ,in 
Expression (A3. 7)
・ To the loess pyrogram of XS:OUR measurement condition 1(Or, condition 3)
340
Appendix 3Experimental method for influent organic matter fractuation
Table A3. 4The shown simple organism removal model of [ni] is done in the fitting
and presumed. This model omitted the (a) fermentation process to the description
according to the organism removal of ASM2d, and gave autolysis of (b) XH, and
limiting factors (SO2 ,SNH4 ,SPO4 ,SALK ) other than the organism element
were given by "Concept of endoecism breath" in the description (Regeneration of
XS was not considered) and the (c) rate equation and all simplifications of
omission were given.
Appendix 3Experimental method for influent organic matter fractuation
341
Table A3. 4Structure of simple model used by presuming XS
Element
Process
Reaction rate equation
SO2
Hydrolysis
Proliferation of XH
-(1- YH )/ YH
Endoecism breath
-(1- fXI )
SS
XS
1
-1
XH
-1/ YH
Kh 
XS / XH
 XH
KX  XS / XH
1
H 
SS
 XH
K S  SS
-1
b XH
区間 ＩＩ
区間 Ｉ
区間 ＩＩＩ
OUR [mg/(L·h)]
30
調整②
20
調整③
実測値
10
デフォルト
調整①
0
0
1
2
3
4
Time [h]
Figure A3. 3Example of fitting for XS presumption
・ The example of the fitting processFigure A3. 3[Ni] result of showing is shown
below.
① The SS density separately presumed and XS and the XH density
arbitrarily set are given as initial condition, and the calculation using the
default value is done to all parameters ("Default" curve).
② To reproduce the inclination of the section (section II) where OUR
decreases, saturation factor KS according to SS is adjusted ("Adjustment
①" curve).
③ To reproduce the terminal in the OUR level and the same section of the
section (section I) considered to be progress of the consumption of SS of the
influent origin, the XH density and μH of measuring initial are adjusted
342
Appendix 3Experimental method for influent organic matter fractuation
("Adjustment ②" curve).
④ To reproduce the behavior after OUR decreases, an initial XS density is
adjusted ("Adjustment ③" curve). It executes it if necessary on two or more
conditions of changing kinetics constant (Kh ,KX ) according to hydrolysis.
⑤ ① -③ is repeated if necessary.
・ XH: As the XH density that can reproduce this, OUR(OUR0 ) when beginning to measure it
from the result of measurement condition ④ OUR is extrapolated, and the expression.
Expression (A3. 3)[Suru] of ..twining.. calculation (Figure A3. 4)｡
O U R [m g/l/h]
40
30
OUR0
20
10
0
0
1
Tim e [h]
2
3
Figure A3. 4Example of OUR measurement result for XH presumption
Bibliography
3A)
Hiroshi Itokawa [**], Toshikazu Hashimoto, and Hitoshi Nakazawa: Fundamental discussion
concerning method of influent organic constituent segmentation for activated sludge model, the
39th drainage research symposium lecture collection, and pp. 221-223,2002.
3B)
Hiroshi Itokawa [**], Toshikazu Hashimoto, and Takao Murakami: Measurement of
segmentation of organic constituent in drainage for activated sludge model, environmental
engineering study collection, Vol.40, and pp.41-52,2003.
3C)
[**] Mino: The 3rd influent segmentation, calibration, journal "EICA", Vol.8, No.1, and
pp.61-68,2003.
3D)
"Activated sludge model ASM1, ASM2, ASM2d, and ASM3" [**] Mino ([**yaku]):
Environmental newspaper and 2005.
3E)
Dircks,K., Pind,P.F., Mosbak,H. and Henze,M.: Yield determination by respirometry- The
possible influence of storage under aerobic conditions in activated sludge. Water SA, Vol.25,
Appendix 3Experimental method for influent organic matter fractuation
343
pp.69-74, 1999.
3F)
Guisasola,A., Sin,G., Baeza,J.A., Carrera,J. and Vanrolleghem,P.A.: Limitations of ASM1 and
ASM3: a comparison based on batch oxygen uptake rate profiles from different full-scale
wastewater treatment plants. Wat.Sci.Tech., Vol.52, No.10-11, pp.69-77, 2005.
3G)
Sperandio,M., Urbain,V., Ginestet,P., Audic,M.J. and Paul,E.: Application of COD
fractionation by a new combined technique: comparison of various wastewaters and sources of
variability. Wat.Sci.Tech., Vol.43, No.1, pp.181-190, 2001.
Appendix 4Investigation of actual conditions of influent organic matter characterization
345
Appendix４Investigation of actual conditions of influent organism
element
１．Outline
It is necessary to construct the process model based on the composition and the
parameter of the object facilities when the process simulation using ASM2d is done, and
to set the influent quantity, the water quality, the operating condition, and the water
temperature, etc. as input data. Especially, a peculiar index to ASM2d of which the unit
is CODCr is used for the organism element, and the water quality data that we are
using in the place of a usual business cannot be used directly though it is necessary to
give the density of each element defined by ASM2d about the influent quality. The
typical one is "as the method for the grasp of the organism element density in influent.
Error! Reference source not found.
Error! Reference source not found.A complex
experiment operation and the analysis of the result are needed compared with a usual
measuring water quality, and it is unsuitable for a daily monitor though it is the one
using the measurement of oxygen utilization speed (OUR) introduces by". Then, the
discussion about a simple estimation method using the measurement data that can
effectively use the water quality data daily collected in the sewage plant in our country,
and build it in usual water analyses situation of the waste treatment facility easily
becomes important. A lot of cases where the segmentation is actually operated on the
assumption of use with ASM2d are necessary for that for the influent of the sewage
plant in our country.
Various investigation of actual conditions is executed from such a background from 2001
to 15 years in JS, and it introduces a part of the result here.
・ A relativity investigation of CODCr and other carbon-estimating parameters:
Other carbon-estimating parameters (BOD5,CODMn) along with CODCr were
measured for influent in 90 places in the sewage plant, and these correlations
were investigated.
・ The investigation of actual conditions of the organism element composition: It
investigated whether the segmentation of the organism element of 18 kinds of
influent samples that collected in 16 places in the sewage plant mainly composed
of the OUR measurement was operated, and how the organism element
346
Appendix 4Investigation of actual conditions of influent organic matter characterization
composition of influent was the different between the treatments.
・ The time varying situation investigation of the organism element composition:
The change situation of the influent organism element composition of three kinds
of Thymus vulgaris span (time varying, change on a day of the week, and secular
fluctuation) was investigated for four places in the sewage plant.
Appendix 4Investigation of actual conditions of influent organic matter characterization
347
２．Relativity investigation of CODCr and other carbon-estimating parameters
（１）Purpose
CODCr is the only carbon-estimating parameter used with ASM2d. On the other hand,
you may say that it is none at all that this index is used in the place of the business
according to the design and driving the sewage plant because this index is not adopted
as an item of the water quality regulation according to the organism in our country. If
you can presume CODCr by using these because BOD5 and CODMn of influent are
regularly measured on the other hand in all sewage plantsError! Reference source not found.､One
load when the activated sludge model is used will be reduced. Moreover, the method of
difficult the collection of the data of the influent quality at the stage of the facilities
design actually, and appropriately presuming CODCr from the design water quality of
BOD5 and CODMn may be called an indispensable matter to use the activated sludge
model then.
Investigation of actual conditions aiming to find the method of presuming CODCr from
the carbon-estimating parameter such as BOD5 and CODMn usually used from such a
viewpoint intended for the influent of a lot of sewage plants.
（２）Search procedure
Object facilities
90 places of the Japanese whole country in the sewage plant were targeted. So as not to
cause the deviation in the geographical distribution, the exclusion scheme, and the
facilities scale, etc. , the object facilities were selected. The distribution of the facilities
scaleFigure A4. 1It is as showing [ni]. Moreover, facilities of the separate system occupy
71 places and many, and, additionally, the confluence type and the complete confluence
type where the separate system including the confluence district contains the branch
district by 11 places are 4 places in the exclusion scheme respectively.
Method of sampling
[Mizu] of the multiple (2-17 times) ..adoption.. was done in each facilities, and various
carbon-estimating parameters were measured. As a rule, the collection part of the
sample was made reactive tank influent. The method of collecting the sample is a spot
hydrocast in the greater part of facilities in a part of facilities in the composite sample or
the morning and the afternoon of 24h though it is an equiponderance mixture of each
spot hydrocast that collects once. 1-3 is required while refrigerated from the sampling to
348
Appendix 4Investigation of actual conditions of influent organic matter characterization
the measuring water quality because sending out the obtaining water and the sample
was requested from the person in charge of the maintenance in the greater part of
facilities.
Appendix 4Investigation of actual conditions of influent organic matter characterization
349
50
40
30
Number of parts
20
10
0
～5,000
5,000～
10,000
10,000～
50,000
50,000～
100,000
100,000～
3
Existing..facilities..ability.
/d]
Figure A4. 1Distribution of existing process capability of object facilities of relation investigation of
CODCr and other carbon-estimating parameters
（３）Finding
Difference between facilities
The relation between BOD5 of influent and CODMn and CODCrFigure A4. 2､Figure A4.
3The cumulative frequency distribution of [ni], the CODCr /BOD5 ratio, and the CODCr
/CODMn ratioFigure A4. 4[Ni] was shown. Moreover, amount of statistics according to
the CODCr /BOD5 ratio and the CODCr /CODMn ratioTable A4. 1[Ni] was brought
together.
・ An excellent correlation is seen between CODCr and BOD5･CODMn of influent.
Each average, and the CODCr /BOD5 ratio and the CODCr /CODMn ratio are 2.1,
and 3.6.
・ It doesn't depend on the distinction between the facilities scale and the crude
sewage and [go] ..sinking.. water and an excellent linear relationship is seen
between CODCr and CODMn especially.
・ The crude sewage is CODCr compared with [shizugomizu] in case of BOD5/ It
tends ..large the BOD5 ratio... This seems by the crude sewage it is because
350
Appendix 4Investigation of actual conditions of influent organic matter characterization
comparatively a lot of small organisms at the use speed by the living being are
contained.
Appendix 4Investigation of actual conditions of influent organic matter characterization
800
Crude sewage
[Shizugomizu]
600
[mg/l]
Linear regression
Cr 400
COD
y = 1.9755x
R2 = 0.8222
200
0
0
100
200
300
400
BOD [mg/l]
5
Figure A4. 2Relation between BOD5 and CODCr of influent
800
Crude sewage
[Shizugomizu]
600
[mg/l]
Linear regression
y = 3.6901x
R2 = 0.9546
COD400
Cr
200
0
0
50
100
COD
Mn
150
200
[mg/l]
Figure A4. 3Relation between CODMn and CODCr of influent
351
352
Appendix 4Investigation of actual conditions of influent organic matter characterization
100
80
Cumulative
60
frequency %
40
20
CODCr/BOD
CODCr/CODMn
0
0
1
2
3
4
5
6
ratio [-]
Figure A4. 4Cumulative frequency distribution of CODCr /BOD5 ratio and CODCr /CODMn ratio
of influent
Table A4. 1Amount of statistics according to CODCr /BOD5 ratio and CODCr /CODMn ratio of
influent
Standard Coefficient
Number
Average
Maximum
Minimum
deviation
of
of data
[-]
[-]
[-]
[-]
variation
[%]
CODCr
/BOD5
90
2.05
3.02
1.29
0.34
16.4
ratio
CODCr /CODMn
90
3.63
4.77
2.67
0.34
9.4
ratio
Appendix 4Investigation of actual conditions of influent organic matter characterization
353
Change in the same facilities
The cumulative frequency distribution of the CODCr /BOD5 ratio and the CODCr
/CODMn ratio of 65 facilities where the obtaining water of one time was done at each
season every seasonFigure A4. 5､Figure A4. 6[Ni] was shown.
・ As for the CODCr /BOD5 ratio, a strong tendency is seen a little in the data of
summer (July-August). Moreover, the small tendency is seen a little in the data in
winter (January-February) as for the CODCr /CODMn ratio. However, neither is
the one that is bigger than the difference between waste treatment facilities.
100
80
Cumulative
60
January-February
40
April-May
July-August
20
October-November
0
0
1
2
3
4
5
6
COD Cr/BOD 5 Ratio-
Figure A4. 5Cumulative frequency distribution of ratio of every season of influent CODCr /BOD5
354
Appendix 4Investigation of actual conditions of influent organic matter characterization
100
January-February
80
April-May
July-August
Cumulative
frequency
%
60
October-November
40
20
0
0
1
2
3
4
5
6
COD Cr/COD Mn Ratio-
Figure A4. 6Cumulative frequency distribution of ratio of every season of influent CODCr
/CODMn
Appendix 4Investigation of actual conditions of influent organic matter characterization
355
Moreover, eight facilities ..adoption.. (..there were comparatively a lot of [mizu]
frequencies... Table A4. 2Amount of statistics of) according to the CODCr /BOD5 ratio
and the CODCr /CODMn ratio of each facilities
356
Appendix 4Investigation of actual conditions of influent organic matter characterization
Table A4. 3､Table A4. 4[Ni] was brought together.
・ The average level of the size of the change of the CODCr /BOD5 ratio and the
CODCr /CODMn ratio in the same facilities between the above-mentioned
facilities as long as it compares it by the coefficient of variation is equal to the
difference.
・ The CODCr /CODMn ratio tends as well as the difference between facilities and
the coefficient of variation tends to the small.
・ The clear trend is not seen in the difference of the coefficient of variation value of
the crude sewage and [shizugomizu] in three facilities (a,b,e) where both the
crude sewage and [shizugomizu] were measured in the same facilities (The
coefficient of variation of the crude sewage is not necessarily larger).
Table A4. 2Outline of change research objective facilities of CODCr /BOD5 ratio and CODCr
/CODMn ratio of influent
Existing process
Facilities
Exclusion scheme
Investigation period
capability *
name
[m3/d]
Branch
a
790,000
2002.8～2004.3
(confluence of
b
160,000
Branch
2000.7～2001.3
part)
-
c
99,000
Branch
2000.7～2001.3
d
28,000
2003.5～2004.3
e
26,000
Branch
(confluence of
part)
Branch
f
2,800
Branch
2000.7～2001.3
g
2,600
Branch
2003.5～2004.3
h
1,600
Branch
2000.6～2001.3
Official process capability when investigation is executed.
2002.8～2004.3
Appendix 4Investigation of actual conditions of influent organic matter characterization
357
Table A4. 3Change situation of CODCr /BOD5 ratio of influent
Facilitie
s name
a
b
c
d
e
f
g
h
Influent
Type
Data
Number
Average
[-]
Maximum
[-]
Minimum
[-]
Standard
deviation
[-]
Coefficient
of variation
Crude
sewage
[Shizugo
mizu]
Crude
sewage
[Shizugo
mizu]
Crude
sewage
[Shizugo
mizu]
Crude
sewage
[Shizugo
mizu]
Crude
sewage
Crude
sewage
Crude
sewage
13
2.34
4.03
0.96
0.760
32.5%
16
2.42
3.57
0.98
0.614
25.4%
9
2.12
2.50
1.63
0.271
12.8%
7
1.91
2.20
1.58
0.194
10.1%
9
2.27
3.12
1.49
0.532
23.5%
11
2.69
4.20
2.17
0.619
23.0%
12
2.40
3.16
0.96
0.577
24.1%
14
2.38
3.44
0.99
0.567
23.9%
12
1.85
2.46
1.33
0.317
17.2%
11
3.46
4.36
3.00
0.526
15.2%
16
2.14
2.81
1.54
0.319
14.9%
Table A4. 4Change situation of CODCr /CODMn ratio of influent
Facilitie
s name
a
b
c
d
e
f
g
h
Influent
Type
Data
Number
Average
[-]
Maximum
[-]
Minimum
[-]
Standard
deviation
[-]
Coefficient
of variation
Crude
sewage
[Shizugo
mizu]
Crude
sewage
[Shizugo
mizu]
Crude
sewage
[Shizugo
mizu]
Crude
sewage
[Shizugo
mizu]
Crude
sewage
Crude
sewage
Crude
sewage
15
3.36
3.88
1.68
0.494
14.7%
17
3.43
3.85
2.74
0.274
8.0%
9
3.58
4.04
3.33
0.217
6.1%
8
3.03
3.52
1.58
0.599
19.8%
9
3.85
4.56
3.26
0.364
9.5%
10
3.21
3.94
2.47
0.429
13.4%
12
3.56
3.96
2.98
0.267
7.5%
12
3.45
3.90
3.10
0.211
6.1%
13
2.69
3.51
1.89
0.415
15.4%
11
3.22
3.66
2.75
0.285
8.8%
16
3.54
5.35
2.64
0.661
18.7%
358
Appendix 4Investigation of actual conditions of influent organic matter characterization
３．Investigation of actual conditions of organism element compositionError!
Reference source not found.
（１）Purpose
The segmentation operation based on the OUR measurement was presumed for the
influent of 16 facilities with a different condition of presence in the facilities scale and
the primary sedimentation tank etc. aiming to understand what organism element
composition the influent of the sewage plant in our country had, and very it varied
between the treatments and the doing each organism element density was presumed.
Additionally, it is a method of STOWA as the representative of a simple estimation
method. 4S)The water quality data to do the [niyoru] segmentation collected also, too and
the comparison of the estimated results was attempted.
（２）Search procedure
Object facilities
(intended for 16 places of the Kanto region in the sewage plant. Table A4. 5)｡When
facilities were selected, it was noted that facilities of various scales were covered. The
range of an existing process capability of the object facilities (The investigation is
executed) is 1,000-910,000 It is m3/d.
Table A4. 5Outline of facilities of organism element composition of influent for investigation of
actual conditions
Name
Existing process
capability
[m3/d]
Exclusion scheme
Processing mode
The first
precipitation
ground
A
1,400
Branch
OD method
It is not.
B
1,000
Branch
OD method
It is not.
C
1,750
Branch
OD method
It is not.
D
3,150
Branch
OD method
It is not.
E
8,250
Branch
F
26,060
Branch
G
21,200
Branch
H
29,200
Branch (confluence of
part)
I
39,400
Branch
J
39,600
K
78,000
Branch (confluence of
part)
Confluence (part of
sub-culture)
Standard
procedure
Standard
procedure
Standard
procedure
Standard
procedure
Standard
procedure
Standard
procedure
Standard
procedure
It is.
It is.
It is.
It is.
It is.
It is.
It is.
Appendix 4Investigation of actual conditions of influent organic matter characterization
L
195,000
Branch (confluence of
part)
M
520,000
Branch
N
705,000
Confluence
O
790,000
Branch (confluence of
part)
P
910,000
Confluence
Standard
procedure
Standard
procedure/A2O
Standard
method
procedure
Standard
procedure
Standard
procedure
It is.
It is.
It is.
It is.
It is.
359
360
Appendix 4Investigation of actual conditions of influent organic matter characterization
Sampling and measuring method
Two investigations in winter (January-March) of 2003 and spring (May-June) were
executed. Influent, the treated water (final sedimentation pond overflow), and the
sending back dirt were collected in each facilities, and it supplied it to the experiment
operation and the measuring water quality for the presumption of organism element
(SA ,SF ,SI ,XS ,XI ,XH ) density in influent. The sedimentation pond overflow
([go] ..sinking.. water) was collected first in the treatment influent (crude sewage) and
other facilities in facilities A-D that did not have the primary sedimentation tank for the
reactive tank influent as influent it. However, both the crude sewage and [shizugomizu]
facilities F and facilities O were collected. The sample is all the spot hydrocasts that
collected at 8:30-9:00.
The segmentation of the organism element is "Error! Reference source not found.
Error! Reference source not found.Though it executed by an experimental estimation
method using the OUR measurement introduces by" because XS obviously becomes
undervaluation (XI overvalues it as a result) when the crude sewage is targeted in this
methodError! Reference source not found.､The estimated result using ultimate BOD based on the
method of STOWA was adopted about the life drainage sample.
（３）Finding
Estimated result of each element
Each element density of influent in two investigationsFigure A4. 7[Ni] and the element
composition rate (abundance ratio that occupies it to total CODCr)Figure A4. 8[Ni] was
shown. Moreover,Table A4. 1Basic amount of statistics concerning the abundance ratio
of [niha] and each element was arranged to the crude sewage and [shizugomizuwaka].
・ Total CODCr varies between facilities, and the coefficient of variation is 20% in
the crude sewage, and 28% in [shizugomizu]. The proportion of total CODCr that
dissolubility CODCr(S-CODCr) it occupies is 0.45 μm or 0.10 (..39.0% of 26.0 on
the average respectively it in the crude sewage when the membrane filter of μm is
used 20.7%, and [go] water and ..sinking.. 46.5 on the average respectively it it...
Figure A4. 9)｡
・ Crude
sewage..average..average..presume..abundance
ratio..respectively..average..small.Figure A4. 10)｡The difference such as there is the
one to exceed 10% by the abundance ratio, too is large while the density and the
abundance ratio are greatly different according to facilities, and there is a sample
Appendix 4Investigation of actual conditions of influent organic matter characterization
361
not detected at all. ..SA of elements of two that composes SS.. (in the
subject ..between this element and SS.. ..seeing a strong positive correlation
relation... Figure A4. 11)｡On the other hand, (..between S-CODCr, BOD5, and
SS.. ..not seeing clear relation..Figure A4. 12､Figure A4. 13)｡(..showing a high SS
density.. ..inside-large scale.. ..between the length of the pipe and drain and the
SS density.. ..not seeing the clear relation.. though it is often facilities, and the
time of flow of drainage in the pipe and drain is suggesting the possibility of
influencing it in the SS density. Figure A4. 14)｡It seems that it is necessary to
temper with the factor affecting after the drainage of the contribution of the
operation condition and the sludge disposal return-into water in pump [**] and
the primary sedimentation tank etc. flows in the waste treatment facility.
800
700
600
XI
500
[mgCOD/L]
XH
Xs
400
Ss
300
SI
200
100
0
A
B
C
D
E
F
F'
G
H
I
J
K
Treatment name
(a) Investigation in winter
L
M
N
O
O'
P
362
Appendix 4Investigation of actual conditions of influent organic matter characterization
800
700
600
XI
500
[mgCOD/L]
XH
Xs
400
Ss
300
SI
200
100
0
A
B
C
D
E
F
F'
G
H
I
J
K
L
M
N
O
O'
P
Treatment name
(b) Investigation in spring
Figure A4. 7Influent organism element segmentation result of each waste treatment facility
(element density)(A round bawn of the facilities name shows that the crude sewage was
targeted).
Appendix 4Investigation of actual conditions of influent organic matter characterization
363
100%
80%
[%]
XI
) Cr
T-CO
60%
XH
Xs
Ss
40%
..abundance ratio (.. pair
SI
20%
0%
A
B
C
D
E
F
F'
G
H
I
J
K
L
M
N
O
O'
P
Treatment name
(a) Investigation in winter
100%
80%
[%]
XI
) Cr
T-COD
60%
XH
Xs
Ss
40%
..abundance ratio (.. pair
SI
20%
0%
A
B
C
D
E
F
F'
G
H
I
J
K
Treatment name
(b) Investigation in spring
L
M
N
O
O'
P
364
Appendix 4Investigation of actual conditions of influent organic matter characterization
Figure A4. 8Influent organism element segmentation result of each waste treatment facility
(element composition ratio)(A round bawn of the facilities name shows that the crude sewage
was targeted).
Appendix 4Investigation of actual conditions of influent organic matter characterization
365
Table A4. 6Amount of statistics concerning CODCr and the element composition ratio
Crude sewage (n=10)
Average
[-]
Index/element
Maximum
[-]
Minimum
[-]
Standard
deviation
[-]
Coefficient
of variation
Water
quality
parameter
[mg/L]
492
705
343
97.3
20%
S-CODCr, 0.45*1[mg/L]
128
163
102
23.7
18%
S-CODCr, 0.10*2[mg/L]
102
140
81
21.5
21%
T-CODCr
Element abundance ratio of
ASM2d
SF
[-]
0.5
2.5
0.0
0.8
151%
SA
[-]
2.1
7.5
0.0
2.5
121%
SI
[-]
3.0
4.5
1.3
1.1
37%
XI
[-]
15.3
48.2
0.2
14.7
96%
XS
[-]
70.4
89.8
45.2
12.4
18%
XH
[-]
8.9
14.4
5.3
2.9
32%
[Shizugomizu] (n=24)
Average
[-]
Index/element
Maximum
[-]
Minimum
[-]
Standard
deviation
[-]
Coefficient
of variation
Water
quality
parameter
[mg/L]
186
264
74
52.1
28%
S-CODCr, 0.45*1[mg/L]
86.4
160
37.0
30.4
35%
S-CODCr, 0.10*2[mg/L]
72.6
133
29.0
26.6
37%
T-CODCr
Element abundance ratio of
ASM2d
SF
[-]
0.8
4.4
0.0
1.2
150%
SA
[-]
6.3
23.6
0.0
5.2
83%
SI
[-]
9.8
16.3
5.4
2.7
28%
XI
[-]
14.0
31.6
2.2
8.8
63%
XS
[-]
55.2
73.7
37.7
9.8
18%
XH
[-]
14.4
23.1
7.2
4.8
33%
- 1 Pore size 0.45 The filtrate with the membrane filter of μm is measured.
- 2 Pore size 0.10 The filtrate with the membrane filter of μm is measured.
Appendix 4Investigation of actual conditions of influent organic matter characterization
200
150
Cr 100
S-CO
50
S-CODCr(0.45μm)
S-CODCr(0.10μm)
0
0
200
400
T-COD
Cr
600
800
[mg/L]
(a) Crude sewage
200
S-CODCr(0.45μm)
S-CODCr(0.10μm)
150
S-CODCr [mg/L]
366
100
50
0
0
100
200
300
T-CODCr [mg/L]
(b) [Shizugomizu]
Figure A4. 9Relation between total CODCr and dissolubility CODCr of influent it
Appendix 4Investigation of actual conditions of influent organic matter characterization
70
60
Crude sewage
[Shizugomizu]
50
[mg/L]
40
SS
30
20
10
0
0
200
400
COD
Cr
600
800
[mg/L]
Figure A4. 10Relation between CODCr and total SS of influent
80
Crude sewage
60
[Shizugomizu]
[mg/L]
40
SS
20
0
0
20
40
60
80
VFA [mgCOD/L]
Figure A4. 11Relation between organic acid (SA ) of influent and SS
367
368
Appendix 4Investigation of actual conditions of influent organic matter characterization
70
60
Crude sewage
50
[Shizugomizu]
40
SS
30
20
10
0
0
50
100
S-COD
Cr,0.45
150
200
[mg/L]
Figure A4. 12Relation between S-CODCr and SS of influent
70
Crude sewage
60
[Shizugomizu]
50
[mg/L]
40
SS
30
20
10
0
0
100
200
300
BOD [mg/L]
5
Figure A4. 13Relation between BOD5 and SS of influent
Appendix 4Investigation of actual conditions of influent organic matter characterization
70
60
Crude sewage
[Shizugomizu]
50
[mg/L]
40
SS
30
20
10
0
10
100
1000
10000
[km]of soil pipe
The total extension
Figure A4. 14Pipe and drain (soil pipe) extension and relation of SS of influent
30
Crude sewage
25
[Shizugomizu]
20
[mg/L]
15
SI
10
5
0
0
50
100
S-COD
Cr,0.45
150
200
[mg/L]
Figure A4. 15Relation between S-CODCr of influent and 0.45 and SI
369
370
Appendix 4Investigation of actual conditions of influent organic matter characterization
・ SI is 8-19 in the crude sewage It is mg/L (14.0 on the average mg/L), [shizugomizu], and
10-27 It is presumed mg/L (17.4 on the average mg/L), and the abundance ratio is 3.0, and
9.8% on each average. SI suggests in small-scale facilities and the inflow load of the
processing process suggests that estimate value of this element become small in the small (SRT
is long) in the small point comparatively though S-CODCr of the treated water is considered
to be SI in the fractionation procedure used. (..S-CODCr of influent in the [shizugo] water
sample.. ..little seeing relativity between the two of the life drainage sample.. though SI by
high tends to rise. Figure A4. 15)｡The coefficient of variation of the abundance ratio
to total CODCr is a level that is 37 and 28% respectively a little higher than the
coefficient of variation of total CODCr as for the crude sewage and [shizugomizu].
・ XI is 1-280 in the crude sewage It is mg/L (80.7 on the average mg/L), [shizugomizu], and
3-81 It is presumed mg/L (28.3 on the average mg/L), and the abundance ratio is 15.3, and
14.0% on each average. Both do not have a big difference in the abundance ratio though the
crude sewage is higher when seeing in the density. Note the point where presumption is
extremely uncertain only because this element is indirectly calculated as a balance with the
sum total of total CODCr and other element densities in the fractionation procedure used. It is
usual in an actual simulation to adjust this element at the stage of the calibration on the basis
of the amount etc. of the excess sludge solid as described in "Business use of the Chapter 4
activated sludge model".
・ XS was 263-511 in the crude sewage It was mg/L (343 on the average mg/L), [shizugomizu],
and 45-149 It was presumed mg/L (100 on the average mg/L). It is the maximum constituent in
all samples, and the abundance ratio reaches 70.4 and 55.2% on each average. It can be said
that both levels of the difference between facilities are the same levels in both as total CODCr
in the coefficient of variation of the abundance ratio by 18%. Because the positive correlation
is seen between total CODCr though it varies widely, (Figure A4. 16It is possible to
presume from total CODCr simply by using an average abundance ratio if it
thinks this element density along with above-mentioned XI is adjusted in the
calibration.
Similar..presumption..floatage..subtract..one..suspended
solid..do..think..main
enumeration..result..range..index..with..presumption..reliability..improve.Figure
A4. 17､Figure A4. 18)｡
・ XH was 31-60 in the crude sewage It was mg/L (42 on the average mg/L), [shizugomizu], and
11-45 It was presumed mg/L (26 on the average mg/L). ..magnitude correlation of 8.9, each
14.4% on the average, and both.. (..the reversal.. when seeing by the abundance ratio though
the crude sewage where the concentration of suspended solids is high is obviously more high
density. Figure A4. 19) ｡ The rejection ratio of the biomass in the primary
Appendix 4Investigation of actual conditions of influent organic matter characterization
371
sedimentation tank suggests the small from other organisms in this. The
coefficient of variation of the abundance ratio of XH is a level that is 32 and 33%
respectively a little larger than total CODCr as for the crude sewage and
[shizugomizu].
372
Appendix 4Investigation of actual conditions of influent organic matter characterization
600
Crude sewage
500
[Shizugomizu]
400
[mg/L]
300
XS
200
100
0
0
200
400
T-COD
Cr
600
800
[mg/L]
Figure A4. 16Relation between CODCr and total XS of influent
600
Crude sewage
500
[Shizugomizu]
400
[mg/L]
300
XS
200
100
0
0
200
400
600
P-COD [mg/L]
Cr
Figure A4. 17Relation between floatage CODCr (P-CODCr ) of influent and XS
Appendix 4Investigation of actual conditions of influent organic matter characterization
600
Crude sewage
500
[Shizugomizu]
400
[mg/L]
300
XS
200
100
0
0
200
400
600
SS [mgSS/L]
Figure A4. 18Relation between suspended solid (SS) of influent and XS
70
60
Crude sewage
[Shizugomizu]
50
[mg/L]
40
H
X
30
20
10
0
0
200
400
T-COD
Cr
600
800
[mg/L]
Figure A4. 19Relation between CODCr and total XH of influent
373
374
Appendix 4Investigation of actual conditions of influent organic matter characterization
Comparison with estimated result by method of STOWA
The biggest difference with the method of the fractionation procedure and STOWA used
by the main enumeration is in the estimation method of two elements (SS and XS) used
by the living being. Then, the one that the result of presuming both elements by the
method of STOWA was compared with the above-mentioned estimated result :. Figure
A4. 20､Figure A4. 21....going out.. (.. however, the estimation method of XS when the
crude sewage is targeted is not shown because it is identical. )
・ Method..estimate
value..crude
clause..show..measurement..based
sewage..average..average..preceding
on..estimated
result..compared
with..obviously..high.Figure A4. 20)｡Both of estimate values of SF will differ
greatly in the law because the both hands method uses both measurements of
organic acid for SA.
・ The estimated result of XS by the method of STOWA is 39-161 Mg/L (94.2 on the
average mg/L), and it is a method based on the OUR measurement and roughly
an estimated result of this level. However, it can be said that small estimate value
will be given a little , considering the amount so that estimate value of XS by the
former may contain the majority of the amount of the organism of the living being
in influent (Correspond to XH).
・ About the influence that the difference of the estimated result by the both hands
method gives the simulation result, "Error! Reference source not found. Error!
Reference source not found.Refer because it introduces the discussion case to".
Appendix 4Investigation of actual conditions of influent organic matter characterization
140
120
[mg/L]
100
Law) 80
STOWA
60
(
SS
40
Crude sewage
20
[Shizugomizu]
0
0
10
20
30
40
50
60
70
S ( OURLaw)[mg/L]
S
Figure A4. 20Comparison of SS estimate values by OU R method and STOWA method
375
376
Appendix 4Investigation of actual conditions of influent organic matter characterization
200
[mg/L]150
Law)
STOWA
100
(
XS
50
[Shizugomizu]
0
0
50
100
150
200
X ( OURLaw)[mg/L]
S
Figure A4. 21Comparison of XS estimate values by OU R method and STOWA method
Comparison with other segmentation cases
The one that the estimated result that had been obtained by the main enumeration was
compared with a domestic and foreign report case
Appendix 4Investigation of actual conditions of influent organic matter characterization
377
Table A4. 7..going out.. .
・ The segmentation result is greatly different according to the report case.
・ There is the estimated result of the main enumeration by SS comparing other
segmentation cases based on the OUR measurement and are not some big
differences on average excluding the small point.
・ SS is comparatively high in the estimated result based on the method of STOWA
as well as this finding.
378
Appendix 4Investigation of actual conditions of influent organic matter characterization
Table A4. 7Comparison of influent organism element segmentation results
Crude sewage
Pres
umpt
ion
Meth
od
OU
Doc
ume
nt
9～13
(12)
OU
4G)
Abundance ratio % *1
Object
T-CODCr
[mg/L]
SF
SA
SI
XI
XS
XH
One
..main
enumeration *..
343～705
(492)
0～3
(1)
0～8
(2)
1～5
(3)
0～48
(15)
45～90
(70)
5～14
(9)
Sewage plant
(Japan; three places)
256～430
(338)
1～4
(2)
0～8
(3)
3～6
(4)
2～3
(3)
73～76
(75)
Two ..sewage plant
*..
(Turkey; one place)
Sewage plant
(France;
seven
places)
Sewage plant
(Netherlands;
21
places)
590
9
-- 3
11
80
-
OU
484～965
(663)
0～13
(5)
-- 3
30～63
(47)
24～49
(38)
6～17
(10)
OU
241～827
(551)
11～42
(27)
4～10
(6)
23～50
(39)
10～47
(28)
-
ST
Pres
umpt
ion
Meth
od
OU
Err
or!
Ref
ere
nce
4K)
sou
rce
not
4S)
fou
nd.
[Shizugomizu]
Main enumeration
ASM2d technical
Report
(illustration
value)
Sewage plant
(Japan; six samples a
place)
[mg/L]
SF
SA
SI
XI
XS
XH
74～264
(186)
0～4
(1)
0～24
(6)
5～16
(10)
2～32
(14)
38～74
(55)
7～23
(14)
260
12
8
12
10
48
12
ｰ
150～210
2～5
0～15
40～65
6～10
OU
133/186
5/4
0/2
9/4
20/16
54/72
11/3
OU
7～13
(10)
9～24
(14)
5～11
(8)
14～40
(27)
23～34
(29)
9～18
(12)
OU
9～35
(23)
9～24
(14)
5～10
(8)
13～19
(17)
24～52
(39)
-
ST
4K)
one
320
14
9
8
47
22
OU
three
250～430
(335)
7～11
(9)
10～20
(14)
8～10
(9)
53～60
(56)
7～15
(12)
OU
4O)
37
6
10
45
-
OU
39
6
24
31
-
ST
Sewage plant
(Japan; five samples
a place)
Sewage plant
(Netherlands;
place)
one
120～190
(156)
579
4O)
- 1 The upper row: In the range and the the lower parentheses: The mean.
- 2 Mean of 18 samples.
- 3 It is included in XI.
16～32
Doc
ume
nt
Err
or!
Ref
ere
nce
sou
4G)
rce
not
fou
nd.
4T)
Sewage plant
(Japan; one place)
Sewage plant
(Switzerland;
place)
Sewage plant
(Switzerland;
places)
Abundance ratio % *1
T-CODCr
Object
Appendix 4Investigation of actual conditions of influent organic matter characterization
379
４．Change situation investigation of organism element compositionError! Reference
source not found.
（１）Purpose
In the discussion about propodus, it was clarified that the organism element composition
of influent was greatly different according to the sewage plant. On the other hand, it is
useful to discuss whether to only have to collect the influent quality data used by the
simulation at the frequency of which extent and to set it to understand how the
organism element composition changes timewise when the same facilities are seen.
When the simulation intended for a season different from the data especially used by
the calibration and the simulation of a long term that exceeds one month are done, this
becomes an important viewpoint. 4K)｡
The influent organism element was measured from such an aspect with three different
Thymus vulgaris span (change in the time varying and the week and secular
fluctuation) in a real sewage plant, and the change situation was investigated.
（２）Search procedure
Object facilities
Four places in the sewage plant where the process capability was different were
targeted (① ..facilities.. -④). The outline of each facilitiesTable A4. 8It is as [ni] is
brought together.
Table A4. 8Outline of change situation research objective facilities of influent organism element
Existing
process
capability
Exclusion
scheme
One ..process
ing mode *..
Primary
sedimentation
Twotank
..sludge
disposal *..
[Mizu] method
*3 of adoption
Facilities ①
Facilities ②
Facilities ③
Facilities ④
2,600 m3/d
26,000 m3/d
28,000 m3/d
790,000 m3/d
Branch
Branch
Branch
(confluence of
part)
OD method
A2O method
It is not.
Standard
procedure
It is.
It is.
Branch
(confluence of
part)
Standard
procedure
It is.
Thickness
and escape
C
Thickness
and escape
of ..erasing..
S
Thickness
and escape
C
Thickness,
escape, and
scorch
S
- 1 OD method: Oxidation ditch method and standard procedure: Conventional activated sludge process and A2O method: Conceited
reluctance-hypoxia-method.
- 2 Thickness: Concentrate and/anaerobic digestion and ..erasing.. escape: Dehydration and scorch: Incineration.
- 3 [Mizu] method of adoption in main enumeration. C: Composite (24h) and S: Spot (nine o'clock).
380
Appendix 4Investigation of actual conditions of influent organic matter characterization
Appendix 4Investigation of actual conditions of influent organic matter characterization
381
Sampling and measuring method
Facilities..moon..frequency..obtaining
water..do..reaction..tank..influent..facilities..screen..pass..crude
sewage..facilities..primary
sedimentation
tank..overflow..as
follows..only..influent..record..for..organism..element..density..understand..segmentation
..operate..secular fluctuation..investigation.The expert day investigation was executed in
winter in -③, and ..adoption.. influent that did [mizu] was supplied to the segmentation
operation similarly at the regular time (4h interval level) in a day ..facilities ①
moreover.. (time varying investigation). In addition, the investigation that examined the
influent quality change of one week at the time varying investigation and the
simulataneous period was done in facilities ③(change investigation in the week), too. It
is a secular fluctuation, a change investigation in the week, and the influent used is a
spot ..adoption.. sample in composite sample of 24h that uses autosampler and facilities
② and ④ ..facilities ① and ③.. in about 9:00 that does [mizu] in the morning.
The segmentation operation executed by each investigation is a method mainly
composed of the OUR measurement similar to propodus. However, only facilities ①
intended for the crude sewage used ultimate BOD and XS was presumed.
（３）Finding
Time varying situation
The time varying situation of the influent organism element density in facilities ① -③
Figure A4. 22[Ni] was shown. Moreover, amounts of statistics of the mean, the standard
deviation, and the coefficient of variation, etc. of the abundance ratio of each element to
total CODCr
382
Appendix 4Investigation of actual conditions of influent organic matter characterization
Table A4. 9[Ni] was brought together.
・ The tendency to which total CODCr decreases from midnight to the morning is
seen in each facilities. It is similar to a general water quality variability pattern
of drainage mainly composed of the domestic wastewater.
・ Each organism element changes timewise about not only the density but also the
abundance ratio. Especially, the change of SF and SA is large. For instance, it is
30 in facilities ③ where both element density was comparatively high in the
sample at 13 o'clock and 21 o'clock by gross weight (SS ) of both It is hardly
detected in the sample at 1 o'clock and five o'clock of midnight while mg/L is
exceeded.
・ The point that the density of SA of [shizugomizu] changes timewise and greatly is
a case where the continuous monitoring with the automatic measurement device
of organic acid is executed.
Error! Reference source not found. However,
it is shown. It is
considered that the deposit's to which it rotted when the relation is seen in the
variability pattern of the pump discharge and the organic acid concentration, and
the pump discharge is increased and the flow rate in the incurrent canal rises in
the pipe and drain flowing in contributes there.
Appendix 4Investigation of actual conditions of influent organic matter characterization
350
300
XI
250
mg/L]
200
[
XH
Xs
150
Ss
100
SI
50
0
9:00
13:00
17:00
21:00
1:00
5:00
9:00
Time
(a) Facilities ① (H15.12.9-10)
300
250
XI
200
mg/L]
[
XH
150
Xs
Ss
100
SI
50
0
10:00
12:00 14:00
18:00
22:00
2:00
6:00
Time
(b) Facilities ② (H16.3.2-3)
300
250
XI
200
mg/L]
XH
150
Xs
[
Ss
100
SI
50
0
9:00
13:00 17:00
21:00
1:00
5:00
9:00
Time
(c) Facilities ③ (H16.1.28-29)
Figure A4. 22Time varying of influent organism element density
383
384
Appendix 4Investigation of actual conditions of influent organic matter characterization
Table A4. 9Amount of statistics concerning organism element abundance ratio in time varying
investigation
Facilitie
s ①
Facilitie
s ②
Facilitie
s ③
Abundance ratio %
T-CODCr
[mg/L]
SF
SA
SI
XI
XS
XH
Average
231
0.4
0.0
5.1
23
60
12
Maximum
304
1.3
0.0
9.8
29
70
18
Minimum
110
0.0
0.0
3.5
16
52
8.9
Standard
deviation
Coefficie
nt of
Average
variation
69
0.5
-
2.2
6.0
6.1
3.1
30%
110%
-
43%
26%
10%
27%
235
1.6
0.0
6.4
17
62
14
Maximum
280
3.7
0.0
9.6
19
65
18
Minimum
167
0.0
0.0
5.1
14
56
10
Standard
deviation
Coefficie
nt of
Average
variation
42
1.2
-
1.7
2.1
3.8
2.8
18%
75%
-
26%
13%
6%
21%
206
2.9
4.8
8.0
14
53
17
Maximum
266
4.9
11
9.8
21
57
22
Minimum
154
0.0
0.0
5.9
6.2
49
12
Standard
deviation
Coefficie
nt of
variation
44
2.1
4.4
1.4
4.9
2.8
4.1
21%
71%
91%
18%
35%
5%
24%
(..examination of time varying situation in the following three places in influx route in
waste treatment facility as index of organic acid (SA ) to clarify variation factor of SS of
influent in facilities ③..Figure A4. 23)｡
‒ Part A: The upstream part of inflow screen (crude sewage before sludge
disposal return-into water mixes)
‒ Part B: The first sedimentation pond inflow part (crude sewage after
return-into water mixes)
‒ Part C: Reactive tank inflow part (the first sedimentation pond overflow)
・ The density is overall small, and time variance's pattern is also greatly different
from another though the time variance to whom SA is roughly similar is shown in
part B and C in part A.
・ The composition of SA is different part only A. As for things except the detection of the
propionate in part A with the sample at 17 o'clock while the propionate and the butyrate were
detected in part B･C at time zone with high density of SA, only the acetate has been detected.
Appendix 4Investigation of actual conditions of influent organic matter characterization
385
・ It can be guessed to the time varying of the density of SA seen in the reactive
tank influent of this facilities that the generation of SA with SA and inflow pump
[**] of the sludge disposal return-into water origin contributes greatly from the
change of the influent quality to the waste treatment facility from these. Actually,
the case where high density organic acid is detected with the gravity concentrate
tank overflow and the dehydration separation liquid is reported.
not found., Error! Reference source not found. ｡
Error! Reference source
386
Appendix 4Investigation of actual conditions of influent organic matter characterization
60
Crude sewage
VFA50
[mgCOD/L]
In front of [shizu] naive
40
[Shizugomizu]
30
20
10
0
9:00
13:00
17:00
21:00
1:00
5:00
9:00
Time
Crude sewage:In front of [shizu] naive of the style (There is not a return-into
water mixing) ..on the inflow screen..: the first [go] ..sinking.. water of the
sedimentation pond influent (After the return-into water mixes): Reactor
influent.
Figure A4. 23Time varying of organic acid concentration in inflow system each place (Facilities
③; H16.1.28-29)
387
Appendix 4Investigation of actual conditions of influent organic matter characterization
Change situation in week
The result of the change investigation in the week executed in facilities ③Figure A4.
24And,Table A4. 10[Ni] was shown.
・ Total CODCr and each organism element abundance ratio are steady though the
average water quality of the day based on the composite sample is used here
compared with the above-mentioned time varying. There is no rainfall for this
investigation period.
・ However, the change of SF and SA day in and day out is large, and the coefficient
of variation equals 1.20 respectively, 0.37, a time varying, and the following
secular fluctuations.
・ The difference of the influent quality by the presence of dehydration is not clear
though the dehydration work is not done in this facilities on the soil and Sunday.
300
[mg/L]
250
XI
200
XH
150
Xs
100
Ss
SI
50
0
1/27 1/28 1/29 1/30
(Tue) (Wed) (Thu) (Fri)
1/31 2/1
2/2
2/3
(Sat) (Sun) (Mon) (Tue)
Date
Figure A4. 24Change in week of influent organism element density (Facilities ③; H16.1.27-2.3)
Table A4. 10Amount of statistics concerning organism element abundance ratio in change
investigation in week
Facilitie
s ③
Abundance ratio %
T-CODCr
[mg/L]
SF
SA
SI
XI
XS
XH
Average
199
2.2
1.8
7.6
15
55
18
Maximum
215
3.6
6.2
7.9
22
63
20
Minimum
182
1.2
0.0
7.4
7.6
50
15
Standard
deviation
Coefficie
nt of
variation
12
0.8
2.1
0.2
4.4
4.1
1.6
0.06
37%
120%
2%
29%
7%
9%
388
Appendix 4Investigation of actual conditions of influent organic matter characterization
Long-term change situation
The result of the long-term change investigation in facilities ① -④Figure A4. 25And,
389
Appendix 4Investigation of actual conditions of influent organic matter characterization
Table A4. 11[Ni] was shown.
・ The tendency with a large change of SF and SA is seen in each facilities compared
with total CODCr and another element as well as the change in the time varying
and the week.
・ The tendency to a seasonal change is not clear. However, the tendency to which
the density and the abundance ratio of SF ･SA become small in winter is seen in
facilities ① -③. Especially, both elements are not detected with the greater part
of samples in facilities ① and ② at one February-March.
・ In facilities ④ including the confluence district, the tendency to which total
CODCr extremely becomes a low concentration at the rainfall, and the abundance
ratio of SF and SA becomes small in addition in that is seen. This not only is
diluted drainage at the rainfall but also suggests that conversion in the inflow
process including the incurrent canal ditch be greatly influenced. However, the
influence of the rainfall doesn't appear clearly similarly in facilities ③ including
300
250
250
XI
200
mg/L]
[
XH
150
Xs
Ss
100
Rainfall
Rainfall
→
300
→
the confluence district.
XI
200
mg/L]
[
XH
150
Xs
Ss
100
SI
50
SI
50
0
0
5/28
7/9
9/30
10/5 10/29 12/9
Date
(a) Facilities ①
1/28
2/25
3/24
4/30 5/20 6/24
8/4
9/11 11/25 12/9 1/14 2/24
Date
(b) Facilities ②
3/2
390
Rainfall
300
250
XI
200
mg/L]
XH
150
Xs
[
Rainfall
→
350
→
Rainfall
→
Rainfall
Rainfall
→
→
300
Appendix 4Investigation of actual conditions of influent organic matter characterization
Ss
100
SI
50
XI
250
mg/L]
200
[
XH
Xs
150
Ss
100
SI
50
0
0
5/29 6/26 7/30 8/27 9/17 10/3011/27 12/18 1/28 2/26 3/25
Date
(c) Facilities ③
3/13
5/1
6/18
8/6
9/2 11/17 12/16 1/15 2/27 3/31
Date
(d) Facilities ④
Figure A4. 25Secular fluctuation of influent organism element density
391
Appendix 4Investigation of actual conditions of influent organic matter characterization
Table A4. 11Amount of statistics concerning organism element abundance ratio in long-term
change investigation
Facilitie
s ①
Facilitie
s ②
Facilitie
s ③
Facilitie
s ④
Abundance ratio %
T-CODCr
[mg/L]
SF
SA
SI
XI
XS
XH
Average
225
0.5
0.6
4.8
18
64
13
Maximum
284
1.3
1.5
6.1
30
76
15
Minimum
158
0.0
0.0
3.7
2.2
55
10
Standard
deviation
Coefficie
nt of
Average
variation
47
0.4
0.6
1.0
7.8
6.6
1.6
21%
96%
108%
20%
44%
10%
12%
186
2.1
3.6
9.1
19
49
17
Maximum
246
5.6
10
15
35
58
20
Minimum
136
0.8
0.0
5.9
7.7
42
14
Standard
deviation
Coefficie
nt of
Average
variation
40
1.5
4.2
2.6
8.0
6.5
2.2
22%
72%
116%
29%
42%
13%
13%
193
2.3
5.1
8.4
14
51
19
Maximum
226
6.5
13
10
31
68
23
Minimum
163
0.0
0.0
7.0
2.8
28
15
Standard
deviation
Coefficie
nt of
Average
variation
20
2.2
5.0
1.1
9.9
12
2.7
11%
95%
98%
12%
69%
24%
14%
224
1.4
8.0
9.4
20
46
16
Maximum
305
3.6
13
13
27
53
22
Minimum
91
0.0
0.0
6.9
13
41
12
Standard
deviation
Coefficie
nt of
variation
68
1.3
4.3
2.1
4.6
3.9
2.7
30%
94%
54%
23%
24%
8%
17%
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Appendix 4Investigation of actual conditions of influent organic matter characterization
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Appendix 5Comparison of various fractuation method of influent organic matter
395
Appendix５Comparison of various methods of influent organism
element segmentation
１．Outline
Fixed something doesn't exist though various methods to measure and to presume the
amount (density) in influent of each organism element (SA ,SF ,SI ,XS ,XI ,XH ) defined
by ASM2d are proposed as described in "Business use of the Chapter 4 activated sludge
model".
Here, it introduces the result of presuming and comparing each organism element
densities by using the water quality and the OUR measurement result of influent in a
real sewage plant by eight methods including the influent quality presumption tool built
in a simulator on the market in addition to three kinds introduce by this volume. In
addition, this facilities are actually simulated by using each influent quality, and it
introduces the result of evaluating the influence that the difference of the estimation
method gives the simulation result.
２．Review method
（１）Object facilities
Real facilities of a conceited reluctance-hypoxia-method were targeted. The operating
condition when the facilities parameter and the investigation is executed
396
Appendix 5Comparison of various fractuation method of influent organic matter
Table A5. 1[Ni] was brought together.
（２）Use data
The data used to presume each organism elementTable A5. 2[Ni] was brought together.
These are based on the water quality data and the OUR measurement result that
collected when the expert day executed for the calibration of the model is investigated.
However, the mean of 24h was used from the intention to facilitate the comparison of
the results for all items.
（３）Estimation method of organism element
Table
A5.
2(..drinking
and
the
use
of
data..
..each
organism
element
(SA ,SF ,SI ,XS ,XI ,XH ) according to eight methods.. ..the calculation of the density...
Table A5. 3)｡
Method ① (OUR) ･･････ In the method mainly composed of the OUR measurement, (a) (b)
that divides SS and XS from the difference of the OUR level The point of presuming XH from
OUR is a feature. A complex OUR measurement in addition to water analyses of S-CODCr
of T-CODCr, organic acid, and the treated water of influent is needed.
397
Appendix 5Comparison of various fractuation method of influent organic matter
Processing mode
Facilities
parameter
Capacity
reactive tank
2,190 m3
(reluctance:
m3×3)
Operating condition etc.
Capacity of the
final
sedimentation
Influent quantity
pond
Dirt sending back
ratio
Internal circulation
ratio
960 m3
SRT
12.7d
Water
temperature
reactive tank
MLSS
Conceited
DO
-
of
Table A5. 1Outline of object facilities
Conceited reluctance-hypoxia-method
sedimentation tank).
of
236 m3 and hypoxia:
(There
is
a
primary
236 m3×3 and conceit:
415
5,160 m3/d
0.3
1.05
16.2℃
2,580 mg/L
tank
The first division: 0.3 mg/L and the 2nd division:
1st division: 1.6 Mg/L
0.6 mg/L and the
The operating conditions etc. are the day all ..adoption.. means on [mizu] execution day.
Table A5. 2Outline of measurement data used to presume influent quality
Water quality item
CODCr
Influent
quality
(water
analyses)
Treated
effluent
quality
-
Remarks
192
S-CODCr (0.45 μm)
78
S-CODCr (0.10 μm)
70
Ultimate BOD(BODn)
93
Suspended solid (TSS)
65
VFA
Influent
quality
(OUR
measuremen
t)
Density *
[mgCOD/L]
Presumption from BOD1, 2, 5, 7, and
12.
7
Refer to "Appendix
estimation method.
SS
14
XS
109
〃
XH
34
〃
S-CODCr (1 μm)
16
Simple mean of 24 4h pitch × h.
3"
for
the
398
Appendix 5Comparison of various fractuation method of influent organic matter
Method ② (JS#1) ･･････ It is a method for use in JS chiefly, and the SS presumption by the
OUR measurement and the combination of the XS presumption by the ultimate BOD
measurement are the points. Necessary water analyses are S-CODCr of T-CODCr of influent,
organic acid, ultimate BOD, and the treated water.
Method ③ (JS#2) It is a simple method that assumes the OUR measurement and the ultimate
BOD measurement to be unnecessary to method ② of ･･････, and presumes according to
conversion factor (Only about T- CODCr;SS, organic acid) based on investigation of actual
conditions by JS instead.
Method ④ (STOWA) It is a simple method proposed by STOWA of the ･･････ Netherlands.
(b) that doesn't measure OUR, and presumes the gross weight of the organism of a possible
biodegradation from the (a) ultimate BOD measurement result (c) into which SS and XS are
physically divided by furnace paper (0.10 μm) It has the feature that XH doesn't expect it.
Measurements of S-CODCr of T-CODCr of influent, organic acid, ultimate BOD, and the
treated water are used.
Method ⑤ (SIM#1) It is a simple method built in the simulator of the ･･････ marketing. A
floating element and a dissolubility element are first divided on the basis of gross weight (Σ
X) of the floating element presumed from TSS measurements, and each breakdown is
calculated according to conversion factor. At least, T-CODCr of influent and the
measurement data of TSS are necessary.
Method ⑥ (SIM#2) In the simple method built in the simulator of the ･･････ marketing, the
point to calculate a floating element and gross weight each dissolubility element first of all on
the basis of TSS measurements and the point to calculate the breakdown from conversion
factor are similar to method ⑤. At least, T-CODCr of influent and the measurement data of
TSS are necessary.
Method ⑦ (SIM#3) All elements that exclude XS are calculated from conversion factor to
T-CODCr by the simple method built in the simulator of the ･･････ marketing. At least, the
measurement data of T-CODCr of influent is necessary.
Method ⑧ (default) ･･････ All elements were calculated from T-CODCr on the basis of the
influent quality illustrated in the technical report of ASM2d.
Appendix 5Comparison of various fractuation method of influent organic matter
399
Note the following points about method ⑤ -⑦ to refer to a simulator on the market.
・ These simple methods are not recommended in each simulator. It handles it as an
alternative method when it is not possible to experiment at an enough data
collection and the batch.
・ These are basically input data, and the user sets it though the numerical value is
given to conversion factor used beforehand. That is, work to decide the coefficient
on the basis of the data measured in the object facilities is needed if original. 5A)｡
・ The plural corresponding to data that can be used kind of influent quality setting
tool is installed in a simulator on the market usually. It removes from the object,
and T-CODCr is equal to the method of presuming T-CODCr of influent in all the
methods though the one with an unnecessary measurement of T-CODCr is
included in that in this discussion. As the corresponding method when the
measurement data of CODCr is not obtained, the method of presuming the gross
weight of the organism of a possible biodegradation from BOD5 is a subject (For
instance, rate constant kBOD is assumed to be a parameter from BOD5 and
ultimate BOD is presumed), and the coefficient value used there should note it
similar to on.
（４）Simulation methodology
The simulation of the object facilities was executed by using the influent quality
presumed by the each way. The condition is a simulation by the steady condition that
gives the average data of the day when the investigation is executed as follows.
・ The reactive tank composition in the process model was assumed to be real
facilities and identical.
・ The final sedimentation pond model was assumed to be an ideal sedimentation
pond model who with the same capacity as real facilities.
・ The day mean when everything was investigated on the expert day was used for
various flow rate and water temperatures. Dirt pulled out and set the flow rate on
the basis of this investigation date Kon's SRT.
・ The day mean when influent qualities (SNH4 ,SNO3 ,SPO4 ,SALK ) other than
the organism element were investigated this was used.
・ DO of the conceited tank each division was compulsorily fixed to the day mean
when this investigating.
・ All defaul t values were used for the parameter of ASM2d.
400
Appendix 5Comparison of various fractuation method of influent organic matter
・ Numerical calculation by Runge-the fourth Kutta methods (*t=10s). The
simulation of each influent quality for 90d was executed, and each element
density at that time was used to evaluate the result. Each element density of a
reactive tank reached roughly regularly on this calculation condition in all the
simulations though initial condition was assumed to be a stationary solution
when the estimated result by method ① was used.
Table A5. 3Outline of each organism element estimation method
Element
Method ①
(OUR)
Method ②
(JS#1)
SS
･Presumption from OUR measurement
result
･Presumption from OUR measurement
result
･Conversion from SA
(SS =1.1×SA+1.4)
･ S-CODCr and 0.10 μ m
measurement value- SI
SA
･Organic acid measurement value
･Organic acid measurement value
･Organic acid measurement value
･Organic acid measurement value
SF
･SS-SA
･SS-SA
･SS-SA
･SS-SA
SI
･ Treated water S-CODCr
measurement value
XI
･T-CODCr-Σ another element
･T-CODCr-Σ another element
･T-CODCr-Σ another element
･T-CODCr-Σ another element
XS
･Presumption from OUR measurement
result
･Presumption from ultimate BOD and
SS
･T-CODCr×0.55
･Presumption from ultimate BOD and
SS
XH
･Presumption from OUR measurement
result
･Presumption from OUR measurement
result
･T-CODCr×0.15
･It doesn't expect it.
Remark
s
･T-CODCr is an actual measurement
value.
･T-CODCr is an actual measurement
value.
･T-CODCr is an actual measurement
value.
･T-CODCr is an actual measurement
value.
actual
･ Treated water S-CODCr
measurement value
Method ③
(JS#2)
actual
･ Treated water S-CODCr
measurement value
Method ④
(STOWA)
actual
･ Treated water S-CODCr
measurement value ×0.9
actual
actual
Table A5. 3..(.. ..continuation..)
Method ⑤
(SIM#1)
Element
Method ⑥
(SIM#2)
Method ⑦
(SIM#3)
Method ⑧
(default)
SS
-
-
･ T-CODCr ×
/T-CODCr ratio)
SA
･ΣS × coefficient (SA /ΣS ratio)
･Organic acid measurement value
･SS × coefficient (SA /SS ratio)
･T-CODCr×0.077
SF
･ΣS × coefficient (SF /ΣS ratio)
･ΣS × coefficient (SF /ΣS ratio)
･SS-SA
･T-CODCr×0.115
SI
･ΣS-SA-SF
･ΣS × coefficient (SI /ΣS ratio)
･T-CODCr × coefficient (SI /T-CODCr
ratio)
･T-CODCr×0.115
XI
･ΣX-XS-XH
･ΣX-XS-XH
･T-CODCr × coefficient (XI /T-CODCr
ratio)
･T-CODCr×0.096
XS
･ΣX × coefficient (XS /ΣX ratio)
･ΣX × coefficient (XS /ΣX ratio)
･T-CODCr-Σ another element
･T-CODCr×0.481
XH
･ΣX × coefficient (XH /ΣX ratio)
･ΣX × coefficient (XH /ΣX ratio)
･ T-CODCr ×
/T-CODCr ratio)
･ Σ X=TSS ÷ coefficient (TSS/COD
ratio)
･ΣS=T-CODCr-ΣX
･T-CODCr is an actual measurement
value.
･ Σ X=TSS × coefficient (COD/VSS
ratio)× coefficient (VSS/SS ratio)
･ΣS=T-CODCr-ΣX
･T-CODCr is an actual measurement
value.
･T-CODCr is an actual measurement
value.
Remark
s
coefficient
coefficient
(SS
(XH
-
･T-CODCr×0.115
･T-CODCr is an actual measurement
value.
Appendix 5Comparison of various fractuation method of influent organic matter
403
３．Result of review
（１）Estimated result of organism element
The influent organism element density presumed by the each wayFigure A5. 1[Ni] was
shown. Because 0 had been given to the initial value only as for SA in method ⑥, the
measurement data of organic acid in facilities A was used though the one substituted for
each simulator as a initial value was used for conversion factor used by method ⑤ -⑦.
・ Each organism element density is greatly different depending on the estimation
method.
・ In the method (① and ②) using the OUR measurement, SF is a small, and the
amount XS is large estimated results.
・ Method ③ is presumption based on the investigation of actual conditions result
of the sewage plant in our country based on the OUR measurement, and estimate
value equal with method ① and ②.
・ In method ④, SF and XS are this almost densities and features the point that XI
is presumed remarkably greatly.
・ The point where the abundance ratio of SF is comparatively large is common
though a big difference is seen in the estimated result in method ⑤ -⑦ based on
a simulator on the market.
・ SS can be divided roughly into a small group ( ① ..method.. - ③ ) and a large group
(④ ..method.. -⑥) with SS(SA+SF ) when paying attention to the division of XS. Method ⑦
and method ⑧(It is based on the component ratio illustrated in the technical report of
ASM2d) are the intermedius levels of both groups.
404
Appendix 5Comparison of various fractuation method of influent organic matter
200
XI
[mgCOD/L]
150
XH
XS
SF
100
SA
SI
⑧default
⑦SIM#3
⑥SIM#2
⑤SIM#1
④STOWA
③JS#2
②JS#1
0
①OUR
50
Figure A5. 1Estimated result of influent organism element density by various methods
Appendix 5Comparison of various fractuation method of influent organic matter
405
・ It is not clear whether it is originates in the estimation method that the point
that SS becomes a low concentration in method ①
-③
uses the OUR
measurement or a general feature of the municipal sewage in our country
(Method ④ -⑧ uses the coefficient value etc. found in Europe and entire North
America).
・ In ASM2d, the organic nitrogen and the phosphorus concentration are calculated
from each organism element by conversion factor (iN,i ,iP,i ). (..all the conversion
factors.. ..the difference of an organism element concentration estimation value
the above-mentioned when the default value is used.. ..becoming also of the
calculation value of the organic nitrogen and the phosphorus concentration of
influent a big difference and appearance... Figure A5. 2)｡
（２）Simulation result that uses various organism element estimate values
With the actual measurement value (day mean) when SNH4, SNO3, and the SPO4
density (profile) of the reactive tank each division in the simulation of which the input
data is each estimated result are investigated on the expert dayFigure A5. 3～Figure A5.
5[Ni] was shown. Moreover, SNH4, SNO3, and the SPO4 density of the treated water
(reactive tank end division)Figure A5. 6Living being element (XH ,XPAO ,XAUT ) and
the XTSS density of [ni] and this divisionFigure A5. 7[Ni] was shown.
・ The difference of the influent quality according to the estimation method
especially has a big influence on the expectation of the phosphorus removal, and
the concentration profile of SPO4 and the simulation result of the amount of
XPAO are greatly different depending on the estimation method.
・ In method ① -③, a density of treated water SPO4 that is small SPO4 burst size
in the reluctance tank, higher than that of the actual measurement value is given.
SF estimate value reflected the small point in this.
・ On the other hand, SPO4 burst size in reluctance-hypoxia tank has risen overall
than the actual measurement value though the SPO4 density of the treated water
has decreased even at the measurement value level in method ④ -⑧.
406
Appendix 5Comparison of various fractuation method of influent organic matter
8
7
6
5
[mg/L]
Org-N
4
Org-P
3
2
1
0
OUR
JS#1
JS#2
①
②
③
STOWA SIM#1
④
⑤
SIM#2
SIM#3
default
⑥
⑦
⑧
Figure A5. 2Calculation result of organic nitrogen and phosphorus concentration of influent that
uses each estimated result
Appendix 5Comparison of various fractuation method of influent organic matter
407
・ The difference between the above-mentioned SPO4 density and the profile is the
levels that greatly change the policy of the calibration when thinking the fitting is
done to the measurement data shown here in the calibration. For instance, it will
be adjusted that the SPO4 density is decreased in method ④ -⑧ while becoming
in method ① -③ the adjustment of the direction where the SPO4 density is
increased to reproduce the behavior of SPO4 under the anoxic.
・ The XI density of a reactive tank is greatly different according to the difference of
the estimated result of XI of influent. That is, the simulation result of the reactive
tank solid concentration and the amount of the excess sludge solid is also greatly
different depending on the setting method of the influent quality.
Bibliography
5A) Makinia,J., Swinarski,M. and Dobiegala,E.: Experiences with computer simulation at two large
wastewater treatment plants in northern Poland. Wat.Sci.Tech., Vol.45, No.6, pp.209-218, 2002.
408
Appendix 5Comparison of various fractuation method of influent organic matter
Measurement
18
16
14
[mgN/L]
12
10
NH4 8
6
S
4
2
0
① OUR
② JS#1
③ JS#2
④ STOWA
⑤ SIM#1
⑥ SIM#2
ReluctanceHypoxia 1 Hypoxia 2
Hypoxia 3
Conceit 1
Conceit 2
Conceit 3
Reactive tank division
⑦ SIM#3
⑧ default
Figure A5. 3Reactive tank profile in simulation that uses each estimated result (SNH4 )
8
Measurement
7
① OUR
6
[mgN/L]
② JS#1
5
③ JS#2
4
NO3
S
3
④ STOWA
2
⑤ SIM#1
1
⑥ SIM#2
0
ReluctanceHypoxia 1
Hypoxia 2
Hypoxia 3
Conceit 1
Reactive tank division
Conceit 2
Conceit 3
⑦ SIM#3
⑧ default
Figure A5. 4Reactive tank profile in simulation that uses each estimated result (SNO3 )
409
Appendix 5Comparison of various fractuation method of influent organic matter
Measurement
20
①We
15
MgP/lambert
②JS#1
③JS#2
10
PO4
④STOWA
S
⑤SIM#1
5
⑥SIM#2
0
ReluctanceHypoxia 1 Hypoxia 2
Hypoxia 3
Self-conceitSelf-conceit
1
Self-conceit
2
3
Reactive tank force
⑦SIM#3
⑧Default
Figure A5. 5Reactive tank profile in simulation that uses each estimated result (SPO4 )
410
Appendix 5Comparison of various fractuation method of influent organic matter
8
7
6
[mg/L]
5
4
3
2
1
0
SNH4
実測
①OUR
②JS#1
SNO3
③JS#2
④STOWA
SPO4
⑤SIM#1
⑥SIM#2
⑦SIM#3
⑧default
Figure A5. 6Element density of reactive tank end division in simulation that uses each estimated
result (SNH4 ,SNO3 ,SPO4 )
2,000
1,800
2,670
1,600
[mg/L]
1,400
1,200
1,000
800
600
400
200
XH
①OUR
②JS#1
XPAO
③JS#2
④STOWA
XAUT
⑤SIM#1
⑥SIM#2
XI
⑦SIM#3
⑧default
Figure A5. 7Element density of reactive tank end division in simulation that uses each estimated
result (XH ,XPAO ,XAUT ,XI )
Appendix 6Sensitivity analysis example
411
Appendix６Example of sensitivity analysis
１．Outline
Work to evaluate that it is the impact combination of giving of various parameter values
in the activated sludge model and the input data, etc. when simulating it to the
simulation result is called "Sensitivity analysis". It does defining the condition of
becoming basic (case etc. to use the default value for all parameters), and repeating the
simulation to which the parameter etc. made a analysis object there are changed, and
the calculation result is compared and will be evaluated specifically.
In "Business use of the Chapter 4 activated sludge model", this work is not necessarily
done with one process of the calibration though locates as indispensable work. However,
when the calibration and the simulation result are evaluated as the influence that the
uncertainty of (b) process model and input data to be able to extract the candidate of the
parameter and the input condition adjusting it at the (a) calibration by the sensitivity
analysis gives the simulation result can be understood, useful information can be
obtained. Additionally, it can be said that it is one of the very effective methods to
understand the meaning and the role of each parameter in the model to consider that it
is the impact combination about each parameter value in detail.
Here, it introduces the result of doing the sensitivity analysis for the waste treatment
facility of virtual, that the system configuration is different four kinds of. It is sure to
become useful information as a material to understand the image of the sensitivity
analysis work and to consider the meaning of parameter value and other condition
settings though this result cannot be applied to other cases as it is because it is different
the impact combination about parameter value and the input condition depending on
the composition and the condition of the processing system that targets it. Additionally,
it introduces the case where the influence of the time varying of the case and the inflow
load (volume of water and water quality) in which the influence of parameter value
involved in the simulation result of the biological phosphorus removal is discussed more
in detail is discussed in the sensitivity analysis. The former is a part of a series of
discussion executed to discuss the calibration procedure related to the phosphorus
removal. Moreover, the latter was executed as a part that discussed the amount and the
quality of appropriate input data for the simulation.
412
Appendix 6Sensitivity analysis example
２．Sensitivity analysis intended for various processing mode
（１）Outline
Facilities virtual that seems to be typical it of four kinds of activated sludge processes
(reluctance ..a conceited method and the circle method nitrification denitrification
method..-conceited active sludge process and reluctance-hypoxia-conventional activated
sludge process) used are set in the sewage plant in our country.
The sensitivity
analysis that targeted the item that the user had to set when simulating it like all
parameters, the influent qualities, and the operating conditions etc. of ASM2d covering
it was executed.
（２）Review method
Object facilities
A parameter of virtual facilities (A~D) made the object of the analysis and a basic
operating condition, etc.Table A6. 1[Ni] was brought together. When a calculation of
capacitance and various operating conditions are set, an existing design principle.
6A)It
referred to [wo].
Process model
The process model was constructed on the basis of the set facilities parameter, and the
influent quality and the operating condition were set. The main note is as follows.
・ ASM2d was used as a biological response model.
・ A reactive tank was expressed according to the complete mixing [souretsu] model.
Table A6. 1Wanting one complete mixing tank and be making (The reluctance
tank: one division and hypoxia tank: four divisions and conceited tanks: four
divisions) severally of the shown [ni] each division.
・ The final sedimentation tank was modeled as an ideal sedimentation pond where
the floatage element did not flow out to the treated water. However, it tempered
with the phenomenon in the waste treatment facility of the reality, and DO(SO2 )
of the sending back dirt and nitrate-nitrogen (SNO3 ) were compulsorily 0 in
facilities A and B It fixed to mg/L. The purpose of this is to exclude a part of an
indirect influence through the sending back dirt water quality when parameter
value etc. are changed.
・ The time varying was not given to all the flow rate (influent, sending back dirt,
413
Appendix 6Sensitivity analysis example
dirt pulling out, and internal circulation), and it was assumed the definite value.
・ SRT (And, ASRT) : the dirt pulling out flow rate. Table A6. 1It was set that it
became a shown [ni] numerical value. A numerical value different in facilities A･B
where nitrification was required and facilities C･D not so was set to ASRT (14d
and 5 eachd). Because HRT of a conceited tank is assumed to be identical in all
facilities, the difference of SRT by the difference of the solid concentration of a
reactive tank will be chiefly expressed.
・ The water temperature was fixed to 20℃. ASRT set in facilities A･B is making
the maintenance of nitrifying bacteria at design water temperature (13 ℃ ),
assumption it can be said the condition with room as capacity of a reactive tank.
・ The DO(SO2 ) density of a conceited tankTable A6. 1It fixed compulsorily to the
shown [ni] numerical value. Because the DO density was required to reflect the
realities in the calibration as well as the sending back dirt water quality, an
indirect influence through this was excluded. This DO set point was a condition of
evenly supplying air to each division of a conceited tank in facilities A, and DO in
the conceited tank end was 1.5 It set it from the DO density of a conceited each
tank when the state to control a total blast volume to become mg/L was simulated
(All the default value was used for the parameter), and the calculation result
reached regularly.
Table A6. 1Outline of virtual facilities made object of sensitivity analysis
Facilities name
Facilities A
Facilities B
Facilities C
Facilities D
Processing
mode
Conceited
reluctance-hypoxia-m
ethod
Circle method
nitrification
denitrification method
Conceited
reluctance-active
sludge process
Conventional
activated sludge
process
Reactive tank
Composition
Reluctance ×1
Hypoxia × four
conceit ×4
Hypoxia ×4
Conceit ×4
Reluctance ×1
Conceit ×4
Conceit ×4
Reactive tank
HRT
15.5h
(8h of reluctance
1.5h+ hypoxia 6h+
conceit)
14h (8h of hypoxia
6h+ conceit)
11.3h (8h of
reluctance 3.3h+
conceit)
8h (conceit 8h)
SRT(ASRT)
27d(14d)
24.5d(14d)
7.1d(5d)
5d(5d)
Dirt sending
back ratio
Internal
circulation ratio
0.5
1.5
-
414
Appendix 6Sensitivity analysis example
Water
temperature
20℃
Conceited tank
DO*1
The first division: 0.3 mg/L and the 2nd division: 0.5 mg/L and the 3rd division: 0.7 mg/L and
the 4th division: 1.5 Mg/L
Influent
two ..quality *..
[mg/L]
T-COD:235, SI :23, SF :2.4, SA :9.6, XI :35, XS :135, XH :30
XAUT･XPAO :0.2, SNH4 :22.6, SNO3 :0, SPO4 :1.9, SALK :250
Reactive tank
end water
quality mg/L in
basic condition
SNH4 :1.2,
SNO3 :6.6,SPO4 :0.8
, XI :2,370,XS :24,
XH :680,
XPAO :330,
XPP :99XPHA :0.3,
XAUT :55,
XTSS :3,080
SNH4 :0.8,
SNO3 :6.7,SPO4 :1.8
, XI :2,440,XS :25,
XH :850,
XPAO :190,
XPP :58XPHA :0.2,
XAUT :61,
XTSS :3,030
SNH4 :2.4,
SNO3 :11.3SPO4 :0.
8, XI :750,XS :18,
XH :753,
XPAO :89,
XPP :27XPHA :0.1,
XAUT :43,
XTSS :1,460
SNH4 :0.9,
SNO3:16.0,SPO4 :2.
3, XI :724,XS :22,
XH :980,
XPAO :3,
XPP :0.8XPHA :0.0,
XAUT :52,
XTSS :1,490
- 1 Compulsion fixed value.
- 2 Basic condition. It sets it on the basis of a sedimentation pond overflow average at the beginning composition in investigation of actual conditions.
Appendix 6Sensitivity analysis example
415
・ First of all, when the organism element density of influent is set, do investigation
of actual conditions result (..BOD5 used by the calculation of capacitance.. "by JS.
Error! Reference source not found.
Error! Reference source not found.It
converted into CODCr on the basis of Refer to"), and each element density was set
on the basis of an average water quality composition of [shizugomizu] that had
been obtained in addition for investigation of actual conditions. On the other hand,
what used by the calculation of capacitance was used as it was for nitrogen and
the Rin element density. The time varying was not given to the influent quality as
well as the influent quantity.
Object and evaluation method of sensitivity analysis
Table A6. 1In shown inflow condition and [ni] operating condition, the condition using
the default value was assumed to be "Basic condition" to all parameters. On the other
hand, the element density when the simulation that individually changed the following
analysis objects respectively by ±20% was executed, and the calculation reached
regularly was used for the evaluation.
‒ All parameters of ASM2d (kinetics constant, amount of theory coefficient,
and conversion factor)
‒ Organism element density of influent (SA ,SF ,XI ,XS ,XH )
‒ Operating condition etc. (water temperature, influent quantity, dirt sending
back ratio, internal circulation ratio, dirt pulling out flow rate, and conceited
tank DO density)
The element used as an evaluation target is SNH4, SNO3, SPO4, and XTSS of the
reactive tank each division.
The following indices were used to evaluate that the simulation result was the impact
combination about each analysis object (sensitivity).
・ Amount (AbSp,n,y ) of the average relative change: Expression in the mean
though the variation in reactive tank division n to the basic condition of element y
was divided in the variable ratio of a function of change of analysis object p(+20%
or -20% in case of this discussion).
Expression (A6. 1)Each analysis object and
each element of the [niyori] each reaction tank for the evaluation were calculated.
A negative value is taken in the axis of abscissas when positive and decreasing
416
Appendix 6Sensitivity analysis example
when y increases when it corresponds to the inclination of the straight line when
element y is plotted in the variable ratio of a function of change and the spindle of
analysis object p, and p is increased. It uses it as a basic index to express the
sensitivity of each analysis object in this discussion.
pdef 

AbS p ,n, y  Ave y 

p 

Expression (A6. 1)
417
Appendix 6Sensitivity analysis example
It is ?y here: the variation and pdef to the basic condition of element y: It is
variation of p to the value of analysis object p in the basic condition and ?p:pdef.
・ Sensitivity uniqueness (SPy,p ): In the reactive each tank division, it is an
expression as the index that expresses analysis object p influences element y in
the peculiarity very (Compared with the influence on other elements for the
evaluation).
Expression (A6. 2)[Niyori] was calculated. It means analysis
object p influences only element y in the peculiarity by taking the range of 0-1,
and the numerical value large (It is near one) as for this index in the division.
ReS 

 ReS 
2
SPp ,n , y
p ,n, y
2
Expression (A6. 2)
p ,n ,k
k
ReSp, n, and y here: ..(of the rate of the average relative change.. expression of y in
division n by p. Expression (A6. 3))The denominator will calculate the ratio of contribution of the
 y p def  AbS p ,n , y
Re S p ,n , y  Ave 


y def
 y def p 
Expression (A6. 3)
It is ydef here: It is element y density in the basic condition.
Calculation way
The calculation condition in the simulation is as follows. Note the point evaluated when
it is not the process but it calculates on the 90th and each element density reaches
roughly regularly though each element density begins to change according to it when
the analysis object is changed from the basic condition.
‒ Numerical calculation: Runge-the fourth Kutta methods
‒ Width (*t) of time interval in numerical calculation: 10s
‒ Initial condition: Stationary solution in basic condition
‒ Simulation period: 90d
418
Appendix 6Sensitivity analysis example
（３）Result of review
Simulation result in basic condition
Because it evaluates that it is the impact combination about the analysis object by the
variation and the variable ratio of a function of change to the basic condition in the
sensitivity analysis, it is important to consider the simulation result in the basic
condition enough. The water quality profile of a reactive tank of facilities A-D in the
basic conditionFigure A6. 1Each element density of [ni] and the reactive tank end
divisionTable A6. 1[Ni] was shown.
The latter half of this tank becomes an anoxic because there is room in the capacity of
the hypoxia tank, and the feature the point that the discharge of then remarkable SPO4
is
caused
though
a
water
quality
changing
typical
a
conceited
reluctance-hypoxia-method like nitrification, the denitrification, the discharge of
PO4-P(SPO4 ), and the incorporation, etc. is seen in facilities A. The hypoxia tank latter
part of facilities B where the biological phosphorus removal is not considered on the
facilities parameter substantially functions similarly to this as a reluctance tank, and
the calculation result (The proportion of XPAO in XTSS equals facilities C of a conceited
reluctance-method) to which some biological phosphorus removal progresses. In
facilities C, the biological phosphorus removal is excellently caused, and nitrification
progresses, too. However, the SNH4 density of the treated water is a little high
compared with other facilities. It is a calculation result that XPAO doesn't exist in the
faction because there is no division that becomes an anoxic also in facilities D though
nitrification progresses excellently.
Sensitivity to nitrification
The NH4-N(SNH4 ) density of the reactive tank end division of the influence on the
nitrification of parameter value etc. that become analysis objects is arranged as the
main index.
419
Appendix 6Sensitivity analysis example
Table A6. 2Sensitivity (The amount of the average relative change; AbSp, n, and y) and
sensitivity uniqueness (SPp,n,y ) were shown about SNH4 of the reactive tank end
division of each facilities of ..peel.. ,. The parameter of ASM2d is extracted only the one
of 0.5 or more by sensitivity, divided on the basis of the involved reaction process, and
displayed. On the other hand, the result of all analysis objects is published about the
influent quality and the operating condition.
・ Sensitivity with a big parameter concerning XAUT is shown and the sensitivity
uniqueness is also high (It influences SNH4 in the peculiarity) in each facilities.
Especially, sensitivity that μAUT that changes the proliferation rate of XAUT (=
nitrification rate) directly is the maximum in any facilities is shown. Moreover,
the sensitivity of bAUT is common, high, too and it is shown that the amount of
XAUT in the faction greatly influences SNH4 of the treated water. The sensitivity
of various saturation factor that defines that excluding this, the proliferation rate
of XAUT such as KO2, KNH4, and KALK is the impact combination about the
environmental factor is comparatively high.
・ The sensitivity of the above-mentioned parameter is growing in facilities where
the density of treated water SNH4 in the basic condition is high, and the
calculation result according to nitrification is overall sensitive to parameter value.
In this, in the simulation usage in which the marginal condition to maintain
nitrifying bacteria in the faction is found, it is suggested that the prediction result
greatly depend on parameter value, and attention is a necessary point.
25
25
SNH4
20
SNH4
20
SNO3
[mg/L]
15
SNO3
[mg/L]
15
SPO4
10
10
5
5
0
SPO4
0
1
2
3
4
5
6
7
Reactive tank division
(a) Facilities A
8
9
1
2
3
4
5
6
Reactive tank division
(b) Facilities B
7
8
420
Appendix 6Sensitivity analysis example
25
SNH4
SNO3
25
20
SPO4
SNH4
20
SNO3
[mg/L]
15
[mg/L]
15
SPO4
10
10
5
5
0
1
0
1
2
3
4
5
2
3
4
Reactive tank division
Reactive tank division
(c) Facilities C
(d) Facilities D
Figure A6. 1Simulation result by basic condition (reactive tank water quality profile)
・ There is something that shows SNH4 big sensitivity also in the parameter
concerning XPAO in facilities A and C. This is because the limitation paragraph
according to SPO4 greatly influences in the rate equation in the proliferation
process of XAUT the SPO4 density of the conceited tank latter part is small in
both facilities. Because the nitrification rate doesn't decrease remarkably even if
SPO4 becomes a low concentration, such a phenomenon is actually prevented
being caused by reducing KP (KP,A ) in this process in the calibration.
・ The influence of the influent organism element density is a small overall.
・ The DO ..pulling out.. density of dirt of [haka] and a conceited tank comparatively
shows big sensitivity in the operating condition etc.As for the former, if it is
considered that ..dirt.. ..pulling out.. [haka] in the final sedimentation pond model
used in this discussion decides SRT, it is a result of it is easy to understand. If the
DO density of the object facilities cannot be understood on such a condition, the
uncertainty at the calibration extremely grows though the influence of the DO
density is growing by equaling to parameter value.
Sensitivity to denitrification
Sensitivity to the SNO3 density of the reactive tank end division and the sensitivity
uniqueness as an influence on the denitrification as well as the preceding clause
Appendix 6Sensitivity analysis example
421
Table A6. 3[Ni] was brought together.
・ Generation by nitrification contributes chiefly, and many of parameters
concerning XAUT show big sensitivity here for the change in a conceited tank in
the SNO3 density. Validity that does the calibration of nitrification before the
denitrification is here.
・ In other parameters, the parameter that influences the denitrification rate such
as ηNO3 and KO2 concerning XH directly comparatively shows big sensitivity,
and the sensitivity uniqueness is also high.
・ This is because the density of the total nitrogen that flows in chiefly changes
though conversion factor iN and Xs comparatively show big sensitivity. As for the
point where the sensitivity of the XS density of influent is large, it is similar.
・ In the operating condition etc. , the internal circulation ratio in facilities A･B in
addition to [haka] and the DO density shows big sensitivity in the preceding
clause pulling out ..showing.. ..dirt... This is because the SNO3 load to the
hypoxia tank is chiefly decided depending on the internal circulation ratio, and a
common characteristic to the process of a hypoxia tank prepositive type.
Sensitivity to phosphorus removal
Sensitivity to the SPO4 density of the reactive tank end division and the sensitivity
uniqueness as an influence on the biological phosphorus removal
422
Appendix 6Sensitivity analysis example
Table A6. 4[Ni] was shown. About the influence of the parameter concerning XPAO,
"Error! Reference source not found.Error! Reference source not found.Refer to".
・ Sensitivity with a big a lot of parameters is shown excluding facilities D where
XPAO doesn't exist in the faction.
・ It has sensitivity with a big parameter related to hydrolysis involved in the substrate supply to
XPAO in addition to the parameter that changes the behavior of XPAO, and the sensitivity
uniqueness is also high.
Appendix 6Sensitivity analysis example
423
・ There is something that shows sensitivity big as for the parameter concerning XH. As for XH,
only it is not in XPAO and the competition over the organic substrate, and because the
substrate supply to XPAO as the subject of hydrolysis and the fermentation process is borne,
the way of the influence appearance is complex.
・ Conversion factor iP and Xs see to change the amount of total phosphorus that
flows in as well as nitrogen and have big the above sensitivity.
・ SA and XS comparatively show big sensitivity, and it is suggested that the
behavior of XPAO be limited by supplying the organic substrate in each facilities
in the influent organism element. As for the influences of the organism element
density except XI, the majority appear to the phosphorus removal (The sensitivity
uniqueness to SPO4 is high).
・ The influence of the operating condition etc. is also larger than that of
nitrification and the denitrification.
Sensitivity to abundance of solid
Sensitivity to the XTSS density of the reactive tank end division and the sensitivity
uniqueness as an influence on the abundance of the solid
424
Appendix 6Sensitivity analysis example
Table A6. 5[Ni] was shown. Because SRT is fixed in the simulation condition used by
this discussion, the XTSS density of a reactive tank can be handled as an index that
reflects the abundance of the solid directly.
・ It has sensitivity with big growth yield and ratio autolysis speed that influences
the amount in the faction of the main living being element (XH and XPAO) in the
parameter.
・ Because the amount of autolysis relatively grows in facilities A･B where SRT is
long, the sensitivity of fXI that defines the XI yield according to autolysis is
growing.
・ The sensitivity of XI and XS is large in the influent organism element. XI is a
parameter that influences only the abundance of the solid in the peculiarity. On
the other hand, XS influences the biomass in the faction and the amount of XI of
the autolysis origin as the main organism element of influent.
・ The sensitivity of [haka] ..the influent quantity, the dirt sending back ratio, and
dirt.. ..pulling out.. is large in the operating condition. The influence of the inflow
load remarkably appears to the solid concentration of a reactive tank because
SRT is kept constant though the inflow load volume changes by changing the
influent quantity in this discussion condition. On the other hand, ..pulling
out ..the dirt sending back ratio and dirt.... [haka] influences the abundance of the
solid because it changes SRT.
Sensitivity to treated water SNH4 and the SPO4 density when three of the conditions
setting the influent organism element density by using the component ratio illustrated
in the condition of reducing condition (ASRT=14d→9d) with different (a) SRT and the
capacity of the (b) hypoxia tank to 60% and the (c) technical report about facilities A
referring executes a similar sensitivity analysis respectively as a basic condition
Appendix 6Sensitivity analysis example
Table A6. 6､
425
426
Appendix 6Sensitivity analysis example
Table A6. 7[Ni] was published. If the facilities condition, the inflow condition, and the
operating condition, etc. are different even if it is the same processing mode, the point
where the sensitivity analysis result is different should be able to be understood.
427
Appendix 6Sensitivity analysis example
Table A6. 2Summary of sensitivity to SNH4 in reactive tank end
Analysis
object
....drinkin
g.. [ku]..
Addi
tion
of
wate
r
Res
oluti
on
Facilities A
(conceited
reluctance-hypoxia-metho
d)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
Facilities B
(circle method nitrification
denitrification method)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
Facilities C
(conceited
reluctance-active sludge
process)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
Kh
on
1.12
2%
ηfe
1.10
2%
Facilities D
(conventional activated
sludge process)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
Parameter
XH
Rela
tion
XPAO
Rela
tion
YPAO
0.75
2%
μAUT
bAUT
KO2
KNH4
KALK
-15.07
7.18
3.24
1.81
0.96
99%
99%
99%
99%
99%
μAUT
bAUT
KO2
KNH4
KALK
-7.02
3.66
1.82
1.22
0.55
100%
100%
100%
100%
100%
Inflo
w
Wat
er
quali
ty
SF
SA
XI
XS
XH
0.00
-0.02
0.00
0.06
0.00
0%
0%
0%
0%
0%
SF
SA
XI
XS
XH
0.00
-0.02
0.00
-0.04
0.00
0%
0%
0%
0%
0%
-0.11
27%
-0.06
76%
-0.04
0%
0.01
0%
Drivi
ng
Con
ditio
n
[Na],
[do]
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-0.93
21%
-0.50
57%
0.17
1%
0.18
3%
1.52
24%
0.86
60%
-3.76
91%
-2.05
94%
XAUT
Rela
tion
YPAO
bPAO
qPHA
41.12
-1.06
0.68
87%
2%
0%
μAUT
bAUT
KO2
KNH4
KALK
YA
-54.39
16.50
11.11
3.35
2.64
0.76
97%
88%
86%
83%
83%
43%
μAUT
KO2
bAUT
KNH4
KALK
-10.69
2.35
2.33
1.28
0.74
100%
100%
100%
100%
100%
0.04
0.08
0.00
1.63
0.08
0%
0%
0%
3%
0%
SF
SA
XI
XS
XH
0.00
-0.04
0.00
-0.05
0.01
1%
36%
0%
1%
1%
-0.86
74%
-0.25
99%
0.08
0%
0.10
2%
-12.47
66%
-2.03
95%
-
-
-
-
11.85
91%
3.15
95%
-25.23
89%
-2.61
97%
Oth
ers
Other input data
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
SF
SA
XI
XS
XH
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
Basic
condition
1.21 mg/L
0.77 mg/L
2.39 mg/L
0.85 mg/L
[Deno]
- density
The amount of the average relative change displays it, and the small one and the order display from 0.5 and the 16th place
following are not displaying the parameter.
428
Appendix 6Sensitivity analysis example
Table A6. 3Summary of sensitivity to SNO3 in reactive tank end
Analysis
object
....drinkin
g.. [ku]..
Addi
tion
of
wate
r
Res
oluti
on
Facilities A
(conceited
reluctance-hypoxia-metho
d)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
Facilities B
(circle method nitrification
denitrification method)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
ηNO3
KO2
Parameter
KO2
ηNO3
-1.26
-1.25
18%
88%
ηNO3
-0.51
3%
μAUT
bAUT
KO2
KNH4
4.26
-1.87
-0.68
-0.54
0%
0%
0%
0%
μAUT
bAUT
1.70
-0.74
iN,Xs
1.76
32%
iN,Xs
Inflo
w
Wat
er
quali
ty
SF
SA
XI
XS
XH
-0.02
-0.17
0.00
-0.77
0.16
0%
0%
0%
0%
0%
SF
SA
XI
XS
XH
0.03
0%
0.05
0%
Drivi
ng
Con
ditio
n
[Na],
[do]
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-0.80
1%
-2.91
11%
-0.50
0%
2.55
1%
XH
Rela
tion
XPAO
Rela
tion
XAUT
Rela
tion
Oth
ers
Other input data
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-1.50
-1.49
81%
25%
Facilities C
(conceited
reluctance-active sludge
process)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
Kh
-0.81
on
0%
ηfe
-0.62
0%
ηNO3
KO2
YH
bH
-5.74
-5.73
2.33
0.63
91%
91%
1%
1%
1%
1%
1%
1%
1%
2%
μAUT
KO2
bAUT
KNH4
KALK
YA
9.92
-2.13
-2.13
-1.21
-0.68
-0.53
0%
0%
0%
0%
0%
2%
3.45
-1.32
8%
8%
iN,Xs
iN,BM
fXI
5.07
-2.60
0.60
96%
96%
31%
-0.07
-0.42
0.00
-2.93
0.14
0%
0%
0%
0%
0%
SF
SA
XI
XS
XH
-0.13
-0.74
0.00
-4.30
0.05
43%
33%
0%
22%
43%
0.52
1%
0.23
0%
-0.01
0%
0.43
0%
4.87
0%
1.95
0%
-
-
-
-
-7.26
2%
-2.98
0%
17.38
2%
8.11
3%
KO2
ηNO3
-2.67
-2.62
5%
29%
YPAO
bPAO
-23.76
0.67
1%
0%
0%
0%
μAUT
bAUT
KO2
KNH4
KALK
YA
28.65
-9.73
-6.29
-2.07
-1.57
-0.67
1.76
36%
iN,Xs
iN,BM
-0.02
-0.16
0.00
-0.78
0.14
0%
1%
0%
1%
0%
SF
SA
XI
XS
XH
0.02
0%
0.02
0%
-0.95
3%
-2.97
12%
-0.28
0%
2.02
1%
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
Facilities D
(conventional activated
sludge process)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
Kh
-1.07
on
89%
KX
0.58
88%
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
Basic
condition
6.59 mg/L
6.65 mg/L
11.29 mg/L
16.01 mg/L
[Deno]
- density
The amount of the average relative change displays it, and the small one and the order display from 0.5 and the 16th place
following are not displaying the parameter.
429
Appendix 6Sensitivity analysis example
Table A6. 4Summary of sensitivity to SPO4 in reactive tank end
Analysis
object
....drinkin
g.. [ku]..
Addi
tion
of
wate
r
Res
oluti
on
XH
Rela
tion
Facilities A
(conceited
reluctance-hypoxia-metho
d)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
ηfe
-1.63
on
100%
Kh
-1.42
100%
KX
0.59
100%
Facilities B
(circle method nitrification
denitrification method)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
Kh
-1.66
on
99%
ηfe
-1.63
99%
ηNO3
-0.89
99%
KX
0.83
99%
Parameter
Other input data
YH
bH
-2.16
0.61
93%
96%
μH
KO2
0.67
-0.67
99%
73%
YPAO
KMAX
bPAO
qPP
YPO4
μPAO
qPHA
-3.58
-1.93
1.81
-1.37
1.07
0.95
-0.59
97%
100%
98%
100%
99%
100%
100%
YPAO
bPAO
qPHA
KMAX
qPP
KA
YPO4
-3.14
1.90
-1.39
-1.24
-0.78
0.77
0.71
98%
99%
99%
99%
99%
99%
99%
XAUT
Rela
tion
μAUT
0.75
1%
Oth
ers
iP,Xs
1.27
99%
iP,Xsi
N,Xs
1.33
0.63
Inflo
w
Wat
er
quali
ty
SF
SA
XI
XS
XH
-0.08
-0.44
0.00
-2.00
-0.08
99%
99%
0%
96%
99%
SF
SA
XI
XS
XH
-0.10
-0.56
0.00
-2.59
-0.02
Drivi
ng
Con
ditio
n
[Na],
[do]
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
XPAO
Rela
tion
0.12
72%
0.62
40%
1.13
70%
1.03
88%
-1.75
69%
0.72
7%
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
Facilities C
(conceited
reluctance-active sludge
process)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
Kh
-2.79
on
98%
ηfe
-2.78
98%
KX
1.21
99%
Facilities D
(conventional activated
sludge process)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
YH
bH
-1.95
0.53
44%
51%
YPAO
qPHA
bPAO
μPAO
qPP
KA
YPO4
KMAX
-4.89
-3.48
2.25
0.94
-1.20
1.16
1.13
-1.47
12%
100%
98%
100%
99%
99%
99%
99%
μAUT
bAUT
KO2
2.43
-1.83
-1.40
2%
11%
13%
99%
64%
iP,Xs
1.28
99%
iP,Xs
iP,BM
1.33
-0.77
99%
100%
95%
96%
0%
88%
95%
SF
SA
XI
XS
XH
-0.18
-0.87
0.00
-3.19
-0.27
99%
100%
0%
95%
99%
SF
SA
XI
XS
XH
0.01
-0.07
0.00
0.35
0.25
5%
14%
0%
7%
5%
0.03
0%
-0.03
0%
0.36
0%
0.08
22%
0.84
21%
0.39
7%
2.04
84%
-0.94
13%
1.10
5%
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
0.16
24%
0.92
63%
2.84
33%
-
-
-0.90
5%
2.56
9%
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-
-
-0.56
0%
-0.01
0%
Basic
condition
0.81 mg/L
1.76 mg/L
0.76 mg/L
2.30 mg/L
[Deno]
- density
The amount of the average relative change displays it, and the small one and the order display from 0.5 and the 16th place
following are not displaying the parameter.
430
Appendix 6Sensitivity analysis example
Table A6. 5Summary of sensitivity to XTSS in reactive tank end
Analysis
object
....drinkin
g.. [ku]..
Parameter
Addi
tion
of
wate
r
Res
oluti
on
XH
Rela
tion
Facilities A
(conceited
reluctance-hypoxia-metho
d)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
ηfe
on
235
0%
Facilities B
(circle method nitrification
denitrification method)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
ηfe
on
235
1%
Kh
215
1%
Facilities C
(conceited
reluctance-active sludge
process)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
YH
bH
2,230
-462
7%
4%
YH
bH
2,495
-525
95%
36%
YH
bH
YPAO
bPAO
KMAX
1,128
-414
243
1%
0%
0%
YPAO
bPAO
qPHA
773
-358
205
2%
1%
1%
Oth
ers
iTSS,XI
iTSS,BM
fXI
1,809
960
436
100%
100%
15%
iTSS,XI
iTSS,BM
fXI
1,826
996
435
Inflo
w
Wat
er
quali
ty
SF
SA
XI
XS
XH
33
134
1,062
1,492
449
1%
1%
100%
4%
1%
SF
SA
XI
XS
XH
36
154
1,075
1,620
457
Drivi
ng
Con
ditio
n
[Na],
[do]
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
XPAO
Rela
tion
1,180
-310
73%
4%
YPAO
366
0%
100%
100%
33%
iTSS,BM
iTSS,XI
796
564
100%
100%
5%
2%
100%
12%
5%
SF
SA
XI
XS
XH
21
86
393
835
254
0%
0%
100%
2%
0%
Facilities D
(conventional activated
sludge process)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
YH
bH
1,401
-327
54%
46%
iTSS,BM
iTSS,XI
928
543
100%
100%
SF
SA
XI
XS
XH
13
50
393
722
254
50%
17%
100%
71%
50%
XAUT
Rela
tion
Other input data
-50
1%
2,894
60%
1,454
8%
-154
0%
-2,216
8%
-97
0%
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-45
3%
2,808
79%
1,509
33%
-301
1%
-2,265
27%
-158
0%
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-25
0%
1,362
37%
593
0%
-
-
-1,110
2%
-95
0%
Basic
condition
3,080 mg/L
3,030 mg/L
1,460 mg/L
[Deno]
- density
The amount of the average relative change doesn't display the small one of the parameter from 200.
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-20
0%
1,444
98%
722
4%
-
-
-1,131
4%
4
0%
1,490 mg/L
431
Appendix 6Sensitivity analysis example
Table A6. 6Comparison of sensitivity to SNH4 in reactive tank end when condition of facilities A is
changed
Analysis
object
....drinkin
g.. [ku]..
Addi
tion
of
wate
r
Res
oluti
on
Facilities A
(conceited
reluctance-hypoxia-metho
d)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
XH
Rela
tion
Parameter
Other input data
YH
10.19
13%
YH
1.83
7%
YPAO
μPAO
qPP
YPO4
KMAX
bPAO
6.89
-2.35
2.24
-1.87
1.42
-1.40
12%
7%
6%
12%
3%
7%
μAUT
bAUT
KO2
KNH4
KALK
-25.08
10.09
4.17
2.13
1.15
99%
98%
98%
6%
98%
iP,Xs
-0.96
9%
SF
SA
XI
XS
XH
0.58
0.47
0.00
1.03
0.01
7%
5%
0%
4%
7%
-0.25
27%
-0.46
10%
-2.54
29%
-0.43
4%
3.68
17%
-5.51
81%
7%
7%
YPAO
μPAO
qPP
YPO4
KMAX
bPAO
44.10
-36.22
27.29
-13.34
4.85
-4.53
36%
67%
44%
34%
4%
6%
μAUT
bAUT
KO2
KNH4
KALK
-15.07
7.18
3.24
1.81
0.96
99%
99%
99%
99%
99%
μAUT
bAUT
KO2
KNH4
KALK
-53.51
38.95
17.91
4.80
3.80
95%
92%
86%
75%
76%
iP,Xs
-4.26
13%
SF
SA
XI
XS
XH
0.32
1.45
0.00
8.77
0.66
11%
9%
0%
7%
11%%
SF
SA
XI
XS
XH
Drivi
ng
Con
ditio
n
[Na],
[do]
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
Basic
condition
[Deno]
Reactor
End water
quality
ηfe
Kh
2%
Inflo
w
Wat
er
quali
ty
0.00
-0.02
0.00
0.06
0.00
0%
0%
0%
0%
0%
-0.11
27%
-0.04
0%
-0.93
21%
0.17
1%
1.52
24%
-3.76
91%
SNH4 :1.2,
SNO3 :6.6,SPO4 :0.8,
XI :2,370,XS :24, XH :680,
XPAO :330,
XPP :99XPHA :0.3,
XAUT :55,
XTSS :3,080
Condition A-4
(technical report
segmentation ratio)- 2
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
ηfe
on
1.07
5%
Kh
0.85
6%
Sensiti
vity
Uniqu
eness
0.75
Oth
ers
Condition A-3
(capacity of hypoxia
×60%)- 1
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
on
Avera
ge
Relativ
ity
Variati
on
6.12
5.31
Analys
is
Object
YPAO
XPAO
Rela
tion
XAUT
Rela
tion
Condition A-2
(ASRT:14d→9d)
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-0.98
36%
-2.00
15%
-13.02
56%
-3.59
9%
19.56
44%
-29.99
83%
SNH4 :3.1,
SNO3 :6.0,SPO4 :0.1,
XI :1,480,XS :23, XH :610,
XPAO :290,
XPP :77,XPHA :0.4,
XAUT :45,
XTSS :2,230
μAUT
bAUT
KO2
KNH4
SF
SA
XI
XS
XH
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-4.80
2.59
1.34
0.98
0.00
-0.02
0.00
-0.02
0.00
100%
100%
100%
100%
0%
1%
0%
0%
0%
-0.05
62%
0.01
0%
-0.39
48%
0.15
5%
0.68
53%
-1.50
94%
SNH4 :0.6,
SNO3 :6.7,SPO4 :1.5,
XI :2,420,XS :25, XH :880,
XPAO :230,
XPP :69,XPHA :0.1,
XAUT :64,
XTSS :3,110
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
SNH4 :1.4,
SNO3 :6.3,SPO4 :0.2,
XI :1,910,XS :24, XH :620,
XPAO :450,
XPP :122,XPHA :0.5,
XAUT :54,
XTSS :2,860
432
Appendix 6Sensitivity analysis example
- The amount of the average relative change displays it, and the small one and the order display from 0.5 and the 16th place
following are not displaying the parameter.
- 1 The capacity of the hypoxia tank of four divisions is reduced to 60% respectively.
- 2 The influent quality is set on the basis of the organism element composition ratio illustrated in the technical report of ASM2 (SF :27 mg/L, SA :18 mg/L, SI :27 mg/L, XI :23 mg/L, XS :113 mg/L, and
XH :27 mg/L; Total CODCr is 235 It is the same as other conditions in mg/L).
433
Appendix 6Sensitivity analysis example
Table A6. 7Comparison of sensitivity to SPO4 in reactive tank end when condition of facilities A is
changed
Analysis
object
....drinkin
g.. [ku]..
Addi
tion
of
wate
r
Res
oluti
on
XH
Rela
tion
Condition A-2
(ASRT:14d→9d)
Sensiti
vity
Uniqu
eness
ηfe
Kh
Avera
ge
Relativ
ity
Variati
-0.95
on
-0.83
Analys
is
Object
93%
93%
Condition A-3
(capacity of hypoxia
×60%)- 1
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
ηfe
-1.70
on
100%
Kh
-1.69
100%
KX
0.78
100%
ηNO3
-0.64
100%
Condition A-4
(technical report
segmentation ratio)- 2
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
ηfe
-0.59
on
95%
Parameter
Other input data
YH
bH
-2.16
0.61
93%
96%
YH
-1.12
86%
YH
μH
bH
-1.45
0.62
0.56
58%
100%
82%
YH
-0.89
92%
YPAO
KMAX
bPAO
qPP
YPO4
μPAO
qPHA
-3.58
-1.93
1.81
-1.37
1.07
0.95
-0.59
97%
100%
98%
100%
99%
100%
100%
YPAO
qPP
μPAO
KMAX
YPO4
bPAO
-2.53
-1.32
1.07
-1.06
0.81
0.78
63%
55%
31%
96%
66%
94%
YPAO
bPAO
KMAX
qPHA
qPP
YPO4
KA
-3.26
2.13
-1.46
-1.29
-0.97
0.82
0.65
98%
99%
100%
100%
100%
99%
100%
YPAO
qPP
μPAO
KMAX
YPO4
bPAO
-2.49
-1.17
1.14
-1.07
0.68
0.66
88%
94%
93%
97%
88%
93%
XAUT
Rela
tion
μAUT
0.75
1%
Oth
ers
iP,Xs
1.27
99%
iP,Xs
1.32
99%
Inflo
w
Wat
er
quali
ty
SF
SA
XI
XS
XH
-0.08
-0.44
0.00
-2.00
-0.08
99%
99%
0%
96%
99%
SF
SA
XI
XS
XH
-0.10
-0.53
0.00
-2.17
-0.01
97%
97%
0%
89%
97%
SF
SA
XI
XS
XH
-0.29
-0.27
0.00
-0.63
-0.02
93%
94%
0%
94%
93%
0.05
73%
-0.03
2%
Drivi
ng
Con
ditio
n
[Na],
[do]
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
0.52
69%
0.26
88%
-1.07
82%
0.34
18%
XPAO
Rela
tion
Basic
condition
[Deno]
Reactor
End water
quality
-
Facilities A
(conceited
reluctance-hypoxia-metho
d)
Avera
Sensiti
Analys
ge
vity
is
Relativ
Uniqu
Object
ity
eness
Variati
ηfe
-1.63
on
100%
Kh
-1.42
100%
KX
0.59
100%
0.12
72%
0.62
40%
1.13
70%
1.03
88%
-1.75
69%
0.72
7%
SNH4 :1.2,
SNO3 :6.6,SPO4 :0.8,
XI :2,370,XS :24, XH :680,
XPAO :330,
XPP :99XPHA :0.3,
XAUT :55,
XTSS :3,080
SF
SA
XI
XS
XH
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
-0.04
-0.20
0.00
-1.43
-0.08
88%
90%
0%
93%
88%
0.06
63%
0.16
51%
0.49
42%
0.50
91%
-0.94
54%
0.55
15%
SNH4 :3.1,
SNO3 :6.0,SPO4 :0.1,
XI :1,480,XS :23, XH :610,
XPAO :290,
XPP :77,XPHA :0.4,
XAUT :45,
XTSS :2,230
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
0.09
36%
0.80
25%
0.65
22%
0.65
79%
-1.07
23%
0.83
5%
SNH4 :0.6,
SNO3 :6.7,SPO4 :1.5,
XI :2,420,XS :25, XH :880,
XPAO :230,
XPP :69,XPHA :0.1,
XAUT :64,
XTSS :3,110
Water
tempe
rature
Inlet
flow
Sendi
ng
back
ratio
Circul
ation
ratio
..pullin
g out..
[haka]
DO
SNH4 :1.4,
SNO3 :6.3,SPO4 :0.2,
XI :1,910,XS :24, XH :620,
XPAO :450,
XPP :122,XPHA :0.5,
XAUT :54,
XTSS :2,860
The amount of the average relative change displays it, and the small one and the order display from 0.5 and the 16th place
434
Appendix 6Sensitivity analysis example
following are not displaying the parameter.
- 1 The capacity of the hypoxia tank of four divisions is reduced to 60% respectively.
- 2 The influent quality is set on the basis of the organism element composition ratio illustrated in the technical report of ASM2 (SF :27 mg/L, SA :18 mg/L, SI :27 mg/L, XI :23 mg/L, XS :113 mg/L, and
XH :27 mg/L; Total CODCr is 235 It is the same as other conditions in mg/L).
435
Appendix 6Sensitivity analysis example
３．Detailed sensitivity analysis on parameter according to biological phosphorus
removal
（１）Outline
A lot of processes and parameters are used for the biological phosphorus removal model
of ASM2d. It is easy to be going to spend the labor that number of degrees of freedom at
the calibration is high and large because a lot of parameters have a big influence from
the sensitivity analysis result of propodus on the simulation result like clearness.
There is the interaction between complex processes, and the behavior of the simulation
result when parameter value is changed is and is intuitively a part not understood
easily either there is a competition over organic substrate (XPHA ) like "Store of XPP"
and process that XPAO bears "Proliferation of XPAO", etc.
The parameter involved in the simulation result of the biological phosphorus removal
from the result of review of propodus is extracted, and it introduces the result of
discussing the influence more in detail from such circumstances here.
（２）Review method
Object facilities and process model
(..using
facilities
parameter
and
process
model
of
facilities
A
(conceited
reluctance-hypoxia-method) used by discussing propodus..Table A6. 1)｡
Object parameter and width of change
The object was selected centering on the parameter that comparatively showed big
sensitivity to the simulation result related to the phosphorus removal by discussing
propodus. However, the possibility of changing it by the process of the calibration such
as YPAO excluded the small one and the conversion factor.
The simulation was executed within each parameter value-..individual.. range of
80%-+100% at intervals of 10% on the condition of changing it, and each stationary
solution (element density after it had simulated it ..between 90d..) was used for the
analysis and the evaluation.
The calculation way in the simulation is identical with the discussion about propodus.
（３）Result of review
The appearance of the change in SPO4 in the SPO4 density of the SPO4 density of the
(a) reaction tank each division when each parameter value is changed (PO4-P profile)
436
Appendix 6Sensitivity analysis example
and the (b) reluctance tank and a conceited tank ends, XPAO, and the XH densityFigure
A6. 2～Figure A6. 14[Ni] was shown.
Appendix 6Sensitivity analysis example
437
Behavior of anoxic
・ The parameter concerning the store process of the parameter concerning
hydrolysis involved in the substrate supply to XPAO there (Kh, KX, and ηfe, etc.)
and XPAO (qPHA and YPO4, etc.) greatly influences Rin's discharge under the
anoxic.
・ It comparatively resembles it, and the phosphorus removal is shown and the
method of the influence shows the promotion or the effect of depraving it overall
though parameters except YPO4 are different among these the size of each
influence it. For instance, (..increasing SPO4 burst size under the anoxic when it
is changed into the direction in which it promotes it that these phosphorus
removals (Kh, ηfe, increase of qPHA, and decrease of KX).. ..the SPO4
incorporation under aerobic condition hypoxia/.. ..the promotion.. ..the final
reduction of the SPO4 density of the treated water... Figure A6. 2～Figure A6. 5)｡
This increases the amount of the organism for which XPAO can be used, and is
corresponding (Here, the competition between XPAO and XH over the organic
substrate is important) the growth yield of XPAO (XPAO density) is increased.
・ SA and the density of SF of influent grow in the condition of this discussion and
the contribution of the hydrolysis process is growing relatively compared with the
substrate supply in the small straightening and the anoxic. Moreover, it is
hydrolyzed before it arrives at a conceited tank, and the increase and decrease of
the hydrolysis rate by the anoxic by the increase and decrease of the speed of the
entire hydrolysis process by Kh and ηfe is the results of it having a roughly equal
influence in the majority of XS of the influent origin.
・ ..density of SF of influent.. (of ..parameter's concerning the small straightening
and the fermentation process (qfe etc.) influence.. small in the condition of this
discussion. Figure A6. 6)｡
・ YPO4 changes the amount of the phosphorus release for each organic substrate uptake by
XPAO, and doesn't influence direct in the uptake of the organic substrate. Therefore, it
influences SPO4 burst size under the anoxic in the peculiarity comparatively. (..the influence
on the XPAO density and the amount of the endogenous substrate.. ..moving of the SPO4
profile of a small straightening and a reactive tank comparatively overall up and down...
Figure A6. 13)｡
・ Because the above-mentioned parameter influences the entire SPO4 profile of a
reactive tank, the good policy in the calibration doing the fitting of the anoxic
before hypoxia/aerobic condition.
438
Appendix 6Sensitivity analysis example
Behavior of hypoxia/aerobic condition
・ As the behavior of XPAO, under aerobic condition hypoxia/the store and the
proliferation process of XPP are subjects. The parameter involved in these
processes doesn't influence the behavior of SPO4 under the anoxic too much.
・ Both processes are in the relation that competes over intracellular store organism
XPHA of XPAO. When the speed of either of process is changed because XPHA
often becomes the limiting factor of both processes in the vicinity of the reactive
tank end, the other process is influenced, too. In addition, an interaction complex
as the amount of XPAO changing when the speed of the proliferation process
changes influences the speed of both processes is caused. However, it is easy to
understand it usually pays attention to SPO4 because the variable ratio of a
function of change of SPO4 is larger than the variable ratio of a function of
change of XPAO and the calibration is done (The case where the amount of
autolysis of XPAO is changed by bPAO is excluded).
・ The parameter change to promote this because the subject of the incorporation of SPO4 is a
store process of XPP operates in the direction where SPO4 of the treated water is basically
reduced. (..treatment as high and calibration ..sensitivity.. object for qPP to change speed of
this process directly among these easily..Figure A6. 7)｡
・ Because the speed of this process decreases as the XPP /XPAO ratio rises and it
approaches KMAX, (..large in the uptake situation of SPO4 KMAX, too.. ..the
influence... Figure A6. 10)｡However, you should not change greatly, except when
the contribution of GAO is incorporated by parameter value because the default
value of this parameter is comparatively thought to be a parameter with high
generality by descending on the basis of the measurement data.
・ μPAO comparatively shows big sensitivity, and on the other hand, the amount of
XPAO in the faction increases simply even when this is increased from the
interaction between the above-mentioned processes and the SPO4 density of the
treated water doesn't decrease as a parameter involved in the proliferation of
XPAO. Figure A6. 8In ..drinking.. [rei], as the influence when μPAO is improved,
XPHA in this tank latter part becomes the limiting factor of the XPP store process,
and XPHA is a result of the SPO4 density's rising in front of a conceited tank that
remains comparatively though the SPO4 uptake rate increases in steps though it
is an increase of XPAO finally.
・ Because bPAO changes the autolysis speed of XPAO, the amount of XPAO is changed without
changing the competition between the above-mentioned two processes directly. Therefore,
(..reducing this parameter.. ..the SPO4 density of the treated water when the amount is
439
Appendix 6Sensitivity analysis example
increased XPAO (Differ from μPAO).. ..the change into the decreasing direction... Figure A6.
9)｡
・ ηNO3 and P are typical as the parameter that influences the behavior of XPAO
under the anoxic condition. However, small (..the exclusion of the denitrification
actually of the progressing first division in hypoxia tank because of there is room
in the capacity of the hypoxia tank in the condition of this discussion.. ..it is the
impact combination of that... Figure A6. 12)｡
16
2.5
-40%
-20%
default
+20%
+40%
14
12
[mg/L]
10
PO4
S
8
6
4
XPAO
2.0
XH
1.5
PO4_eff
1.0
Element variable ratio of a function of change
0.5
PO4_ana
0.0
2
-0.5
0
1
2
3
4
5
6
7
8
9
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
Reactive tank division
ParameterVariable ratio of a function of change
(a) SPO4 profile
(b) Variable ratio of a function of change of element
Figure A6. 2Sensitivity analysis result according to phosphorus removal (Analysis object: Kh)
16
3.0
-40%
-20%
default
+20%
+40%
14
SPO4
12[mg/L]
10
8
6
XPAO
XH
PO4_eff
PO4_ana
2.5
2.0
1.5
Element
1.0 variable ratio of a function of change
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
Reactive tank division
(a) SPO4 profile
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
ParameterVariable ratio of a function of change
(b) Variable ratio of a function of change of element
Figure A6. 3Sensitivity analysis result according to phosphorus removal (Analysis object: KX)
440
Appendix 6Sensitivity analysis example
16
3.0
-40%
-20%
default
+20%
+40%
14
12
10
8
PO
Element
1.0 variable ratio of a function of change
0.5
2
0.0
-0.5 0.2
0
2
3
4
5
6
7
8
PO4_eff
PO4_ana
1.5
4
1
XH
2.0
6
S
XPAO
2.5
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
Reactive tank division
ParameterVariable ratio of a function of change
(a) SPO4 profile
(b) Variable ratio of a function of change of element
Figure A6. 4Sensitivity analysis result according to phosphorus removal (Analysis object: ηfe)
16
3.0
-40%
-20%
default
+20%
+40%
14
12
[mg/L]
10
PO4
8
XPAO
XH
PO4_eff
PO4_ana
2.5
2.0
1.5
Element
1.0 variable ratio of a function of change
6
S
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
ParameterVariable ratio of a function of change
Reactive tank division
(a) SPO4 profile
(b) Variable ratio of a function of change of element
Figure A6. 5Sensitivity analysis result according to phosphorus removal (Analysis object: QPHA)
16
3.0
14
-40%
-20%
default
+20%
+40%
12
[mg/L]
10
PO4
S
8
6
XPAO
XH
PO4_eff
PO4_ana
2.5
2.0
1.5
1.0 variable ratio of a function of change
Element
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
Reactive tank division
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
ParameterVariable ratio of a function of change
441
Appendix 6Sensitivity analysis example
(a) SPO4 profile
(b) Variable ratio of a function of change of element
Figure A6. 6Sensitivity analysis result according to phosphorus removal (Analysis object: Qfe)
-40%
-20%
default
+20%
+40%
16
14
12
[mg/L]
10
PO4
S
3.0
XPAO
XH
PO4_eff
PO4_ana
2.5
2.0
1.5
8
Element
1.0 variable ratio of a function of change
6
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
ParameterVariable ratio of a function of change
Reactive tank division
(a) SPO4 profile
(b) Variable ratio of a function of change of element
Figure A6. 7Sensitivity analysis result according to phosphorus removal (Analysis object: QPP)
16
-40%
-20%
default
+20%
+40%
14
SPO4
12 [mg/L]
10
8
XPAO
XH
PO4_eff
PO4_ana
3.0
2.5
2.0
1.5
Element
1.0 variable ratio of a function of change
6
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
Reactive tank division
(a) SPO4 profile
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
ParameterVariable ratio of a function of change
(b) Variable ratio of a function of change of element
Figure A6. 8Sensitivity analysis result according to phosphorus removal (Analysis object: μPAO)
442
Appendix 6Sensitivity analysis example
16
3.0
-40%
-20%
default
+20%
+40%
14
12
10
8
PO4
XPAO
XH
PO4_eff
PO4_ana
2.5
2.0
1.5
Element
1.0 variable ratio of a function of change
6
S
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
ParameterVariable ratio of a function of change
Reactive tank division
(a) SPO4 profile
(b) Variable ratio of a function of change of element
Figure A6. 9Sensitivity analysis result according to phosphorus removal (Analysis object: BPAO)
16
3.0
-40%
-20%
default
+20%
+40%
14
12
[mg/L]
10
PO4
S
8
6
XPAO
XH
PO4_eff
PO4_ana
2.5
2.0
1.5
Element
1.0 variable ratio of a function of change
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
Reactive tank division
(a) SPO4 profile
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
ParameterVariable ratio of a function of change
(b) Variable ratio of a function of change of element
Figure A6. 10Sensitivity analysis result according to phosphorus removal (Analysis object:
KMAX)
443
Appendix 6Sensitivity analysis example
16
3.0
-40%
-20%
default
+20%
+40%
14
12
[mg/L]
10
8
PO4
6
S
XPAO
XH
PO4_eff
PO4_ana
2.5
2.0
1.5
Element
1.0 variable ratio of a function of change
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
Reactive tank division
ParameterVariable ratio of a function of change
(a) SPO4 profile
(b) Variable ratio of a function of change of element
Figure A6. 11Sensitivity analysis result according to phosphorus removal (Analysis object: KPHA)
16
3.0
14
12
[mg/L]
10
PO4
S
8
-40%
-20%
default
2.5
+20%
+40%
1.5
XPAO
XH
PO4_eff
PO4_ana
2.0
1.0 variable ratio of a function of change
Element
6
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
Reactive tank division
(a) SPO4 profile
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
ParameterVariable ratio of a function of change
(b) Variable ratio of a function of change of element
Figure A6. 12Sensitivity analysis result according to phosphorus removal (Analysis object: ηNO3
and P)
444
Appendix 6Sensitivity analysis example
16
3.5
-40%
-20%
default
+20%
+40%
14
SPO4
12[mg/L]
10
8
6
2.5
2.0
1.5
Element variable ratio of a function of change
1.0
4
0.5
2
0.0
-0.5 0.2
0
1
2
3
4
5
6
7
8
9
XPAO
XH
PO4_eff
PO4_ana
3.0
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
Reactive tank division
ParameterVariable ratio of a function of change
(a) SPO4 profile
(b) Variable ratio of a function of change of element
Figure A6. 13Sensitivity analysis result according to phosphorus removal (Analysis object: YPO4)
16
3.0
14
-40%
-20%
default
+20%
+40%
12
[mg/L]
10
PO4
S
8
XPAO
XH
PO4_eff
PO4_ana
2.5
2.0
1.5
1.0 variable ratio of a function of change
Element
6
0.5
4
0.0
2
-0.5 0.2
0
1
2
3
4
5
6
7
Reactive tank division
(a) SPO4 profile
8
9
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-1.0
ParameterVariable ratio of a function of change
(b) Variable ratio of a function of change of element
Figure A6. 14Sensitivity analysis result according to phosphorus removal (Analysis object: YPHA)
445
Appendix 6Sensitivity analysis example
４．Sensitivity analysis on time varying of inflow loadError! Reference source not found.
（１）Outline
The influent quantity and the water quality have changed timewise in the sewage plant
of the reality, and the point even where should reflect this in the simulation is an
important concern that controls necessary data of the simulation. It is evident that the
influent quantity and the water quality data in equal Thymus vulgaris span are at least
needed when it is necessary to expect the water quality variation at the time level.
However, if the water quality at the average level of the day is predictable according to
the simulation purpose of use and method of the calibration, it is enough. For that case,
it is important in the business for the inflow condition data at the time level to be
needed still or to understand the point enough if the inflow load at the average level of
the day is understood.
Then, it introduces the result of examining the influence that the size of the time
varying of the influent quantity and the water quality gives the simulation result in this
discussion for virtual reluctance-hypoxia-conceited method facilities.
（２）Review method
Object facilities and process model
(..using
facilities
parameter
and
process
model
of
facilities
A
(conceited
reluctance-hypoxia-method) used by discussion to propodus..Table A6. 1)｡
How to give influent quantity and water quality variation
Varying conditions of the influent quantity and the water quality in a day were set by
the following policies.
・ Influent quantity and the water quality is mean both (of the day. Table A6. 1The
case where the time varying is not given was assumed to be "Basic condition" by
using). This is "Error! Reference source not found.Error! Reference source not
found.It is identical with the basic condition of this facilities in".
・ Various conditions of giving the time varying of the volume of water and the
water quality within the range of 1.2-3.0 compared with the change were set to
the basic condition. "Change ratio" was defined here as a ratio of the day maxima
and the day mean.
・ The given variability patternFigure A6. 15It is as [ni] is illustrated. In the one
446
Appendix 6Sensitivity analysis example
that a typical change of the amount of influent in the sewage plant in our country
was assumed, it has the peak in two places (the morning and nighttime).
・ All conditions made a total inlet flow each day of influent and each water quality
"Basic condition" and identical. This means the flow rate weighted average value
during a day is equal of each water quality.
447
Appendix 6Sensitivity analysis example
・ The same one was used for the variability pattern of the volume of water and the
water quality. This reflected seeing the tendency that each element density in
influent rises, too in an actual sewage plant at time zone with a lot of influent
quantities.
・ When the water quality variation was given, the same variability pattern of all
components except biomass element (XPAO ,XAUT ) and alkalinity (SALK ) other
than XH was given.
・ When the time varying is given to the influent quantity, the sending back dirt
flow rate has been changed in proportion to the influent quantity in consideration
of a general operating condition of a real sewage plant. On the other hand, it
pulled out and the constancy of the internal circulation flow rate and dirt
regardless of the change of the inflow load was other operating conditions like the
DO density etc. of a flow rate and a conceited tank.
Calculation condition
As for the calculation condition in the simulation, it is identical with the discussion to
propodus having given the time varying to the inflow condition and the sending back
dirt flow rate.
変動比＝１．０
3
変動比＝１．４
対日平均比 [-]
変動比＝２．０
変動比＝３．０
2
1
0
0
6
12
18
24
Time [h]
Figure A6. 15Time varying pattern of influent quantity and water quality (example of 1.4, 2.0, and
change ratio =3.0)
448
Appendix 6Sensitivity analysis example
（３）Result of review
Influence on nitrification
The influence that the time varying of the influent quantity and the water quality gives
to the simulation result of the SNH4 density of the treated water (reactive tank end
division)(day mean)Figure A6. 16[Ni] was shown.
・ The SNH4 density of the treated water increases as the change of the influent
quantity grows. However, the influence is extent in the condition that the change
ratio falls below 2.0 that can be disregarded.
・ The tendency to the change of the influent quality similar to the volume of water
is seen. Moreover, the size of the influence of the water quality variation is equal
to the case to give the same change ratio by the volume of water as long as it sees
in the SNH4 density of the treated water.
・ The influence of both is added when the change is given to both the volume of
water and the water quality, and the SNH4 density increases further.
・ When the time varying of the density of treated water SNH4 is seen, (Figure A6.
18Volume of water..water quality..change..give..heavy duty..time zone..inflow..as it
is..unexpectedly
defeat..expected
system
effluent
concentration..appear..day..average..water
quality..improve..understand..natural..treated
water..remain..reaction..tank..density..decrease.Figure A6. 17)｡This suggests that
there not be qualitative difference under the influence that the change of the
volume of water and the water quality gives.
・ Figure A6. 18As for SNH4 of the treated water, the same behavior as the case to
make other element densities change also, too is shown, and it is understood that
SNH4 is a subject as the factor affecting of the above-mentioned water quality
variation though the simulation result that gives the time varying only to SNH4
of the [niha] influent is shown, too.
449
Appendix 6Sensitivity analysis example
4
There is no water quality variation.
Water quality change ratio: 1.4
Water quality change ratio: 2.0
Water quality change ratio: 3.0
3
NH4
S
2
Treated water
1
0
1.0
1.5
2.0
2.5
3.0
[-] change ratio
Volume of water
Figure A6. 16Influence of influent quantity and water quality variation on SNH4 density of treated
water (day mean)
450
Appendix 6Sensitivity analysis example
60
50
[mg/L]
40
AUT
X
30
20
There is no water quality variation.
Water quality change ratio: 1.4
10
Water quality change ratio: 2.0
Water quality change ratio: 3.0
0
1.0
1.5
2.0
2.5
3.0
[-] change ratio
Volume of water
Figure A6. 17Influence of influent quantity and water quality variation on XAUT density of reactive
tank
7
4
6
[mg/L]
NH4
Only the volume(3.0)
of water change
5
3
4
(3.0) variation
Only the water quality
2 Variability pattern (average toward Japan ratio)
There is no change.
S
3
Treated
2 water
1
1
SNH4Only the change
(3.0)
Variability pattern
0
0
0
6
12
18
24
Time [h]
Figure A6. 18Time varying of density of treated water SNH4 when volume of water and water
quality variation are given
Influence on denitrification
The influence that the time varying of the influent quantity and the water quality gives
to the simulation result of the SNO3 density of the treated water (day mean)Figure A6.
19[Ni] was shown.
・ The influence of the change of both the volume of water and the water quality is
not so seen about the SNO3 density of the treated water.
・ But..conceited..tank..nitrification..formation..change..internal
Appendix 6Sensitivity analysis example
circulation..ratio..change..this..discussion..internal
rate..constant..assume..variation
in
451
circulation..flow
flows..give..condition..internal
circulation..ratio..time..change..conceited..tank..steps..denitrification..amount..ch
ange..hydrolysis..amount..change..originate..interact..result. It doesn't mean the
inflow workload fluctuation doesn't influence the simulation result concerning the
denitrification.
452
Appendix 6Sensitivity analysis example
8
7
[mg/L]
6
NO3
S
5
4
3
Treated
2
There is no water quality variation.
Water quality change 1.4
1
Water quality change 2.0
Water quality change 3.0
0
1.0
1.5
2.0
2.5
3.0
[-] change ratio
Volume of water
Figure A6. 19Influence of influent quantity and water quality variation on SNO3 density of treated
water (day mean)
800
700
600
[mg/L]
500
400
XH 300
200
There is no water quality variation.
Water quality change 1.4
100
Water quality change 2.0
Water quality change 3.0
0
1.0
1.5
2.0
2.5
3.0
[-] change ratio
Volume of water
Figure A6. 20Influence of influent quantity and water quality variation on XH density of reactive
tank
453
Appendix 6Sensitivity analysis example
10
4
[mg/L]8
3
Only the volume(3.0)
of water change
NO3
6
Japan
(3.0) ratio)
Only thetoward
water quality
variation
2 Variability pattern (average
S
4
Treated
2
There is no change.
1
0
Variability pattern
0
0
6
12
18
24
Time [h]
Figure A6. 21Time varying of density of treated water SNO3 when volume of water and water
quality variation are given
454
Appendix 6Sensitivity analysis example
・ Actually, when the time varying of the SNO3 density of the treated water is seen,
(..seeing the clear variability pattern... Figure A6. 21)｡
・ ..XH density of reactive tank.. ..remarkable influence of inflow workload
fluctuation.. (..not seeing... Figure A6. 20)｡
Influence on phosphorus removal
The influence that the time varying of the influent quantity and the water quality gives
to the simulation result of the SPO4 density of the treated water and the reluctance
tank (day mean)Figure A6. 22､Figure A6. 23[Ni] was shown.
・ The SPO4 density of the reluctance tank and the treated water shows different
behavior to the time varying of the influent quantity and the water quality. In the
same change ratio, it is larger the impact combination about the influent quantity
overwhelmingly.
・ The tendency to which SPO4 burst size in the reluctance tank increases when the
time varying of the influent quantity grows and the SPO4 density of the treated
water decreases is seen.
・ SPO4 burst size of the reluctance tank decreases and the SPO4 density of the
treated water increases for the time varying of the influent quality.
・ The change of the amount of the inflow organic loading is an important affector,
and SPO4 burst size of the reluctance tank increases for the change of the
influent quality by the time zone of the heavy duty to which the organic loading
increases.
・ On the other hand, it is necessary to consider the change of real HRT of a reactive
tank in addition to this for the change of the influent quantity. The amount of the
inflow organic loading becomes the result of the counterbalance of both each other
because real HRT of the reluctance tank shortens while it is large at the time
zone of the heavy duty. When the time varying situation of the SPO4 density of
the reluctance tank is seen, (in the condition with a large change of the amount of
influent ..long real HRT of the reluctance tank.. ..the understanding of the SPO4
discharge by time zone of the becoming it low load excellence... Figure A6. 26)｡
・ Because the hypoxia tank latter part contributes to the biological phosphorus
removal as a reluctance tank in the facilities condition used by this discussion
besides the behavior of such a reluctance tank, the residual situation of SNO3 in
the hypoxia tank also influences.
Appendix 6Sensitivity analysis example
455
456
Appendix 6Sensitivity analysis example
1.0
[mg/L]
0.8
PO4
0.6
S
0.4
Treated
0.2
There is no water quality Water
variation.
quality change 1.4
Water quality change 2.0 Water quality change 3.0
0.0
1.0
1.5
2.0
2.5
3.0
[-] change ratio
Volume of water
Figure A6. 22Influence of influent quantity and water quality variation on SPO4 density of treated
water (day mean)
16
[mg/L]
14
12
PO4
S
10
8
6
Reluctance tank
4
There is no water quality Water
variation.
quality change 1.4
2
Water quality change 2.0 Water quality change 3.0
0
1.0
1.5
2.0
2.5
3.0
[-] change ratio
Volume of water
Figure A6. 23Influence of influent quantity and water quality variation on SPO4 density of
reluctance tank (day mean)
457
Appendix 6Sensitivity analysis example
400
350
300
[mg/L]
250
PAO
X
200
150
100
There is no water quality Water
variation.
quality change 1.4
50
Water quality change 2.0 Water quality change 3.0
0
1.0
1.5
2.0
2.5
3.0
[-] change ratio
Volume of water
Figure A6. 24Influence of influent quantity and water quality variation on XPAO density of reactive
tank
4
4
[mg/L]
3
3
PO4
S
Only the volume(3.0)
of water change
(3.0) ratio)
Japan
Only thetoward
water quality
variation
2 Variability pattern (average
2
There is no change.
Treated water
1
1
0
Variability pattern
0
0
6
12
18
24
Time [h]
Figure A6. 25Time varying of density of treated water SPO4 when volume of water and water
quality variation are given
40
4
[mg/L]
35
30
PO4
S
3
25
Only the volume(3.0)
of water change
(3.0)
Japan variation
ratio)
Only the toward
water quality
2 Variability pattern (average
20
15 tank
Reluctance
10
There is no change.
1
5
0
0
0
6
12
Time [h]
18
24
Variability pattern
458
Appendix 6Sensitivity analysis example
Figure A6. 26Time varying of density of reluctance tank SPO4 when volume of water and water
quality variation are given
Appendix 6Sensitivity analysis example
459
Summary of influence on treated effluent quality
Figure A6. 27As for the change of both the influent quantity and the water quality, in
the range where the change ratio falls below 2.0, treated effluent quality
(SNH4 ,SNO3 ,SPO4 ) is [ni] equal to the basic condition in the facilities condition used
by this discussion as shown (That is, there is no big influence in the simulation result
even if the average volume of water of the day and the water quality are used as input
data). However, because the acquisition of the time varying data is easy, the influent
quantity can be said it should the reflection in the simulation regardless of the size of
the change. On the other hand, after the change realities are confirmed (If it is possible,
the evaluating impact like this discussion is executed), it is necessary to decide the use
data though it seems that you may often use the data of the day average about the
influent quality because the influence equal with the volume of water change may be
given to the simulation result of nitrification about the time varying of the density of the
ammoniacal nitrogen.
3
[-]
(a) There is no volume of water change.
2
1
Variable
3 ratio of a function of change to basic condition
(b) Volume of water
2.0change ratio =
2
1
0
NH4-N
NO3-N
Water quality change
It is ratio
not. 1.4
PO4-P
2.0
3.0
Figure A6. 27Variable ratio of a function of change of average treated effluent quality of day when
influent quantity and water quality variation are given
460
Appendix 6Sensitivity analysis example
Bibliography
6A)
Hiroshi Itokawa [**] and Takao Murakami: Response of activated sludge model to a variety of
inflow workload fluctuations, the 42nd drainage research symposium lecture collections, and
pp.112-114,2005.
6B)
Japan Sewage Works Association: Japan Sewage Works Association and sewerage facilities
plan, design principle, and explanation-2001 edition -2001.
6C)
Brun,R., Kuhni,M., Siegrist,H. Gujer,W. and Reichert,P.: Practical identifiability of ASM2d
parameters- systematic selection and tuning of parameter subsets. Wat.Res., Vol.36,
pp.4113-4127, 2002.
6D)
Reichert,P.: AQUASIM- a tool for simulation and data analysis of aquatic systems.
Wat.Sci.Tech., Vol.30, No.2, pp.21-30, 1994.
Appendix 7Example of oxidation ditch process model development
461
Appendix７Discussion case with process model of oxidation ditch
method
１．Outline
When the simulation by the activated sludge model is attempted in our country for
[okishide-shondei;cchi] (OD) method with a lot of introduction results, the method for
constructions of the following process models become problems.
・ The inclination of DO and the density of dirt might be caused in the direction of
depth and the direction of the width of the waterway according to the kind, the
ability, and the operational method of the diffused air and the stirrer though a
reactive tank essentially has problem data conversion of the plug flow type. It is
necessary to discuss an appropriate modeling methodology of problem data
conversion in the tank.
・ The influent quantity is done and driving that installs the process of decreasing
the output of the diffuser (cycle) is done generally centering on the case where the
oxygen supply ability of the diffuser becomes relatively excessive small for the
process capability and in the case of, etc. to do the denitrification. The flow rate in
the tank will change according to the change in the output when the diffuser
bears the stir. Moreover, influent and the sending back dirt will flow in while
dirt's the process of completely stopping all diffusers according to facilities might
be incorporated, and it having subsided to the belt during that time in the tank.
Thus, it is necessary to discuss the process model that is expressible of the
amount of the oxygen supply corresponding to the operational method of the
diffuser and the change in the flow rate in the tank.
・ In the regular processing method, it is 3,000-5,000 as for the MLSS density of a
reactive tank Setting it higher than the active sludge process is usual about mg/L
and usually. Therefore, it is designed so that the scale of the final sedimentation
tank may grow. Therefore, it can be said that the dwell time of the supernatant
fluid water and dirt in the final sedimentation tank becomes long, and the
possibility that the biological response there gives the influence that cannot be
disregarded to the behavior of the entire processing process is higher than other
processing mode. An appropriate modeling methodology to expect the biological
462
Appendix 7Example of oxidation ditch process model development
response in the final sedimentation tank is necessary.
One side, and in the OD method, the diversitys of the kind and the spec etc. of the shape
of a reactive tank and the final sedimentation tank to the process capability, parameters,
and diffusers are remarkable and are the smalls one of the most advanced methods in
various processing mode adopted in our country by standardization. This means the
possibility that the process model constructed for a certain facilities can be generally
used is high.
It introduces the case that discusses each element of the process model of the OD
method from such an aspect here on the basis of the data that collects chiefly in real
facilities. It is thought that the process model found here is useful to provide the
illustrative example of the process model construction work besides use as the basic
plan when other similar facilities are modeled can be expected. Here, OD method
facilities (of the horseshoe type to which two spindle type diffusers with most numbers
of adoptions in our country are distributed. Figure A7. 1) was made the main object.
・ Modeling methodology of reactive tank
・ Modeling methodology of oxygen supply corresponding to change in driving
strength of diffuser
・ Modeling methodology of flow rate in reactive tank corresponding to change in
driving strength of diffuser
・ Modeling methodology of dirt subsidence when diffuser is stopped
Appendix 7Example of oxidation ditch process model development
463
[Okishide-shondei;cchi]
( OD)
Treated water
Finality
Sedimentation pond
Influent
(crude sewage)
Excess sludge
Sending back dirt
Spindle type diffuser
Figure A7. 1Reactive tank shape and facilities composition of OD method facilities made object of
process model discussion
464
Appendix 7Example of oxidation ditch process model development
２．Discussion about modeling methodology of reactive tank
（１）Outline
It is general to be realistic not to model as a plug flow type reactor no numerical
calculation, to describe this according to the complete mixing [souretsu] model, for the
mixture to circulate to the foremost tank from the final tank, and to express the no
terminal waterway though problem data conversion in OD is extremely near the plug
flow as already stated. Because computing time increases according to it, it is ideal to
set minimum number of tanks within the range where it doesn't influence the prediction
result though it becomes problem data conversion near the plug flow to increase the
number of tanks that composes the model. However, a necessary number of tanks is
various to the one for the number of tanks that is expected the difference depending on
the kind, the output, the number, arrangement, and the loading condition etc. of
capacity and the diffuser of a reactive tank, and used actually in the case where OD of
the full scale is modeled in foreign countries to exceed 20 tanks from one tank. 7B)
～7K)
｡
An enough discussion case is about the OD method facilities in our country, and on the
other hand, the majority of a no bur and the above-mentioned facilities condition are
standardized and there is a possibility that a generality and high model can
comparatively be found because facilities that are designed and driven on the basis of
this are a lot of. Then, the modeling methodology of OD was discussed for typical OD
method facilities in our country. Because the generation situation of the water quality in
OD and the density gradient such as dirt was investigated before this, it also introduces
the result.
（２）Data collection
It is necessary to understand how to cause the density gradient of the solid and the
water quality in the tank when modeling OD is discussed. For instance, it is extent to be
able to be necessary the model who is appropriately expressible of it, and to disregard
the solid in the tank and the density gradient of the water quality if the area where the
DO density is low is caused by a part of division in a reactive tank, and the entire tank
can be considered to be one complete mixing tank in the aerification worker degree.
Then, the generation situations of the density gradients such as solids and DO were
investigated for the real OD method facilities under operation.
Density gradient investigation of DO and solid in waterway section
465
Appendix 7Example of oxidation ditch process model development
DO in the waterway section in each place in OD and the distribution of the solid
concentration were investigated for the real OD method facilities under operation.
< object facilities >
The outline of the object facilities is as follows.
‒ Reactive tank shape:Horseshoe type
‒ Capacity of reactive tank: 1,650 m3
‒ Diffuser
form
kilowatt-hour)×2
and
the
number:Spindle
type
(Declared
power:
15
466
Appendix 7Example of oxidation ditch process model development
‒ Diffuser ratings oxygen supply ability (per one): 599 kgO2/d
< search procedure and content >
Five-place (A-E in the OD tank by three conditions to change the operational method of
two diffusers;Figure A7. 2DO and the solid concentration were measured by). In each
measured point, nine point (of the passage section. Figure A7. 3It measured it with the
DO meter and dirt photographic densitometer in)(Only the distribution of the direction
of depth of the center part of the waterway was measured in D and E that placed the
straightening vane).
The operational method of the diffuser assumed to be a measuring object is as follows.
‒ Condition 1 (normal operation): Main machine: High speed driving (80%)
and accessory: High speed driving (80%)
‒ Condition 2 (one slow running): Main machine: High speed driving (80%)
and accessory: Slow running (33%)
‒ Condition 3 (two slow running): Main machine: Slow running (33%) and
accessory: Slow running (33%)
< finding >
It is of each as for the measurement result of DO and the solid concentration. Table A7.
1､
Appendix 7Example of oxidation ditch process model development
467
Table A7. 2[Ni] was shown.
・ In each operating condition, the inclination of remarkable DO and the solid
concentration is not caused in the waterway section.
・ The difference is seen about the DO density though the solid concentration is
roughly equal comparing a different measurement part.
Sedimentation pond
E
C
D
F
B
A
+Y
Influent
Sending back dirt
Aerator
+X
4-15.Measurement point outline
Figure
Figure A7. 2Measurement part in tank in density gradient investigation in waterway section
468
Appendix 7Example of oxidation ditch process model development
c
b
Flow direction
a
+Y
1
2
+Z
+X
3
4-17.Measurement point outline
Figure
c
b
a
1950
300
1950
300
+X
500
1
750
+Z
2
900
3
250
b-2 is driven:
- Reading point when non-stationary
- Flow direction:
From the interior to the this side side
4-16.Measurement point outline
Figure
Figure A7. 3Measured point in waterway section in density gradient investigation in waterway
section
Table A7. 1DO concentration measurement result in density gradient investigation in waterway
section
Condition 1 (normal operation)
Condition 2 (one slow running)
Condition 3 (two slow running)
A
a
b
c
A
a
b
c
A
a
b
c
1
2
0.25
0.14
0.10
0.33
0.22
0.13
0.07
0.05
0.14
0.10
0.25
0.15
0.10
1
2
0.10
0.19
1
2
0.10
0.07
0.06
469
Appendix 7Example of oxidation ditch process model development
3
0.19
0.12
0.10
3
0.20
0.15
0.11
3
0.08
0.07
0.06
B
a
b
c
B
a
b
c
B
a
b
c
1
2
3
0.12
0.10
0.10
0.15
0.09
0.10
0.08
0.07
0.10
0.10
0.15
0.09
0.08
0.09
0.08
0.07
0.11
0.09
0.10
0.10
0.08
0.10
1
2
3
0.07
0.10
1
2
3
0.08
0.07
0.10
C
a
b
c
C
a
b
c
C
a
b
c
1
2
3
0.40
0.94
0.33
0.25
0.25
0.24
0.20
0.20
0.84
0.28
0.23
0.22
0.24
0.27
0.15
0.20
0.60
0.51
0.25
0.22
0.25
0.16
1
2
3
0.25
0.65
1
2
3
0.20
0.16
0.20
D
a
b
c
D
a
b
c
D
a
b
c
1
2
3
-
0.50
-
0.10
-
0.10
-
-
-
0.09
-
-
0.09
-
-
0.46
-
-
0.07
-
1
2
3
-
0.49
1
2
3
-
-
-
0.08
-
E
a
b
c
E
a
b
c
E
a
b
c
1
2
3
-
0.45
-
-
0.11
-
0.10
-
0.50
-
-
0.10
-
-
0.09
-
-
045
-
-
0.07
-
1
2
3
-
-
1
2
3
-
0.08
-
470
Appendix 7Example of oxidation ditch process model development
Table A7. 2Solid concentration measurement result in density gradient investigation in waterway
section
Condition 1 (normal operation)
Condition 2 (one slow running)
Condition 3 (two slow running)
A
a
b
c
A
a
b
c
A
a
b
c
1
2
3
2,470
2,540
2,540
2,140
2,510
2,350
2,470
2,470
3,200
2,670
3,600
2,530
2,500
2,540
2,540
2,500
2,540
2,940
2,590
2,940
2,450
2,540
1
2
3
2,410
3,200
1
2
3
2,610
3,130
2,530
B
a
b
c
B
a
b
c
B
a
b
c
1
2
3
2,540
2,610
2,470
2,540
2,540
2,540
2,470
2,540
2,610
2,510
2,540
2,500
2,610
2,510
2,470
2,500
2,610
2,610
-
2,540
-
-
1
2
3
2,470
2,610
1
2
3
2,500
2,670
-
C
a
b
c
C
a
b
c
C
a
b
c
1
2
3
0.40
0.94
0.33
0.25
0.25
0.24
0.20
0.20
0.84
0.28
0.23
0.22
0.24
0.27
0.15
0.20
0.60
0.51
0.25
0.22
0.25
0.16
1
2
3
0.25
0.65
1
2
3
0.20
0.16
0.20
D
a
b
c
D
a
b
c
D
a
b
c
1
2
3
-
2,530
-
-
2,410
-
2,470
-
2,540
-
-
2,410
-
-
2,510
-
-
2,540
-
-
2,610
-
1
2
3
-
-
1
2
3
-
2,620
-
E
a
b
c
E
a
b
c
E
a
b
c
1
2
3
-
2,470
-
-
2,540
-
2,540
-
2,500
-
-
2,540
-
-
2,470
-
-
2,510
-
-
2,540
-
1
2
3
-
-
1
2
3
-
2,530
-
Density gradient investigation of DO and dissolubility water quality parameter in direction of
length of waterway
The time varying situation of a dissolubility water quality parameter according to DO
and nitrogen and Rin was investigated in each place in OD for the real OD method
facilities under operation.
< object facilities >
The outline of the object facilities is as follows.
‒ Reactive tank shape:Horseshoe type
‒ Capacity of reactive tank: 1,329 m3
‒ Diffuser
form
and
the
number:Spindle
type
(Declared
power:
11
Appendix 7Example of oxidation ditch process model development
kilowatt-hour)×2
‒ Diffuser ratings oxygen supply ability (per one): 439 kgO2/d
471
472
Appendix 7Example of oxidation ditch process model development
< search procedure and content >
Four-place in OD tank (① -④;Figure A7. 4And, the mixture obtaining water of 24 2h
pitch × h was done, and NH4-N, NO2-N, NO3-N, and the PO4-P density were measured
in the part immediately after the outflow from this tank. The measurement of the DO
density with the DO meter was also executed at ..adoption.. [mizu].
< finding >
The measurement result of each water quality itemFigure A7. 5[Ni] was shown.
・ A remarkable difference by the measurement part is not seen in NH4-N, NO2-N,
NO3-N, and the PO4-P density.
・ The difference of the density of the clear is seen by [mizu] ..adoption.. part about
the DO density. DO decreases from the diffuser in ② and ④ with the distances
comparatively, and it is understood that it is a level not to be able to disregard the
oxygen consumption rate in the flowing process in the tank compared with the
flow rate in the tank.
The reactive tank model's discussion
The modeling methodology of OD was discussed on the basis of the above-mentioned
finding by the following policies.
・ The complete mixing [souretsu] model is used.
・ You may consider that the solid concentration is uniform in a reactive tank as
long as the operating condition to which dirt subsides is not set. That is, there is
no necessity for building in the phenomenon of the subsidence etc. in a usual
driving.
・ Because a clear difference is seen by the part in the DO density in the direction of
the length of the waterway, it is improper to model the direction of the length of
the waterway in one complete mixing tank.
・ The DO density gradient in the direction of the width of the waterway and the
direction of depth is the extent that can be disregarded. Therefore, there is no
necessity for dividing the waterway section into two or more tanks.
・ A virtual tank of the small capacity is installed in the part (influent, sending back
dirt inflow part, diffuser installation place, and treated water outflow part) with
the inflow going out of the material in consideration of the plug flow type
characteristic. There is no necessity assumed to be place's independent tank
Appendix 7Example of oxidation ditch process model development
because it doesn't consider the inclination of the solid concentration however.
473
474
Appendix 7Example of oxidation ditch process model development
Influent and sending back dirt
④
Spindle rotor
(main machine)
①
②
③
Rise
To the final sedimentation tank
Excess sludge
Figure A7. 4Measurement part in density gradient investigation of flow direction
1
①
③
Outflow
0.8
-N [mg/L]
0.6
4
NH
16
14
②
④
12
-N [mg/L]
10
0.4
3
NO
0.2
8
6
4
①
③
Outflow
2
0
0
9
11 13 15 17 19 21 23
1
3
5
7
9
9
11 13 15 17 19 21 23
Time [h]
1
3
5
7
9
1
3
5
7
9
Time [h]
(a) NH4-N
(b) NO3-N
5
5
①
③
Outflow
4
-P [mg/L]
②
④
①
③
Outflow
4
DO [mg/L]
3
3
4
PO
②
④
2
2
1
1
0
②
④
0
9
11 13 15 17 19 21 23
Time [h]
(c) PO4-P
1
3
5
7
9
9
11 13 15 17 19 21 23
Time [h]
(d) DO
Appendix 7Example of oxidation ditch process model development
475
Figure A7. 5Time variance of each water quality in density gradient investigation of flow direction
476
Appendix 7Example of oxidation ditch process model development
Because detailed water quality profile data in OD in real facilities was not obtained
when the number of tanks of complete mixing [souretsu] models is discussed, virtual
facilities condition that seems to be typical it (here. Table A7. 3It was assumed that the
number of tanks that did not influence the calculation result was found by setting), and
doing the simulation that changed the number of division tanks of directions of the
length of the waterway on the process model within the range of 1-12.
The resultFigure A7. 6It is as showing [ni].
・ The number of division tanks receives and the number of tanks influences the
calculation result of the treated effluent quality strongly in three or less.
Therefore, such small number of tank division is improper.
・ On the other hand, the number of division tanks is equal and the calculation
result is equal in the range of 4-12.
Table A7. 3Simulation condition of virtual facilities used to discuss number of composition tanks
of reactive tank models
Facilities
condition
OD capacity
1600 m3
Capacity of the final
sedimentation pond
Diffuser ability
700 m3
1300 m3/d
Influent quantity
Inflow
condition
SF :11 mg/L､SA :5 mg/L､SNH4 :21 mg/L
SNO3 :0 mg/L､SPO4 :1 mg/L､SI :11 mg/L
XI : 70mg/L､Xs : 148mg/L､XH :31 mg/L
Influent quality
Dirt sending back ratio
Operating
condition and
others
Amount
sludge
of
excess
Diffuser driving cycle
Water temperature
Calculation
condition
Biological
response
model
Numerical
calculation
method
Initial condition
Calculation days
600 KgO2/d×2
0.8
22 m3/d
High speed (80%): 1h and the stop: 2h
of 1h is assumed to be a basic cycle,
and it changes according to the load.
16℃
ASM2d
Calculation given repeating variability
data during a day
Stationary solution when number of
composition tanks is made eight tanks
120 days
Appendix 7Example of oxidation ditch process model development
4.0
477
4.0
NH4-N
NOx-N
PO4-P
3.5
3.0
2.5
Element
density
3.0
2.5 density mg/L
Element
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
1
2
Three (rise3 soon) 4
5
6
8
12
Number of division tanks steps in OD tank
T-N
NH4-N
T-P
3.5
0.0
1
2
Three (rise3 soon) 4
5
6
8
12
Number of division tanks steps in OD tank
Figure A7. 6Calculation result of treated effluent quality in various conditions to change number
of composition tanks of reactive tank models
It tempers this result with room and the inflow part of influent and the sending back
dirt and the outflow parts of the treated water : in consideration of compatibility when
differing. Figure A7. 7The reactive tank model who consisted of eight shown [ni] tanks
decided to be assumed to be basic.
・ The capacity of influent, the sending back dirt inflow tank, the diffuser
installation tank (two tanks), and the treated water outflow tanks is each 3%, is
5%(×2), 5%, and smalls of a total tank capacity among eight tanks where the
[souretsu] model is composed.
・ Four tanks of the remainder correspond to the straight-line portion of OD of the
horseshoe type, and account for 21% of a total tank capacity respectively. It can be
said that these are subjects of the place of the biological response.
・ The flow rate in the tank is 0.1-0.25 when based on the following flow rate models
It is a range of m/s. This is 1.9-4.7 as each four tank above real HRT It is
corresponding in min.
478
Appendix 7Example of oxidation ditch process model development
Sending back dirt
Influent
OD1
3%
OD2
5%
OD3
21%
OD4
21%
Oxygen
OD5
21%
OD6
21%
OD7
5%
OD8
5%
Oxygen
Final sedimentation
[He] tank
- Percent table[Shime]
..drinking.. numerical value shows
(totalthe
capacity
capacity
ratio)｡
of each tank.
Figure A7. 7Tank composition of reactive tank model of OD
479
Appendix 7Example of oxidation ditch process model development
３．Discussion about modeling methodology of oxygen supply
（１）Outline
As for the spindle type diffuser used in the OD method facilities in our country, driving
that can usually adjust the cycle by the inverter, intends the control of nitrification and
the reduction in the electric power used, and changes the cycle timewise is done actually
widely. Enough information is not given about the condition of remarkably decreasing
the cycle though the oxygen supply speed can be presumed if this is used (Shimizu)
because the performance curve of which the index is the cycle is given to the device (On
a usual business, it is considered that there is no oxygen supply and only the stir power
is given).
Then, the relation between the cycle and the oxygen supply speed in a wide range of the
cycle was intended to be found by measuring KLa on two or more conditions to change
the operational method of two diffusers (cycle), and analyzing it along with the
performance curve of the device in real facilities where the trial run by Shimizu before it
used it was done. Describing the oxygen supply by assuming the cycle to be a logical
input value to the given diffuser by building this in the process model as an incidental
model becomes possible.
（２）Data collection
Figure A7. 1KLa was measured in the OD method facilities before it used it with
equal facilities composition.
Object facilities
Various conditions of the object facilities are as follows.
‒ Reactive tank shape:Horseshoe type
‒ Capacity of reactive tank: 1,240 m3
‒ Diffuser
form
and
the
number:Spindle
type
(Declared
power:
11
kilowatt-hour)×2
‒ Diffuser ratings oxygen supply ability (per one): 344 kgO2/d
Diffuser operating condition
KLa was measured under the following four conditions to change the cycle of two
diffusers.
480
Appendix 7Example of oxidation ditch process model development
‒ Condition 1:High speed (44 rpm) + high speed (44 rpm)
‒ Condition 2:High-speed (16 rpm) of (44 rpm) + low speed
‒ Condition 3:High speed (44 rpm) + stop ( 0 rpm)
‒ Condition 4:Low speed (16 rpm) + low speed (16 rpm)
- A set cycle is shown about each condition in order of "Main machine +
accessory". About two diffusers and the positions of the KLa measurement
partFigure A7. 8Refer to [wo].
481
Appendix 7Example of oxidation ditch process model development
- Because the cycle in the declared power is 45 rpm, "High speed" and "Low
speed" mentioned here are corresponding to the output of 98 and each 36%
against ratings.
KLaMeasurement part
Main machine
Accessory
Oxidation
[Dei;cchi]
Flow rate measurement part
Figure A7. 8Position of measurement part of KLa and flow rate and two diffusers
Measuring method of KLa
Because Shimizu should not filled a reactive tank for the trial run, and the biological
response in the tank be considered in this facilities, the unsteady method.
source not found.[Niyori]
Error! Reference
KLa was measured.
The DO sensor for the DO concentration measurementFigure A7. 8It set it up in the
shown measurement part of [ni]. First of all, a sodium sulfite 110ppm a large amount
was added to consume DO in the tank, it stirred with the diffuser in the tank, and DO
was 0 It was confirmed to decrease to mg/L. Next, the diffuser was stopped once and
after it had been confirmed that the flow rate in the tank decreased enough, it was
operated by the cycle in which two diffusers were set. KLa was calculated from the rise
rate of DO at that time by following equation.
KLa 
dCO 2
1

CO 2, S  CO 2
dt
Density..measurement..water temperature..saturation..density.
Expression (A7. 1)
482
Appendix 7Example of oxidation ditch process model development
In the water temperature when measuring it, the soaking depth of 2-3℃ and the scatter
nature rotor ([sabuma-jensu]) is 50mm.
483
Appendix 7Example of oxidation ditch process model development
（３）Measurement result and analysis
The KLa measurement result and this by four conditions were converted into the oxygen
supply speed (It is only called, "Oxygen supply speed" as follows) in the "20℃,DO=0
mg/L" condition. Table A7. 4[Ni] was brought together.
・ KLa decreases greatly when the cycle of the diffuser is decreased up to 36% of
ratings, and the oxygen supply of the amount that cannot be disregarded is
caused in condition 4 of driving both of the two diffusers in low speeds (About 5%
of condition 1 driving both at high speed).
・ (..add as the cycle of two diffusers is simple.. ..cannot the enough expression of the
relation to the oxygen supply speed as the index of matched what... Figure A7. 9)｡
This suggests that the relation between the cycle and the oxygen supply speed be
not linear in one diffuser.
・ Then, the oxygen supply speed per one of the diffusers is presumed from the
measurement result, and what plotted along with performance curve of the device
data :. Figure A7. 10..going out.. .It is understood that approximating by the
straight line is improper when including it to cycle lower area though most
straight lines in the area where the cycle is high the dependency at the oxygen
supply speed to the cycle. Then, the relation between the cycle (driving strength)
and the oxygen supply speed was assumed to be approximate by the 2 secondary
type here.
Table A7. 4Measurement result at KLa and oxygen supply speed
Oxygen supply speed
KLa [h-1]
kgO2/d *
Condition 1
2.14
625
High speed (44 rpm) + high speed
(44 rpm)
Condition 2
1.27
360
High-speed (16 rpm) of (44 rpm) +
low speed
Condition 3
1.27
360
High speed (44 rpm) + stop (0 rpm)
Condition 4
Low speed (16 rpm) + low speed
(16 rpm)
- Water temperature =20℃ and correction value to condition of DO=0 mg/L.
0.10
29
484
Appendix 7Example of oxidation ditch process model development
Condition 1
700
/日]
600
2
Condition 3
500
Condition 2
Oxygen400
supply..speed..total.
300
200
Condition 4
100
0
0
20
40
60
80
100
Cycle of aeration device (two totals) Rpm
Figure A7. 9Simple harmony of cycle of two diffusers and relation to total oxygen supply speed
Figure A7. 10Relation between driving strength and oxygen supply speed of one diffuser
485
Appendix 7Example of oxidation ditch process model development
（４）The oxygen supply model's discussion
Discussion..obtain..diffuser..per..drive..strength..oxygen
supply..speed..relation..homotype..device..declared
power..differ..large..differ..assume..given..declared
power..have..device..oxygen
supply..speed..drive..strength..ratings..output..ratio..index..following
equation..generalization.


RO 2  435.01  f a  91.30  f 
2

RO 2,max
344
 RO 2  RO 2,max  1.26  f a  0.27  f a
2

Expression (A7. 2)
It is RO2 here: oxygen supply speed kgO2/d and RO2 and max: ratings oxygen
supply ability kgO2/d of the diffuser and fa: driving strength (The ratings ratio;
Having obtained the measurement data is in the range of 0.36-0.98) -.
Because the ratings oxygen supply ability of the diffuser is given on the condition of
Shimizu, 20℃, and DO=0 mg/L, KLa in this condition can be calculated by following
equation.
RO 2  10 3
KLa 
Vaer  S O* 2, 20
Expression (A7. 3)
Diffuser..installation..division..volume..water temperature..saturation..density.
It is KLa ..(.. expression if it calculates to correct the mixture and the water temperature
by using this by the method similar to a usual facilities design as for ' Expression
(A7.
4)The DO application rate in the mixture at the given water temperature is an
expression.
Expression (A7. 5)[De] can calculate.
K L a'    K L a  1.024 (T 20)
dS O 2,aer
dt

 K L a'   S O* 2,T  S O 2,aer
Expression (A7. 4)

Expression (A7. 5)
486
Appendix 7Example of oxidation ditch process model development
SO2 and aer ..here..: DO density mg/L of the diffuser installation division and
saturation DO ..KLa 'Mixture correction factor - of water temperature, KLa d-1
that corrects the mixture, and α:KLa and β: mixture correction factor - of
saturation DO density and S*O2 and T:.. density mg/L at water temperature T℃.
Appendix 7Example of oxidation ditch process model development
487
４．Discussion about modeling methodology of flow rate in reactive tank
（１）Outline
The flow rate in the tank changes, too, when the cycle is changed so that this may bear
the stir (secure of the flow rate) in in addition to the oxygen supply a reactive tank when
the spindle type diffuser is used. As a result, it is expected to change DO in a reactive
tank and the generation situation of the density gradient of other substrate, and to
influence processing status.
Then, the flow rate in a reactive tank was also measured in the KLa measurement
shown in the preceding clause. The relation to the flow rate in the operational method of
two diffusers (cycle) and the tank was arranged by using this, and the appropriate
modeling methodology was discussed.
Dirt subsides, will pile up in a reactive tank in the condition to stop two diffusers
completely, and it be a result of review that shows here simply squeezed to "Expectation
of the flow rate". The discussion is added by the next paragraph about the modeling
methodology of the condition that dirt piles up.
（２）Data collection
The flow rate in a reactive tank was measured in the same to what targeted by
discussing the preceding clause facilities.
Diffuser operating condition
The flow rate was measured under the following three conditions to change the cycle of
two diffusers.
‒ Condition 1:High-speed (44 rpm) of (43 rpm) + high speed
‒ Condition 2:High speed (41 rpm) + stop ( 0 rpm)
‒ Condition 3:Low-speed (15 rpm) of (14 rpm) + low speed
Method of measuring flow rate
After driving the diffuser in the setting condition begins, and the flow rate in the tank is
steady enoughFigure A7. 8Nine point in passage section (in the shown measurement
part of [ni]. Figure A7. 11The flow rate was measured by) one by one. (for X, Y, and Z
everyone with the electromagnetic flowmeter of three dimensions it for the
measurement. Figure A7. 11Two of the) of the identifying values taken by 1s pitch The
mean between min was assumed to be measurements.
488
Appendix 7Example of oxidation ditch process model development
（３）Measurement result and analysis
Flowing direction in tank in three operating conditions (Figure A7. 11Distribution of the
flow rate of the direction of ..drinking.. Y) in the direction of the width of the waterway
according to depthFigure A7. 12[Ni] was shown. The flow rate in the direction of the
width of the waterway (direction of this figure X) and the direction of depth (direction of
this figure Z) is 0.05 It disregarded it here because it was remarkably small compared
with the flow rate in m/s or less and the direction of Y.
Appendix 7Example of oxidation ditch process model development
489
c
b
Flow direction
a
+Y
1
2
+Z
+X
3
Figure A7. 11Measured point in passage section in flow rate measurement part
・ As for condition 1-3, a simple mean of the flow rate (It is called, "Total mean flow
velocity" at the following) in the measured point in nine places is 0.25 respectively,
0.17, and 0.15 It is m/s. In condition 2 of driving only one compared with condition
1 of high-speed rotating both of the two diffusers and condition 3 to assume two to
be a low-speed rotation both, the flow rate has decreased obviously. This is 180
times amount (1,200 m3/d) of the design influent as the amount of the circulation
in the tank in this facilities. corresponding of in m3/d a total mean flow velocity
in condition 1
・ However, 0.1 that is the standard of the lowest flow rate that doesn't make dirt
subside in all conditions and the measured points It doesn't enter the state that
greatly falls below m/s.
・ In the direction of the width of the waterway, (..seeing the tendency that the flow
rate grows heading for the direction of corresponding outside of in the time of
surroundings part... Figure A7. 12;When the average of the flow rate by all depth
is taken about three places of the direction of the width of the waterway, it is 0.17
respectively, 0.26, and 0.31 from the inside to the outside in condition 1 It is 0.14,
0.19, and 0.23 in m/s and condition 2 It is 0.15, 0.16, and 0.17 in m/s and
condition 3 Become m/s. )It is a result of it is easy to understand if it thinks the
centrifugal force works at the time of surroundings part. The difference of the
total mean flow velocity seen between condition 2 and 3 originates chiefly in the
490
Appendix 7Example of oxidation ditch process model development
difference of the flow rate outside of the waterway.
・ When the average of the flow rate in the direction of the width of the waterway is
taken about each depth, it is 0.26 respectively, 0.26, and 0.23 in condition 1 for
the direction of depth It is 0.21, 0.17, and 0.17 in m/s and condition 2 It is 0.16,
and is 0.15, and 0.16 in m/s and condition 3 It is m/s. The flow rate in the bottom
causes and a flow velocity distribution remarkable like the direction of the width
of the waterway is not caused though the small tendency is seen.
・ The number of mean rotations of two diffusers of total mean flow velocity in each
operating condition is taken in the axis of abscissas and plotted what :. Figure A7.
13..going out.. .Both show an excellent linear relationship, and are suggesting
that the number of mean rotations (output) is assumed to be an index and the
flow rate in the tank be predictable.
Appendix 7Example of oxidation ditch process model development
491
< azimuth >
In case of the above →(direction of Z)
Outside → on inside (direction of X)
Direction of flowing (direction of y)
>
0.4
Height-amount
Low-lower
Flow0.3rate (+Y) in tank M/sec
[Henhiku]
0.2
0.1
0.0
Inside
Inside
X coordinates (+X)
The outside
< inside >
0.4
Height-amount
Low-lower
Flow0.3rate (+Y) in tank M/sec
[Henhiku]
0.2
0.1
0.0
Inside
Inside
X coordinates (+X)
The outside
< under >
0.4
Height-amount
Low-lower
Flow0.3rate (+Y) in tank M/sec
[Henhiku]
0.2
0.1
0.0
Inside
Inside
X coordinates (+X)
The outside
Figure A7. 12Flow velocity distribution in direction of width of waterway in each condition and
depth
492
Appendix 7Example of oxidation ditch process model development
Condition 3
Condition 2
Condition 1
0.3
0.2rate (+Y) in tank m/second
Flow
0.1
y = 0.0034x + 0.1015
R2 = 0.9994
0.0
0
20
40
60
Simple arithmetic average rpm of cycle
Figure A7. 13Number of mean rotations of two diffusers and relation to total mean flow velocity
（４）The flow rate model's discussion
Figure A7. 13It drinks, driving strength of the person in charge of [kan] is assumed to
be an index, and what arranged as a predicting that doesn't depend on the declared
power of the diffuser again is following equation. It can be said that there is no obstacle
even if it generalizes like this expression within this range because in the planning
approach of the OD method facilities usually used in our country, the quantity of water
to be treated, the capacity of a reactive tank, and the diffuser output are respectively
roughly in the proportional connection. It is necessary to note it because regularity
cannot be simply applied when the relation between the capacity of a reactive tank and
the diffuser specification is greatly different from this.
FOD  0.14  f a ,ave  0.10
Expression (A7. 6)
It is FOD here: mean flow velocity m/s in a reactive tank and fa and ave: Average
driving strength-.
As the logical input value of driving strength of the diffuser in building in the process
model of regularity, flow rate (..in a reactive tank... Figure A7. 7Describing the amount
Appendix 7Example of oxidation ditch process model development
493
of the circulation in case of the [noyouna] [souretsu] model) becomes possible. It can be
said that driving strength that is bigger than this is a regular range because there is a
possibility that the measurement data is not obtained within the range where average
driving strength of two diffusers is 32% or less and the subsidence of dirt is caused, too.
494
Appendix 7Example of oxidation ditch process model development
５．Discussion about modeling methodology of dirt subsidence when diffuser
stops
（１）Outline
When the inflow load volume doesn't especially come up to the designed value, the
example of building in the process of completely stopping the device in the run cycle of
the diffuser is often seen in the OD method facilities in our country as shown in the
above-mentioned. In that case, influent and the sending back dirt will flow in with dirt
subsided in the OD tank.
It is necessary to understand what kind of water quality changing is causing
respectively in the mixed clinical posture in the tank after the diffuser stops, the
supernatant fluid water part, and the piling up dirt to model such a process. Then, the
investigation was executed in real facilities under operation, and the modeling
methodology was discussed on the basis of it.
（２）Data collection
The tracer experiment after the (b) diffuser had stopped of the measuring water quality
in a reactive tank after the (a) diffuser had stopped was executed in the OD method
facilities under operation when the process (stop process) where the diffuser stopped
completely was built in.
Object facilities
Various conditions of the object facilities are as follows.
‒ Reactive tank shape:Horseshoe type
‒ Capacity of reactive tank: 1,330 m3
‒ Diffuser
form
and
the
number:Spindle
type
(Declared
power:
11
kilowatt-hour)×2
‒ Diffuser ratings oxygen supply ability (per one): 439 kgO2/d
Measuring water quality after diffuser stops
75 63 immediately after the process beginning against the stop process of min The
mixture was collected from the vicinity of the surface and the surface of the water from
about 150cm(It is 85cm from the sludge-liquid interface to the direction of depth) in 5
times and 3 places in a reactive tank between after min, and NH4-N and the NOx-N
density were measured.
Appendix 7Example of oxidation ditch process model development
495
The operating condition when the investigation is executed is as follows.
‒ Influent quantity: 922 m3/d
‒ Sending back dirt flow rate: 1160 M3/d (1.3 compared with sending back)
‒ Depth of reactive tank: 250 cm
‒ Diffuser driving cycle:Aerification worker degree 75 min (50% of main
machine 88%+ accessory) stop process 75 Min
‒ Height of dirt field side at stop process:65cm from surface of the water
496
Appendix 7Example of oxidation ditch process model development
Tracer experiment when diffuser stops
Immediately after the stop process beginning or 10 The tracer was sent to the inflow
part of influent and the sending back dirt after min (The lithium chloride; It is 5 as the
average concentration in a reactive tank a large amount of mgLi/L), the mixture was
collected in the direction of depth in 4-5 places in the tank by three points (70 from the
surface and the surface of the water the cm and 150 cm) afterwards, and the lithium
concentration in the filtrate was measured.
The operating condition when this investigation is executed is as follows.
‒ Influent quantity: 878 M3/d
‒ Sending back dirt flow rate: 1157 M3/ d (1.3 compared with sending back)
‒ Depth of reactive tank: 250 cm
‒ Diffuser driving cycle:Aerification worker degree 75 min (50% of main
machine 88%+ accessory) stop process 75 Min
‒ Height of dirt field side at stop process:65cm from surface of the water
（３）Measurement result and analysis
Measuring water quality after diffuser stops
[Mizu] ..adoption.. partFigure A7. 14The change in NH4-N and the NO3-N density in
each part after the stop process begins of [ni]Table A7. 5[Ni] was brought together.
・ The surface and 150 in depth The difference is obviously seen in the water quality,
and the concentration difference is growing by the cm(Hereafter, it is called, "Dirt
layer") with the time passage.
・ The difference of the water quality comes to be shown in the direction of the
length of the waterway with the time passage though the difference of the water
quality is not seen in three places immediately after the stop process beginning.
・ In modeling the stop process, it can be judged that it is necessary to handle the
top layer and the dirt layer independently.
497
Appendix 7Example of oxidation ditch process model development
[Mizu] ..adoption.. part
Influent and sending back dirt
③
①
②
Final sedimentation tank
Figure A7. 14[Mizu] ..adoption.. part after diffuser stops in measuring water quality
Table A7. 5Measuring water quality result after diffuser stops
Measuremen
t part
①
②
③
NH4-N [mg/L]
NO3-N [mg/L]
Elapsed time min after stop process begins
Elapsed time min after stop process begins
0
20
35
50
63
0
20
35
50
63
150cm
<0.2
-
<0.2
<0.2
1.1
0.3
1.0
0.6
1.4
0.8
3.4
-
2.9
3.3
2.0
2.0
2.3
1.2
2.2
1.1
Surface
<0.2
0.2
0.7
0.7
0.8
3.5
3.4
3.1
2.7
2.3
Surface
150cm
-
<0.2
<0.2
<0.2
<0.2
-
3.1
2.1
1.4
0.9
Surface
<0.2
<0.2
<0.2
<0.2
<0.2
3.5
3.3
3.1
2.9
2.8
150cm
-
<0.2
<0.2
<0.2
<0.2
-
3.0
2.2
1.5
1.3
Tracer experiment when diffuser stops
The case and after the stop process begins of sending the tracer, 10 immediately after
the stop process beginning Min : the change in [mizu] part and ..adoption.. lithium
concentration about each case turned on when passing. Figure A7. 15､Figure A7. 16[Ni]
was shown.
・ In the case to have sent the tracer immediately after the stop process beginning,
the tracer flowed promptly in part ① near the turning on point and the peak in
part ③ is about 10 though the peak of the lithium concentration was not seen at
all After min, the peak in part ④ is 60 It is understood that the flow rate in the
tank decreased rapidly after the stop process had begun from now on after min it.
498
Appendix 7Example of oxidation ditch process model development
However, ④ ..part..60 The peak after min suggests and the style in the tank at
that time ..appearing point.. suggests not stopping completely (The backflow need
not be considered as shown in the under).
・ Stop process beginning 10 In the case to have sent the tracer after min, the peak
in part ② is about 25 After min, the peak in part ③ is about 40 After min. The
flow rate in the tank can call a small very much, and proves the result above.
Moreover, it can be judged that the backflow of the mixture may be disregarded
because the lithium concentration is from beginning to end a low concentration in
part ④ and ⑤.
Appendix 7Example of oxidation ditch process model development
30 minutes after
60 minutes
it turnsafter
it onit turns it on
0.00
0.22
0.00
Surface
70cm
150cm
3.8
2.8
0.29
Surface
70cm
150cm
499
30 minutes after
60 minutes
it turnsafter
it onit turns it on
0.00
0.00
0.22
0.11
0.20
0.15
Influent and sending back dirt
④
①
③
②
30 minutes after
60 minutes
it turnsafter
it onit turns it on
1.8
0.20
1.3
Surface
70cm
150cm
< part ① >
0.41
0.18
1.2
Surface
70cm
150cm
5
Lithium concentration mg/L
4
3
tank
30 minutes after
60 minutes
it turnsafter
it onitFinal
turnssedimentation
it on
0.80
0.58
1.9
0.50
0.15
0.38
Surface
70cm in depth
150cm in depth
2
1
0
0
< part ③ >
10
20
30
40
50
60
Elapsed time's worth after tracer is turned on
20
Lithium concentration mg/L
15
70
Surface
70cm in depth
150cm in depth
10
5
0
0
< part ④ >
10
20
30
40
50
60
Elapsed time's worth after tracer is turned on
70
5
Lithium concentration mg/L
Surface
4
70cm in depth
3
150cm in depth
2
1
0
0
10
20
30
40
50
60
Elapsed time's worth after tracer is turned on
70
Figure A7. 15Tracer test result after diffuser stops (case turned on immediately after stop process
beginning)
500
Appendix 7Example of oxidation ditch process model development
Presence of backflow
Part ①
Five minutesTen
after
minutes
it turns30
after
it minutes
onit turns
after
53
it on
minutes
it turns after
it on it turns it Part
on
Surface
70cm
150cm
1.9
5.7
15
Part ②
0.9
2.5
19
1.1
2.0
4.1
1.3
1.4
1.9
1.9
0.8
0.8
70cm
150cm
2.8
1.6
3.9
1.8
1.1
3.0
150cm
Surface
70cm
150cm
Five minutesTen
after
minutes
it turns30
after
it minutes
onit turns
after
58
it on
minutes
it turns after
it on it turns it on
0.0
0.0
0.0
70cm
15 minutes after
35 minutes
it turns after
it55onminutes
it turns after
it on it turns it Part
on
Surface
⑤
Surface
④
0.0
0.0
0.0
0.0
0.2
0.3
0.1
0.2
0.4
15 minutes after
35 minutes
it turns after
it55onminutes
it turns after
it on it turns it on
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Influent and sending back dirt
④
⑤
①
③
②
Flowing of direction of direct flow
Presence of difference of flowing in direction of depth
< part ① >
20
Surface
Final sedimentation tank
70cm in depth
150cm in depth
[mg/L]
15
10 concentration
Lithium
5
0
< part ② >
< part ③ >
< part ④ >
< part ⑤ >
10
60
5
[mg/L]
4
3 concentration
Lithium
2
1
0
0
10
6
5
[mg/L]
4
3 concentration
Lithium
2
1
0
10
60
5
[mg/L]
4
3 concentration
Lithium
2
1
0
0
10
6
5
[mg/L]
4
3 concentration
Lithium
2
1
0
0
10
20
30
40
50
60
70
Elapsed time's worth after tracer is turned on
20
30
40
50
60
70
Elapsed time's worth after tracer is turned on
20
30
40
50
60
70
Elapsed time's worth after tracer is turned on
20
30
40
50
60
70
Elapsed time's worth after tracer is turned on
20
30
40
50
60
70
Elapsed time's worth after tracer is turned on
Figure A7. 16Tracer test result after diffuser stops (Stop process beginning 10 case turned on
Appendix 7Example of oxidation ditch process model development
after min)
501
502
Appendix 7Example of oxidation ditch process model development
（４）Discussion about modeling of stop process
It was 60 though the flow rate in the tank promptly decreased to the stop process than
the above-mentioned investigation after the stop process began of the point of causing
the difference in the surface part and dirt in the water quality The flow's completely
stopping at the process time of about min it clarified that the point and the backflow
that not was were able to be disregarded.
As a simple model by whom these findings are reflected
Appendix 7Example of oxidation ditch process model development
503
Table A7. 6Shown what of [ni] was constructed. The point of modeling is as follows.
・ The flow rate in the direction of the length of the waterway decreases because of
the isokinetic after the diffuser stops until it reaches the threshold (flow rate in
the lowest tank).
・ When the flow rate in the tank decreases up to the threshold (subsidence
beginning flow rate), the [souretsu] model's each division is divided into the
supernatant fluid part and dirt by the sludge-liquid interface.
・ The sludge-liquid interface moves in the direction of depth by the isokinetic until
it reaches the critical depth (height that can subside).
・ The flow rate in the direction of the length of the waterway of the supernatant
fluid part and dirt is assumed to be identical.
・ The whole quantity instantaneously moves to dirt in a floating element, and the
biological response progresses only in dirt.
・ A dissolubility element moves according to the flow rate. Diffusion in the
sludge-liquid interface is not considered.
・ When the stop process ends and the flow rate in the tank exceeds the subsidence
beginning flow rate, the sludge-liquid interface disappears, and becomes a
complete mixing condition again in the direction of depth.
504
Appendix 7Example of oxidation ditch process model development
Table A7. 6Outline of modeling diffuser stop process
< immediately after diffuser stop >
One division division
･ It is complete mixing in the direction of depth.
･ It flows by the flow rate immediately before the stop.
Surface of the water
Bottom
< formation and subsidence of sludge-liquid
interface >
Supernatant fluid part
Sludge-liquid interface
Dirt
･ Subsidence beginning when flow rate in tank becomes it
as follows "Subsidence beginning flow rate" (parameter).
･ The supernatant fluid part and dirt are divided into the
direction of depth in the sludge-liquid interface.
･ The sludge-liquid interface moves in the direction of depth
by "Sedimentation rate" (parameter)(The isokinetic
subsidence is assumed).
･ The subsidence when the height of dirt reaches "Height
that can subside" (parameter) stops.
･ The sludge-liquid interface when the flow rate in the tank
exceeds the subsidence beginning flow rate by operating
the diffuser is canceled.
･ A floating element has the whole quantity in dirt (The
biological response is caused only in dirt).
･ Only the movement (Diffusion is not considered) from dirt
according to the subsidence to the supernatant fluid part :
a dissolubility element.
< mass transfer of flow direction >
･ The flow rate in the tank decreases to "Flow rate in the
lowest tank" (parameter) because of the isokinetic after
the diffuser stops. The flow rate decrease speed is given
by "Decay time" (parameter).
･ The supernatant fluid part is assumed to be equal to the
flow rate of dirt.
･ All elements are transported by the flow rate in the tank.
･ Transportation in the flow and the opposite direction is not
considered.
<It flows in OD and go out >
･ To become equal in dirt, the flow rate in the tank moves
the volume of water from the inflow part to the other side
with the supernatant fluid part though the inflow part of
sewage and the sending back dirt (depth) is set on the
basis of actual facilities arrangement.
･ As for a floating element, the whole quantity flows in dirt.
It doesn't flow out.
･ A dissolubility element is transported according to the
movement of water.
Appendix 7Example of oxidation ditch process model development
OD reactor
[Heno] inflow part
OD reactor
[Karano] outflow part
Q DN ,S
Q IN+Re ,S
Q UP ,S
Q IN+Re -Q UP ,S
(Figure flows in dirt, and : from dirt at the outflow. )
Q OUT
505
506
Appendix 7Example of oxidation ditch process model development
Bibliography
7A)
Japan Sewage Works Association: Drainage Japan Sewage Works Association and test method
-1997 edition -1997
7B)
Alex,J., Tschepetzki,R., Jumar,U., Obenaus,F. and Rosenwinkel,K.-H.: Analysis and design of
suitable model structures for activated sludge tanks with circulating flow. Wat.Sci.Tech., Vol.39,
No.4, pp.55-60, 1999.
7C)
Cinar,.O, Daigger,G.T. and Graef,S.P.: Evaluation of IAWQ Activated Sludge Model No.2
using steady-state data from four full-scale wastewater treatment plants. Wat.Environ.Res.,
Vol.70, pp.1216-1224, 1998.
7D)
Daigger,G. and Nolasco,D.: Evaluation and design of full-scale wastewater treatment plants
using biological process models. Wat.Sci.Tech., Vol.31, No.2, pp.245-255, 1995.
7E)
Fiter,M., Colprim,J., Poch,M. and Rodriguez-Roda,I.: Enhancing biological nitrogen removal
in a small wastewater treatment plant by regulating the air supply. Wat.Sci.Tech., Vol.48,
No.11-12, pp.445-452, 2003.
7F)
Ghermandi,A., Bixio,D., Thoeye,C. and De Gueldre,G.: Technical-economical evaluation of the
operation of oxidation ditches. Wat.Sci.Tech., Vol.52, No.12, pp.133-139, 2005.
7G)
Insel,G., Russell,D., Beck,B. and Vanrolleghem,P.A.: Evaluation of nutrient removal
performance for an orbal plant using the ASM2d model. Proceedings of Water Environment
Federation 76th Annual Technical Exhibition & Conference (CD-ROM), Los Angeles,
Oct.11-15, 2003.
7H)
M?kinia,J. and Wells,S.A.: A general model of the activated sludge reactor with dispersive
flow- I. Model development and parameter estimation. Wat.Res., Vol.34, pp. 3987-3996, 2000.
7I)
Meijer,S.C.F., van der Spoel,H., Susanti,S., Heijnen,J.J. and van Loosdrecht,M.C.M.: Error
diagnostics and data reconciliation for activated sludge modelling using mass balances.
Wat.Sci.Tech., Vol.45, No.6, pp.145-156, 2002.
7J)
Stamou,A., Katsiri,A., Mantziaras,I., Boshnakov,K., Koumanova,B. and Stoyanov,S.:
Modelling of an alternating oxidation ditch system. Wat.Sci.Tech., Vol.39, No.4, pp.169-176,
1999.
7K)
van Veldhuizen,H.M., van Loosdrecht,M.C.M. and Brandse,F.A.: Model based evaluation of
plant improvement strategies for biological nutrient removal. Wat.Sci.Tech., Vol.39, No.4,
pp.45-53, 1999.
Appendix 8Trouble shooting during charibration stepError! Reference source not found.
Appendix８Troubleshooting in calibration
Here, the matter and each counter measure that should be checked when processing
status of the object facilities cannot be reproduced by the simulation at the calibration of
the model is illustrated.
Also because the majority are the matters that should be confirmed as assumption of
work there, it is desirable to confirm these before the adjustment of parameter value
though the situation in which the fitting cannot be basically done to the processing
results data in "Adjustment of 4.8.8 parameter values etc." is assumed.
General point to be checked
Whether the following information is appropriately set is confirmed before all the adjustments.











Facilities composition (reactive tank division, various ducts, and final sedimentation tanks)
Capacity of reactive tank
Influent quantity
Influent quality
Sending back dirt flow rate
Internal circulation flow rate
Excess sludge flow rate (Or, SRT).
Blast volume (Or, KLa and DO control value, etc.).
Water temperature
Reactive tank initial concentration
Numerical calculation method (*t is included).
507
508
Appendix 8Trouble shooting during charibration step
Calibration of amount of solid
Problem
Neither the
abundance
of the solid
nor
the
reactive
tank solid
concentratio
n
are
expressible.
Check matter
Confirmation of handling of
amount of solid that flows out as
treated water

–
Is not numerical values with
different presence of the amount
of the treated water solid
compared?
Confirmation of measurement data
integrity of amount of generation
solid

Counter measure

It compares it by the abundance of
the solid including the amount of the
outflow solid as the treated water
(Because the treated water solid
concentration is expected according
to the final sedimentation pond
model, this is treated easily).

At least, data where Rin's revenue
and expenditure is taken is used.
The average data of half month the frequency of the data that can
be used and after the constancy of
processing status is considered
about one month is used to ease
the influence of the difference of
short-term data.
The changed portion is corrected
when the results data is hit at the
change time of the amount of the
solid in the faction and the amount
of the generation solid is calculated.
An additional investigation to
understand the abundance of the
solid accurately is done.
–
Is Rin's revenue and expenditure
taken?

– Do not you calculate by data with
scarce representative character for
a short term?
– Isn't the reactive tank solid
concentration
in
increase/decreasing tendency at
the results data collection time?



Confirmation of SRT
–
Is
SRT
in
the
simulation 
corresponding of the results value?
The excess sludge flow rate etc. are
adjusted so that SRT may agree
with the results value (Being able to
trust the results value of SRT is
assumption).
Appendix 8Trouble shooting during charibration stepError! Reference source not found.
Confirmation of validity of solid
index and conversion factor

–
When MLSS and MLVSS are 
assumed to be an index, is the
conversion method from the 
element of the model appropriate
(Are neither XTSS, MLSS nor
MLVSS simply compared?)?

– Similarly, is conversion factor to
XTSS appropriate (comparison etc.
between the actual measurement
value and calculated value of the
XTSS /CODCr ratio of dirt)?

Indices that can be compared are
compared.
It is discussed to make CODCr an
index to exclude the uncertainty
concerning the conversion factor.
To be expressible of the actual
measurement value of the XTSS
/CODCr ratio when MLSS and
MLVSS are assumed to be an
index, conversion factor (iTSS, XI,
iTSS, and BM, etc.) is adjusted.
Confirmation of influent quality
–
Has not the segmentation resulted 
high (>30%) as the abundance
ratio of XI is extreme?
The XI /XS ratio of influent is
adjusted.
509
510
Appendix 8Trouble shooting during charibration step
Calibration of nitrification
Problem
Check matter
Counter measure
The NH4-N  Confirmation of SRT
density of a
– Is
SRT
in
the
simulation 
conceited
corresponding
of
the
results
value?
tank and the
treated water
is
not
expressible.

The excess sludge flow rate etc. are
adjusted so that SRT may agree
with the results value (Being able to
trust the results value of SRT is
assumption).
Confirmation of DO density
–
Can DO of the conceited tank each 
division reproduce the actual
measurement value?



The scatter nature model is
adjusted so that the DO density
may become the measurement
value
equivalent
(KLa,
blast
volume, oxygen solubility efficiency,
and DO control value, etc.).
The DO concentration profile of a
conceited tank is investigated (You
may
investigate
the
DO
concentration distribution in the tank
handled as one division on the
model together). The tank division
on the model is changed according
to the result.
DO in the simulation is compulsorily
changed when the measurement
data of DO is all unavailable and
behavior is confirmed.
Confirmation of nitrogen balance
–

Is the revenue and expenditure 
elements of the amount of the
inflow nitrogen and the amount etc.
of the excess sludge nitrogen 
corresponding of the results value?
Confirmation of XAUT density of
influent
–
Is not both initial values of XAUT of
influent and XAUT of a reactive
tank set to 0?
– Isn't the XAUT density of influent
excessive compared with the
amount in the faction?

IN and Xs, etc. are adjusted so that
inflow T-N density may become the
results value equivalent.
IN, BM, and iNXI, etc. are adjusted
so that the amount of the excess
sludge nitrogen may become the
results value equivalent (It is
assumption
appropriately
expressible of the amount of the
excess sludge solid).
The density of influent XAUT of
extent that doesn't influence the
amount in the faction is set.
Appendix 8Trouble shooting during charibration stepError! Reference source not found.

Confirmation of expression of
NH4-N density of reluctance/hypoxia
tank
–
Is SNH4 of the uppermost in
reactive
tank
style
division
expressible?



To be expressible of the NH4-N
density of the sending back dirt, the
final sedimentation pond model is
adjusted.
The validity of the sending back dirt
flow rate set point is confirmed by
the MLSS revenue and expenditure
in the tank circumference.
The blast volume (amount of the
oxygen supply) is adjusted for the
pseudoreluctance tank.
..nitrification.. ..calibration (.. ..continuation..)
Problem
The NH4-N
density of a
conceited
tank and the
treated water
is
not
expressible.
..(.. ..continu
ation..)
Check matter

Counter measure
Confirmation of limiting by PO4-P
–

Is not the limiting by SPO4 caused 
in the proliferation process of XAUT
in the simulation (The value of the
rate equation of clause SPO4 is
confirmed)?
K P value according to XAUT is
reduced, and the limiting by SPO4
is eased.
Confirmation of limiting by alkalinity
–
Is not the limiting by SALK caused 
in the proliferation process of XAUT
in the simulation in facilities of the 
reality though the pH has not
decreased (The value of clause
SALK of the rate equation is
confirmed)?
The SALK density set point of
influent is reviewed.
The KALK value according to XAUT
is reduced, and the limiting by
SPO4 is eased.
Calibration of denitrification
Problem
The NO3-N
density
of
the hypoxia
tank is not
expressible.
Check matter

Confirmation of
circulation liquid
–
DO
Counter measure
density
of
Is the DO density of a conceited
tank corresponding of the results
value?

Refer to "Calibration of nitrification".
511
512
Appendix 8Trouble shooting during charibration step

Confirmation of mixture situation of
reactive tank

–
Is not the backmixing caused
before and behind the hypoxia tank
in the object facilities?

– Is not the inclination of the NO3-N
density etc. made to one division?
The NO3-N  Confirmation of simulation of
density of a
nitrification

conceited
– Is the SNH4 profile of a conceited
tank and the
tank expressible?
treated water
is
not
 Confirmation
of
denitrification
expressible.
situation in conceited tank

–
Do not it excessive/underestimate
the amount of the denitrification in
a conceited tank?

This is reflected in the simulation
when there is a possibility of the
backmixing.
The tank number of partitions of the
hypoxia tank is changed.
Refer to "Calibration of nitrification".
KO2 according to XH is adjusted,
and it ..amount of the denitrification
in a conceited tank.. corresponds in
the results value.
When the DO density of a conceited
tank is unclear, the DO density
along with the calibration of
nitrification is adjusted.
Appendix 8Trouble shooting during charibration stepError! Reference source not found.
Calibration of phosphorus removal
Problem
Check matter
Counter measure
The PO4-P  Confirmation of presence of PAO
density
of
– In the results data, are Rin's 
the
discharge and uptake seen?
reluctance
tank is not
expressible.

When the behavior of PO4-P by
PAO is not seen, the calibration
according
to
the
biological
phosphorus removal is difficult.
Confirmation of NO3-N inlet flow of
sending back dirt origin
–

Is the NO3-N density of the 
sending back dirt corresponding of
the results value?
To be expressible of the NO3-N
density of the sending back dirt, the
final sedimentation pond model is
adjusted.
Confirmation of DO inlet flow of
influent and sending back dirt origin
–
Does the DO density set point of 
influent reflect results?
– Does the DO density set point of
the sending back dirt reflect
results?


Confirmation of mixture situation of
reactive tank

–
Is not the backmixing caused
between reactive tanks in the
downstream?

– Is the tank division of the
reluctance tank appropriate?

Amount of oxygen supply in
pseudoreluctance
tank
and
confirmation of stir situation

SO2 of influent is assumed to be
results
equivalent
(The
data
collection of the addition is done if
necessary).
To be expressible of the DO density
of the sending back dirt, the final
sedimentation pond model is
adjusted.
This is reflected in the simulation
when there is a possibility of the
backmixing.
The tank number of partitions of the
reluctance tank is changed.
Does the blast volume in the
pseudoreluctance
tank 
appropriately reflect results?
– The stir in the pseudoreluctance 
tank is enough.
The blast volume (amount of the
oxygen supply) is adjusted.
The tank number of partitions is
changed.
The division of the small capacity
which influent and the sending back
dirt flow in is installed.
The PO4-P  Confirmation of mixture situation of
density of a
reactive tank

conceited
–
Is
not
the
backmixing
caused
tank and the
between reluctance/hypoxia tank?
treated water
–
Is not the backmixing caused in the
is
not
division of a conceited tank?
expressible.
This is reflected in the simulation
when there is a possibility of the
backmixing.
–
513
514
Appendix 8Trouble shooting during charibration step

Confirmation of limiting by NH4-N
–
Isn't the SNH4 density in the 
simulation remarkable and limiting
[shi] as for proliferation and the
XPP store process of XPAO?
The KNH4 value according to XPAO
is reduced, and the limiting by
SNH4 is eased.
515
Address of thanks
The content of this report is based on the result of investigation "Development of the
activated sludge model (advanced technology development project)" (1999-2001), "Design
using the activated sludge model and development of the maintenance technique"
(2001-2003), and "Design using the activated sludge model and investigation concerning
the applicability of the maintenance technique" (2004-2005) that the JS technology
development part executed. We wish to express our thick gratitude to the parties
concerned all of you in the sewage plant in which it cooperated in the data collection and
hearing in the process of these investigations.
Moreover, describe the use of the result of joint research "Verification ..application..- the
design and the driving management support of development-oxidation ditch method of a
business approach of the activated sludge model" with JS and JFE engineering Ltd. that
executes it clearly in 2001-2004, and in "Chapter 5 case study" and "Discussion case
with the process model of seven appendix oxidation ditch methods" among this reports,
we wish to express our gratitude to the parties concerned all of you of this company.