Journal of Environmental Chemical Engineering 7 (2019) 103091
Contents lists available at ScienceDirect
Journal of Environmental Chemical Engineering
journal homepage: www.elsevier.com/locate/jece
Dynamic modeling of a full-scale anaerobic mesophilic digester start-up
process for the treatment of primary sludge
T
⁎
Wenwen Yanga, Stephanie Younga, , Alex Munozb, Matthew J. Palmarina
a
b
Department of Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, Canada
Stantec Consulting Ltd., Regina, Saskatchewan, Canada
A R T I C LE I N FO
A B S T R A C T
Keywords:
BioWin
Modeling
AMD
Anaerobic sludge digester
Biogas
Methane
The current start-up procedures for anaerobic sludge digesters offer limited early biogas production. This increases the cost of heating since natural gas must be purchased until methane can be produced onsite. In this
research, three simulations were carried out using the BioWin 5.2 software package to expedite the production of
biogas during the start-up of a full-scale anaerobic digester treating primary sludge. The first set of simulations
was conducted to assess the predictive capabilities of the software when used to model this start-up process. The
second set of simulations was conducted to identify which kinetic factors most significantly affect the aforementioned model. The third set of simulations was conducted to evaluate strategies designed to accelerate biogas
production. The start-up strategies were developed for use in wastewater treatment plants with limited availability to seed sludge. These strategies aimed to determine the minimum amount of seed sludge, the initial
sludge feed rate, and the daily sludge feed rate (with and without the addition of a pH control agent), for early
biogas production. BioWin was able to reliably simulate the start-up of a full-scale digester with a relatively good
fit to the plant measured data. The overall mean absolute percentage error was less than 25%, and the overall
Willmott index was greater than 0.82. The results of the sensitivity analysis indicated that the BioWin outputs
were more sensitive to changes in the hydrolysis rate than the acetoclastic maximum specific growth rate or the
acetoclastic anaerobic decay rate.
1. Introduction
Anaerobic sludge digesters are commonly used to manage the biodegradable waste solids produced at wastewater treatment plants
(WWTPs). In medium to large-scale plants, sewage sludge is anaerobically treated to produce biogas, which can be used for the production of
heat and electricity [1,2]. Maximizing biogas production is therefore
important, since wastewater treatment plants face continual pressure to
reduce their operating costs. For this reason, the recovery of biogas
from wastewater treatment processes has been increasing worldwide,
particularly in Europe over the last decade [3–9].
Historically, research related to increasing biogas production has
focused on manipulating operating parameters, the addition of other
waste products, or the pretreatment of sludge prior to anaerobic digestion [10–15]. Research related to modeling has focused on (1) demonstrating that simulated models are accurate enough to describe
biogas production; (2) determining the parameters that can be used to
calibrate those models [11,12,15–20]; and (3) modeling the impact of
nutrient recycling from the anaerobic digesters to the liquid train
⁎
[21,22].
Research related to the start-up of anaerobic digesters has focused
on (1) operating strategies for thermophilic upflow anaerobic sludge
blanket (UASB) reactors treating slaughterhouse wastewater, distillery
wastewater, molasses, animal waste, and municipal solid waste
[23–28]; (2) process description and operating parameters that govern
the start-up process for innovative lab-scale anaerobic reactors for dairy
or synthetic wastewater [29,30]; (3) process description and operating
parameters that govern the start-up process for anaerobic mesophilic
digesters for municipal sludge with very low seed sludge [31]; and (4)
microbial population dynamics during the start-up phase of aerobic
reactors for municipal solid waste or agricultural waste [32–35]. Few
studies have been conducted to model the dynamic behaviour of an
anaerobic digester during the start-up process, and those that do typically used a substantial amount of seed sludge [17,36–38]. To the authors’ knowledge, no studies have been conducted to develop strategies
that accelerate the start-up of a full-scale anaerobic mesophilic digester
for the treatment of municipal primary sludge where the volume of seed
sludge was limited. Consequently, these start-up processes are typically
Corresponding author.
E-mail address: stephanie.young@uregina.ca (S. Young).
https://doi.org/10.1016/j.jece.2019.103091
Received 14 November 2018; Received in revised form 9 April 2019; Accepted 12 April 2019
Available online 16 April 2019
2213-3437/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
2.2. Digester description
not optimized for biogas production.
The start-up process may account for two to three months of no
biogas production every three to five years, depending on the cleaning
frequency of the digester. The operational steps for the start-up of an
anaerobic digester have been discussed in various papers
[31,35,39–42]. The Technical Practice Committee-Subcommittee on
Sludge Digestion [42] recommended a seed quantity equal to 15% of
the digester’s volume, and a sludge feed rate of no more than 10% of the
anticipated maximum daily load for each day. They also recommended
increasing the sludge feed rate by 50–100% of the initial daily feed rate
once gas production reaches approximately 50% of its expected production rate. These guiding principles are not feasible for remote
WWTPs (those located more than 250 km from another WWTP with
anaerobic digesters) due to the cost of hauling seed sludge over long
distances. Without publicly available guiding principles for remote
WWTPs, the digester start-up process is often sub-optimal with significantly delayed biogas production. This increases the operating costs
of the wastewater treatment plant because natural gas must be used as a
replacement energy source for heating other active digesters and
buildings. This effect is felt most strongly in countries operating digesters in cold climates. Consequently, there is a need to develop a
guideline to help these operators start their digesters quickly and efficiently.
A calibrated and validated BioWin model [43] was used in this
study to optimize the start-up of an anaerobic digester for early biogas
production. The optimization was performed by determining the
minimum seed sludge volume, initial sludge feed rate, and daily sludge
feed rate increase, with and without the addition of a pH control agent.
One of the challenges in developing strategies designed to accelerate
digester start-up is the difficulty of conducting pilot-scale studies.
Often, large-scale studies are too technically difficult or expensive to
pursue, limiting much of the research to small-scale studies. Yet, smallscale studies provide limited insight regarding the biological processes
that occur inside large-scale digesters. Thus, they are of limited use to
wastewater treatment plant (WWTP) operators.
Computational modeling has instead been used as an alternative
means of predicting the performance of wastewater treatment processes. There are several simulator packages available on the market for
wastewater treatment. One of the most recognized simulators in North
America is BioWin, developed by EnviroSim Associates Ltd. Several
researchers have shown that BioWin simulations for anaerobic digesters
follow the measured trend quite well while using the software’s default
kinetic and stoichiometric parameters [11,12,15,16,18,19]. For example, a mean absolute percentage error (MAPE) of less than 10% has
been demonstrated for the prediction of biogas production using labscale digesters treating municipal waste activated sludge [11]. The
software, therefore, provides a suitable approach for the simulation of
digester processes.
The Regina WWTP utilizes a two-stage anaerobic mesophilic digestion process to treat the primary sludge removed from the upstream
primary sedimentation process. Two high-rate anaerobic digesters are
coupled in series with a sludge holding tank. The anaerobic digesters,
operated in parallel, are used for sludge digestion and methane gas
production. The digester is a continuous stirred tank reactor (CSTR)
heated to 35 °C and equipped with a Walker mixing system, which
consists of a biogas recirculation compressor, a 1.5 m diameter educator
tube, and eight 50 mm diameter gas lines with diffuser assembly. The
unheated and unmixed sludge holding tank is used to separate the digested sludge into two streams: the thickened sludge, which is pumped
to the belt filter press; and the supernatant/centrate, which flows by
gravity to the grit effluent channel.
2.3. Simulation model description
In general, the computational modeling of anaerobic digesters in
BioWin involves the use of an Activated Sludge/Anaerobic Digestion
Model (ASDM). The ASDM enables an integrated simulation of the
whole plant, where output from the liquid stream model – in the form
of primary/secondary sludge – is input directly into the anaerobic digestion model [16]. This feature of the ASDM facilitates modeling of the
digester without the need to characterize the sludge streams in terms of
carbohydrates, proteins, and lipids, as required by other models. In
addition, the ASDM frees the designer from having to map one model’s
output to another model’s input, which significantly reduces the complexity of building full-plant models, particularly those incorporating
many different processes.
In the anaerobic digestion process, the consortium of microorganisms requires careful volatile fatty acid (VFA)/total alkalinity (TA) ratio
control during the initial establishment phase to maintain a pH above
levels inhibitory to the growth of methanogens. The buffer capacity
regulating this pH is a function of the ammonia, calcium, magnesium,
and bicarbonate concentrations in the anaerobic digester. When the
sludge feed rate is beyond the buffer capacity and the amount of seed
sludge, hydrolysis and fermentation processes begin to produce too
many acids, causing the pH to decrease. The decrease in pH during
start-up is caused by an imbalance in the acidogenic and methanogenic
bacterial populations, where the concentration of methanogenic bacteria is too low in relation to the acidogenic bacteria and no longer
capable of assimilating the excessive production of short-chain fatty
acids. The decrease in pH can also be attributed to the consumption of
alkalinity by carbon dioxide and volatile acids (VA). Due to the partial
pressure of the carbon dioxide gas in the digester, it solubilizes and
forms carbonic acid, which consumes alkalinity [44]. The carbon dioxide concentration in the digester gas is therefore reflective of the
alkalinity requirements. A low pH has a negative impact on the start-up
process because it inhibits the growth of methanogens and further delays methane gas production. When the sludge pH is below 6.2, methanogenic bacteria will no longer function [40], and the digester will
remain in an acidic condition for a longer period unless a pH control
agent is added. Therefore, wastewater treatment practitioners often
suggest maintaining a VFA/TA ratio of less than 0.35 (VFA expressed as
mg L−1 acetic acid and alkalinity as mg L−1 CaCO3) [45,46].
In this study, BioWin was used to simulate the dynamic operation of
a primary treatment plant and the digestion of primary sludge. The
simulations focused specifically on the performance of an anaerobic
mesophilic digester during start-up. To achieve this objective, the
model was calibrated and validated for the dynamic operation of the
plant during 2007 [43]. The calibration results identified volatile suspended solids (VSS) as the most critical parameter. VSS is indirectly
defined by the chemical oxygen demand (COD) of the primary influent,
the removal of total suspended solids (TSS) in the primary settling tank,
the fraction of influent COD composed of non-biodegradable
2. Methodology
2.1. Plant description
For this study, data from the Regina WWTP, located in Regina,
Saskatchewan, Canada, were used to simulate the start-up of an anaerobic digester. The raw sewage flow rate of the Regina WWTP is
70,925 m3 d−1. Regina’s raw sewage is collected at a terminal lift station, where it is screened and pumped to a primary treatment plant. The
primary treatment plant consists of two aerated grit removal tanks and
three primary sedimentation tanks, where grit, suspended solids, and
scum are removed. Primary effluent is then pumped to aerated lagoons.
The primary sludge and scum are collected by a bridge collector and
pumped to two mesophilic anaerobic digesters. Digested sludge is
continuously transferred to a sludge holding tank, and then periodically
pumped to a belt filter press to reduce its water content.
2
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
Fig. 1. Configuration of the simulation model during the start-up of digester 2.
supplementary materials. The primary sludge, digester sludge, and cake
TS concentrations were monitored so that they would remain within the
correct range. Typical values for these parameters are 3.2–3.6%, 5–8%,
and 22–32%, respectively.
The digester start-up processes were monitored by sampling the
digester twice daily at 7:30 AM and 2:00 PM during the acid-forming
phase and once daily during the methane-forming phase. Gas production (Appleton flowmeter model GR series), methane content (Method
2720B), pH, total alkalinity (Method 2320B), total solids (Method
2540 G), and volatile acids [47] were measured for each sample.
particulates (Fup), and the fraction of influent COD composed of slowly
biodegradable particulates (Fxsp) [18]. As soon as the VSS was set to
match the plant measured data, the model predicted values for biogas
production, VFA, and pH fit reasonably well while using default kinetic
parameters. The overall MAPE was less than 16%. Afterwards, the calibrated model was used to simulate the digester start-up process.
2.4. Simulation model configuration
The configuration of the Regina WWTP start-up process is shown in
Fig. 1. The process includes a grit tank, sedimentation tank, digester 1,
digester 2, sludge holding tank, and belt filter press. The element dimensions and operating variables are summarized in Table 1. The
model inputs were as follows: wastewater influent to grit tank, primary
scum to digester 1, and primary scum, bicarbonate, and seed sludge to
digester 2. The model outputs were as follows: primary effluent, primary sludge to lagoons, and cake. It should be noted that during the
start-up of digester 2, digester 1 experienced a major process upset due
to the clogging of its mixing system. Thus, digester 1 was out of service
and the majority of the primary sludge was diverted to the aerated
lagoons. The dynamic simulations presented here encompass the series
of events that occurred since start-up, as listed in Table 2 from April to
September 2012.
2.6. Input parameters
The input wastewater characteristics and wastewater fractions used
for the simulations are summarized in Tables S2 and S3 in the supplementary materials. In addition to the wastewater, scum collected in
the primary sedimentation tanks at the Regina WWTP was also pumped
to the digester and contributed to biogas production. Therefore, scum 1
and scum 2 were considered two additional streams of influent to the
digesters. The wastewater fractions that were adjusted include the
readily biodegradable fraction, Fbs, the acetate fraction, Fac, and the
ammonia fraction, Fna. These values were adjusted because the Regina
force mains from the terminal lift station act as fermenters and thus
increase the Fbs and Fac fractions in the primary influent. It should be
noted that it was considered unacceptable to change the influent Fup
and Fxsp fractions, since it was important to maintain a balance between
all of the wastewater parameters. This was especially the case since the
treatment of the liquid train was not modelled in this study.
2.5. Dynamic simulation of start-up
To ensure a successful simulation, the digester was flushed with
759 m3 of water (two digester volumes) for the first 10 days of the simulation. This step was required to flush out the consortium of bacteria
responsible for anaerobic digestion, and to set the digester alkalinity
and pH to the measured values before sludge seeding. The digester was
then flushed with 2 m3 of water for the next 5 days of the simulation.
This step was required to ensure that the simulation reached stable
values before sludge seeding. The state variables for the flushing water,
sludge seed, and bicarbonate solution, are provided in Table S1 in the
2.7. Base case simulation
The base case was constructed using data from the start-up of an
actual digester at the Regina WWTP. A list of events that occurred
during this start-up is provided in Table 2. The plant measured values
from this start-up are shown with red squares in the simulation results.
Table 1
Element dimensions and operating variables.
Parameter
Grit tank
Volume, m3
Area, m2
Depth, m
Width, m
TSS removal, %
Sludge blanket fraction
Underflow fraction split, m3 d−1
Headspace, m3
Pressure, kPa
Temperature, °C
940
4
4
65
0.1
3.4 × 10−5
Sedimentation tank
Digester (1 or 2)
Sludge holding tank
2106
3.6
3796
405
9.36
3796
405
9.36
65
0.1
4.0 × 10−3
70
0.4
0.1
492
103
35
3
Belt filter press
90
0.2
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
Table 2
List of events that occurred following the start-up of digester 2.
Event
Date
Week
The digester was filled with primary sludge and tertiary effluent. This was done to minimize the presence of dissolved oxygen, COD, sulfate, and
toxins.
The digester was seeded with 40 m3 of seed sludge (1.05% digester volume).
The feed rate was set to 2 m3 d−1 of primary sludge.
The feed rate was increased from 2 m3 d−1 to 16 m3 d−1 at a rate of 1.4 m3 d−1.
The feed rate was halted at 8 m3 d−1 due to a rapid increase in VFA concentration.
The feed rate was stopped as the VFA concentration approached the threshold of irreversible acidic conditions (300 mg L−1 VFA). This threshold
corresponded to a VFA/TA ratio of 0.5 and a pH of 6.4.
The feed rate was set to 2 m3 d−1 of primary sludge.
A pH control agent was added at a dose = 910 kg of sodium bicarbonate dissolved in 10 m3 of solution (1085 mmol L−1 of CO2 and Na).
A pH control agent was added at a dose of 455 kg sodium bicarbonate dissolved in 5 m3 of solution (1085 mmol L−1 of CO2 and Na).
A pH control agent was added at a dose of 182 kg sodium bicarbonate dissolved in 2 m3 of solution (1085 mmol L−1 of CO2 and Na).
A pH control agent was added at a dose of 91 kg sodium bicarbonate dissolved in 1 m3 of solution (1085 mmol L−1 of CO2 and Na).
The feed rate was increased from 2 m3 d−1 to 24 m3 d−1 at a rate of 0.9 m3 d−1.
The feed rate was increased from 24 m3 d−1 to 56 m3 d−1 at a rate of 0.7 m3 d−1.
The gas production reached 500 m3 d−1.
The feed rate was increased from 56 m3 d−1 to 110 m3 d−1 at a rate of 2.0 m3 d−1.
The feed rate was increased from 110 m3 d−1 to 250 m3 d−1 at a rate of 3.1 m3 d−1.
Feed scum was added at a variable flow rate ranging from 5 to 12 m3 d−1.
April 14
1
April 16
April 16
April 17–27
April 28–May 1
May 1–15
1
1
1–2
2–3
3–4
May 16
May 17
May 18
May 28
September 26
May 17–June 10
June 11–24
June 24
June 25–July 21
July 22–September 2
August 27
4
4
4
4
4
4–8
9–10
11
11–14
14–20
19
digester start-up preparations took place. In terms of simulations, digester flushing reset the bacteria populations to the levels measured
before seeding.
(2) Initial feed rate: The initial feed rate was set to 1.7 m3 d−1 after
seeding on day 16. This corresponded to 0.045% of the digester’s volume, or an initial Organic Loading Rate (OLR) of 0.012 kg VS m−3 d−1
or 0.02 kg COD m−3 d−1 (April 17, 2012).
(3) Feed rate increase during the acid-forming phase: The feed
rate increased by 0.75 m3 d−1 for the next 35 d until a feed rate of
28 m3 d−1 was reached on day 51 (May 22, 2012). The increase in feed
rate corresponded to 0.02% of the digester’s volume (or an OLR increase of 0.0054 kg VS m−3 d−1 or 0.0093 kg COD m−3 d−1).
(4) Feed rate increase during the methane-production phase:
The feed rate was increased by 6.35 m3 d−1 for the next 36 d until the
maximum feed rate was reached on day 87 (June 27, 2012). The increase in feed rate corresponded to 0.167% of the digester’s volume (or
an OLR increase of 0.046 kg VS m−3 d−1 or 0.079 kg COD m−3 d−1). A
maximum feed rate of 250 m3 d−1 (or maximum OLR of 1.7 kg VS m−3
d−1 or 3.1 kg COD m−3 d−1) was selected because it corresponded to a
solids retention time of 15 d.
Fig. 2 also presents the estimated food-to-mass ratio, which was
calculated by dividing the primary sludge volatile solids mass rate by
the mass of volatile solids in the digester as estimated by the simulation
model. The food-to-mass ratio increased until it reached a plateau of
0.070 d−1 during the acid-forming phase, and 0.135 d−1 during the
methane-production phase. The food-to-mass ratio then stabilized at
0.115 d−1 after two weeks of reaching a maximum feed rate of 250 m3
d−1.
All of the dynamic simulation results for digester VFA, gas flow rate,
alkalinity, TSS, methane content, and pH are presented in Figs. 3–14. In
Figs. 3–8, the BioWin results for the base case scenario are depicted
with solid red lines. The results for the sensitivity analysis with a
modified acetoclastic maximum specific growth rate are depicted with
solid green lines and full circles. The results for the sensitivity analysis
with a modified acetoclastic anaerobic decay rate are depicted with
dashed blue lines. The results for the sensitivity analysis with a
The base case constructed from this data is shown with a solid red line.
The base case utilized the kinetic parameters set in BioWin by default.
2.8. Sensitivity analysis
A sensitivity analysis for the base case simulation was conducted to
determine which kinetic factors most significantly affected the dynamic
simulation. Methanogen kinetic parameters, such as the hydrolysis rate,
acetoclastic maximum specific growth rate coefficient, and the acetoclastic anaerobic decay rate, were adjusted from their default values of
1.05 d−1, 0.30 d−1, and 0.13 d−1, to 1.36 d−1, 0.31 d−1, and 0.11 d−1,
respectively. It should be noted that the hydrolysis rate constant was
adjusted by modifying the anaerobic hydrolysis factor from its default
value of 0.50 to 0.65. It should also be noted that other studies have
identified anaerobic digestion simulations as being most sensitive to
influent COD, primary settling tank efficiency, influent Fxsp and Fup
fractions [16,18], and the hydrolysis rate constant [17,38].
2.9. Simulation of start-up strategies
Various start-up strategies were simulated with the aim of accelerating the digester start-up process. The results of the simulated strategies were then compared against the base case scenario. An acceptable simulated strategy should also satisfy the following criteria: (1)
the VFA concentration during the acid-forming phase should be less
than 300 mg L−1 – the threshold of an irreversible acidic condition.
This concentration corresponds to a VFA/TA ratio of 0.5; (2) the pH
should be greater than 6.2; and (3) the biogas production should be
equal to 10% of the expected average gas production achieved 35 d
after start-up. It should be noted that these criteria are specific to
Regina’s primary sludge, which contains very low alkalinity. The conditions that produced the most successful strategies are listed in Table 3
and presented in Fig. 2.
The feed rate used for optimizing the digester start-up can be interpreted as four different events:
(1) Digester flushing: This process lasted 15 d during which
Table 3
Dynamic simulation conditions for the optimization of the start-up of an anaerobic digester.
Simulation name
Seed V (as % of digester V)
Sludge feed rate
NaHCO3 addition
NaHCO3 solution V (91 g L−1)
(O) Base case
(A) 80 m3 of seed sludge
(B) Early NaHCO3 addition
1.05% (40 m3)
2.1% (80 m3)
1.05% (40 m3)
Slow: actual plant feed rate
Flow proportional
Flow proportional
Added on day 46
None
Added after seeding
18 m3
0 m3
15 m3
4
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
Fig. 2. Feed rate used for the optimization of the digester start-up.
Table 2. Ideally, VFA should be measured using gas chromatography
since it is the most accurate method. However, this method requires
specialized equipment which was not available at the WWTP. For
convenience, the DiLallo titration method [47] was used, which loses
some accuracy as the bicarbonate concentration in the digester increases while the VFA concentration is less than 250 mg L−1 [48].
These conditions occurred after the addition of bicarbonate on May 17,
18, and 28, and during the methane-forming phase. In addition, the
DiLallo method is susceptible to VA volatilization. Thus, samples low in
VFA generally produced results with high deviation. For VFA results of
less than 180 mg L−1, the modified DiLallo method was used, and the
values were corrected by multiplying them by 1.41 [49].
The performance of a biogas flow meter can be significantly affected
by the moisture content and temperature of the gas. Ideally, the flow
meter should be located downstream of a biogas conditioning system
which chills the gas to reduce its moisture content. BioWin does not
include the moisture content of the mixture and consequently the discrepancy between the simulation results and the plant data could be
attributed to the variable moisture content of the biogas during startup. Biogas flow rate predictions also differed from the values measured
modified hydrolysis rate are depicted with dotted magenta lines. The
plant measured values are shown with red squares. In Figs. 9–14, the
BioWin results for the base case scenario are depicted with solid red
lines; the results for the 80 m3 of seed sludge scenario are depicted with
solid green lines and full circles. The results for the early bicarbonate
addition scenario are depicted with dashed blue lines.
3. Results and discussion
In general, the first set of simulations (base case scenario) indicated
that the model results fit relatively well with the measured values, with
an overall MAPE of 25% and Willmott index of 0.82. The BioWin model
was able to accurately predict pH, alkalinity, and methane content,
with a MAPE of less than 6% for each of these parameters. The base
case simulation was also able to predict the effect of sodium bicarbonate addition on the digester’s pH and alkalinity.
Certainly, there are some discrepancies between the model results
and the measured values on Figs. 6–8. These discrepancies were attributed to the methods used to measure biogas, VFA, and TSS at the
WWTP, and to the significant number of operational events listed in
Fig. 3. Simulation results and sensitivity analysis for digester pH.
5
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
Fig. 4. Simulation results and sensitivity analysis for digester alkalinity.
The simulation for the base case scenario provided a good fit between the predicted methane content and the measured values shown
in Fig. 5. At the plant, methane was introduced into the digester before
start-up, in order to purge air from the digester headspace and to provide the minimum pressure required to operate the gas recirculation
compressor. Consequently, the methane content was relatively high. It
is interesting to note that the model predicted the gradual increase in
methane content after the digester was seeded and fed. After three
weeks of start-up, the simulation also predicted the measured methane
content of 82%. The methane content gradually decreased as the digester was fed primary sludge, due to the generation of carbon dioxide
by the microorganisms during that time. After five weeks, the feed rate
was stopped in response to the rapid increase in the VFA concentration
which approached the threshold limit of 300 mg L−1. Primary sludge
was reintroduced into the reactor on May 16 at a rate of 2 m3 d−1,
followed by the addition of sodium bicarbonate to increase the pH.
During the intervening time, the methane content rose to 80% before
gradually decreasing to 62% for the remainder of the start-up. The
subsequent drop in methane content was attributed to the resumption
of the sludge feed and the release of carbon dioxide from the
after July 9, due to the limited control the plant operators exercised
over the sludge feed mass. This was reflected in the rapid increase in the
measured TSS concentration in the digester. This is because the sludge
feed rate was controlled manually by the plant operators and the total
solids concentration decreased from 4.5% at the beginning of the
pumping cycle to 3.4% by the end of the pumping cycle.
Most importantly, the simulation was able to predict the acidforming phase that occurred during the first four weeks, with an estimated peak VFA concentration of 270 mg L−1 versus a measured VFA
concentration of 410 mg L−1. The simulation was able to predict the
effect caused by halting the sludge feed from April 28 to May 15 on VFA
concentration. Remarkably, neither the digester nor the simulation
reached the point of an irreversible acidic condition even though the
VFA/TA ratio exceeded the threshold value of 0.5. In addition, the increase in VFA corresponded to a decrease in pH as shown in Figs. 3 and
7. This suggests that BioWin was able to successfully represent the
hydrolysis inhibition caused by the high VFA concentration. Researchers using other simulation packages have found that it is necessary to modify the model to improve its predictive ability in this regard
[37].
Fig. 5. Simulation results and sensitivity analysis for digester biogas methane content.
6
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
Fig. 6. Simulation results and sensitivity analysis for digester biogas flow rate.
but had no significant effect on the digester’s alkalinity, biogas flow
rate, or methane content. The default kinetic values in BioWin provide a
relatively accurate prediction of the VFA concentration, without over
predicting the VFA concentration during the methane-forming phase.
They also accurately predicted the likelihood of developing an irreversible acidic condition during the acid-forming phase. These values
may therefore be used when evaluating start-up strategies.
Both of the simulated start-up strategies exhibited different pH, alkalinity, and VFA profiles, as shown in Figs. 9, 10 and 13. From Fig. 13,
a rapid increase in VFA can be seen to occur between April 17, 2012
and May 7, 2012, with a peak concentration occurring approximately
18 d after the introduction of the seed sludge. Strategies A and B, listed
in Table 3, did not shorten the acid-forming phase, but they did provide
better control over the VFA concentration by reducing the peak concentration to below 200 mg L−1. Strategies A and B also maintained the
VFA concentration above 120 mg L−1 throughout the acid-forming
phase. Strategy B provided better control over the alkalinity than
Strategy A. Fig. 10 shows an abrupt change in alkalinity after the addition of bicarbonate. The alkalinity profiles for both strategies converged 13 weeks after start-up. Both strategies produced a higher
neutralization of the sodium bicarbonate. Methane content stabilized
for both the model prediction and the measured values on June 16,
after which the digester consistently produced more than 20 m3 h−1 of
biogas.
The sensitivity analysis indicated that the simulation results were
most significantly affected by the hydrolysis rate and the acetoclastic
anaerobic decay rate. The acetoclastic maximum specific growth rate
coefficient had a less significant influence. The results of the sensitivity
analysis are summarized in Table 4 and depicted in Figs. 3–8. The adjustment of the hydrolysis rate from 1.05 d−1 to 1.36 d−1 resulted in an
accurate prediction of the VFA and alkalinity concentration during the
acid-forming phase, but caused an over prediction of these two parameters during the methane-forming phase. However, the adjustments
made to the hydrolysis rate provided a better fit to the digester’s pH,
biogas, methane, and TSS during the methane-forming phase. The adjustment of the acetoclastic anaerobic decay rate from 0.13 d−1 to 0.11
d−1 resulted in an under prediction of the VFA concentration by 50%
during the acid-forming phase, but provided a better fit to the digester’s
pH. The adjustments made to the acetoclastic maximum specific growth
rate or the acetoclastic anaerobic decay rate had a small effect on VFA,
Fig. 7. Simulation results and sensitivity analysis for digester VFA.
7
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
Fig. 8. Simulation results and sensitivity analysis for digester TSS.
production was achieved within only 12 weeks of start-up. The addition
of bicarbonate was required only when less than 2% of the seed volume
was available. In this case, 1,365 kg d−1 of sodium bicarbonate dissolved in 15 m3 of water was required for a 3,796 m3 digester. This
strategy provided better pH control during the acid-forming phase by
increasing the digester’s alkalinity by 4 mmol/L, which helped to
maintain the VFA/TA ratio below 0.25 mg L−1.
alkalinity profile than the base case scenario.
Both strategies provided better control of the pH profile by maintaining the minimum pH above 6.35 compared to the base case scenario
of 6.25. However, the early addition of bicarbonate in Strategy B provided the best pH control throughout the entire start-up period.
The simulated start-up also indicated that both strategies exhibited
identical methane content, biogas flow rate, and TSS concentration
profiles, as shown in Figs. 11,12, and 14. Starting on May 30, 2012, the
biogas production rate rapidly increased for 6 weeks until reaching a
stable rate of 135 m3 h−1 on July 09, 2012. These strategies reduced the
period of time to gas production by approximately 7 weeks compared to
the base case scenario. Strategies A and B reached a stable methane
content of 62%, 8 weeks after start-up (June 11, 2012). Both strategies
reduced the fluctuations in methane content compared to the base case
scenario. The strategies also increased the TSS in the digester 6 weeks
after start-up (May 30, 2012). The TSS concentration continued to increase until reaching a stable concentration of 21,000 mg L−1 6 weeks
later (July 09, 2012).
From these simulation results, it was concluded that both strategies
could enhance the start-up process of an anaerobic digester. Biogas
3.1. Guiding principles for digester start-up
According to the simulation results depicted in Figs. 9–14, a set of
guiding principles for the optimal start-up of an anaerobic digester were
developed. These principles apply to anaerobic mesophilic digesters
started with limited seed sludge and fed with primary sludge collected
during wastewater treatment.
1 Guiding principles for WWTPs able to haul as much seed sludge
from a neighboring plant as possible up until the end of the methane-forming phase:
a The minimum amount of seed sludge required is 18% of the
Fig. 9. Simulation results of start-up strategies for digester pH.
8
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
Fig. 10. Simulation results of start-up strategies for digester alkalinity.
digester’s volume at 2.0% VS (or 3.64 kg VS m−3).
b The initial sludge feed rate can be determined using a food-tomass ratio of 0.13 d−1 at the start of the methane-production
phase, which corresponds to 1.87% of the digester’s volume at
2.5% VS. This corresponds to an initial OLR of 0.47 kg VS m−3
d−1 or 0.82 kg COD m−3 d−1. These values are about half of the
values suggested by [29,36,41]. These studies were developed
with a seed sludge volume greater than 50% of the digester’s
volume.
c The sludge feed rate can be increased at a rate of 0.15% of the
digester’s volume. This corresponds to an increase in OLR of
0.038 kg VS m−3 d−1 or 0.066 kg COD m−3 d−1.
d The maximum daily feed rate at the end of the methane-production phase should not exceed the feed rate estimated by dividing
the digester’s volume by the recommended solids retention rate of
15 d. This corresponds to a maximum OLR of 1.8 kg VS m−3 d−1
or 3.1 kg COD m−3 d−1.
e Gas production can be increased by the addition of scum after the
digester has reached its maximum design feed rate. This is because the early addition of scum can increase the VFA/TA ratio
above the maximum threshold of 0.5.
2 Guiding principles for WWTPs able to haul as much seed sludge
from a neighboring plant as possible to avoid the acid-forming
phase:
a The minimum amount of seed sludge required is 13% of the digester’s volume at 2.0% VS (or 2.6 kg VS m−3).
b The initial sludge feed rate can be determined using a food-tomass ratio of 0.07 d−1 at the start of the methane-production
phase, which corresponds to 0.720% of the digester volume at
2.5% VS. This corresponds to an initial OLR of 0.18 kg VS m−3
d−1 or 0.31 kg COD m−3 d−1.
c The sludge feed rate can be increased at a rate of 0.12% of the
digester’s volume. This corresponds to an increase in OLR of
0.030 kg VS m−3 d−1 or 0.052 kg COD m−3 d−1 for 10 days.
d The sludge feed rate can be increased at a rate of 0.15% of the
digester’s volume during the methane-production phase. This
corresponds to an increase in OLR of 0.038 kg VS m−3 d−1 or
0.066 kg COD m−3 d−1.
3 Guiding principles for WWTPs unable to haul a substantial amount
of seed sludge from a neighboring plant:
Fig. 11. Simulation results of start-up strategies for digester methane content.
9
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
Fig. 12. Simulation results of start-up strategies for digester biogas flow rate.
Fig. 13. Simulation results of start-up strategies for digester VFA.
c The initial sludge feed rate and sludge feed rate increase are listed
above (guideline 3b–3d).
a The minimum amount of seed sludge required is 2.1% of the digester’s volume at 2.02% VS (or 0.43 kg VS m−3) without the
addition of a pH control agent.
b The initial sludge feed rate should be less than 0.045% of the
digester’s volume at 2.7% VS during the acid-forming phase. This
corresponds to an initial OLR of 0.012 kg VS m−3 d−1 or 0.021 kg
COD m−3 d−1.
c The sludge feed rate can be increased at a rate of 0.02% of the
digester’s volume during the acid-forming phase. This corresponds to an increase in OLR of 0.0053 kg VS m−3 d−1 or
0.0091 kg COD m−3 d−1 for the first 35 days.
d The sludge feed rate can be increased during the methane-production phase as listed above (guideline 2c and 2d).
4 Guiding principles for WWTPs unable to haul a large amount of seed
sludge from a neighboring plant, with the addition of a pH control
agent:
a The minimum amount of bicarbonate is 0.359 kg NaHCO3 per m3
of the digester’s volume at 91 g NaHCO3 L−1.
b The minimum amount of seed sludge required is 1.05% of the
digester’s volume at 2.02% VS (0.21 kg VS m−3 d−1).
4. Conclusions
This study demonstrated that it is feasible to predict the performance of an anaerobic sludge digester during its start-up using BioWin
5.2. Different indexes were used to measure the agreement between the
measured and predicted data, including R-squared, multiple R,
Willmott index, and MAPE. These indexes indicate that the BioWin
results fit reasonably well with the measured values with an overall
MAPE of 25% and Willmott index of 0.82. The BioWin model was able
to accurately predict changes in alkalinity, methane content, and pH
with a MAPE of less than 6%. The ability of the BioWin model to
produce accurate predictions depended on the correct specification of
the wastewater fractions and kinetic parameters. This research identified that the anaerobic hydrolysis rate and the acetoclastic anaerobic
decay rate have a significant effect on the predicted VFA concentration
and pH.
The strategies identified in this research may be used to determine
10
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
Fig. 14. Simulation results of start-up strategies for digester TSS.
Table 4
Sensitivity analysis for anaerobic digestion in BioWin.
Parameter
Sensitivity index
pH
Alkalinity
VFA
Gas
Methane content
TSS
Overall
Default kinetic values
Observations
Multiple R
R Square
Willmott index
MAPE
Multiple R
R Square
Willmott index
MAPE
Multiple R
R Square
Willmott index
MAPE
Multiple R
R Square
Willmott index
MAPE
146
0.70
0.48
0.70
1.9
0.64
0.41
0.75
1.7
0.68
0.47
0.70
1.8
0.66
0.44
0.70
1.7
146
0.70
0.96
0.99
6.7
0.64
0.92
0.84
23.2
0.68
0.96
0.99
6.7
0.66
0.96
0.99
6.6
146
0.12
0.01
0.42
64.4
0.07
0.01
0.46
87.3
0.08
0.01
0.39
59.1
0.02
0.00
0.37
46.8
146
0.96
0.91
0.97
51.4
0.95
0.90
0.94
72.9
0.96
0.91
0.97
51.2
0.96
0.91
0.97
50.2
21
0.91
0.83
0.85
5.9
0.90
0.80
0.84
5.8
0.91
0.83
0.85
5.9
0.91
0.83
0.85
5.9
45
0.99
0.98
0.97
21.7
0.98
0.97
0.98
12.6
0.99
0.98
0.97
21.8
0.99
0.98
0.97
21.9
0.73
0.70
0.82
25.3
0.70
0.67
0.80
33.9
0.72
0.69
0.81
24.4
0.70
0.68
0.81
22.2
Hydrolysis rate
Acetoclastic maximum specific growth rate
Acetoclastic anaerobic decay rate
References
the appropriate initial sludge feed rate for the available amount of seed
sludge, as well as the sludge feed rate increment to use during each
phase of the start-up process. They also provide a guideline for the
amount of pH control agent to be used for a more stable start-up.
Overall, the developed strategies reduced the digester start-up time and
expedited the production of methane gas. It should be emphasized that
the above conclusions are limited to anaerobic mesophilic digesters fed
with primary sludge. The results may be extrapolated to include
anaerobic digesters fed with waste activated sludge or a mixture of
primary and waste activated sludge. However, in these cases, careful
consideration should be given to the sludge feed rate.
[1] L.D. Nghiem, K. Koch, D. Bolzonella, J.E. Drewes, Full scale co-digestion of wastewater sludge and food waste: bottlenecks and possibilities, Renew. Sustain.
Energy Rev. 72 (2017) 354–362.
[2] Y. Shen, J.L. Linville, M. Urgun-Demirtas, M.M. Mintz, S.W. Snyder, An overview of
biogas production and utilization at full-scale wastewater treatment plants
(WWTPs) in the United States: challenges and opportunities towards energy-neutral
WWTPs, Renew. Sustain. Energy Rev. 50 (2015) 346–362.
[3] A. Mulchandani, P. Westerhoff, Recovery opportunities for metals and energy from
sewage sludges, Bioresour. Technol. 215 (2016) 215–226.
[4] L.D. Nghiem, T.T. Nguyen, P. Manassa, S.K. Fitzgerald, M. Dawson, S. Vierboom,
Co-digestion of sewage sludge and crude glycerol for on-demand biogas production,
Int. Biodeterior. Biodegrad. 95 (2014) 160–166.
[5] C. Sawatdeenarunat, D. Nguyen, K.C. Surendra, S. Shrestha, K. Rajendran,
H. Oechsner, L. Xie, S.K. Khanal, Anaerobic biorefinery: current status, challenges,
and opportunities, Bioresour. Technol. 215 (2016) 304–313.
[6] N. Scarlat, J.-F. Dallemond, F. Fahl, Biogas: developments and perspectives in
Europe, Renew. Energy 129 (Part A) (2018) 457–472.
[7] M. Torrijos, State of development of biogas production in Europe, Procedia Environ.
Sci. 35 (2016) 881–889.
[8] N.T. Tuyet, N.P. Dan, N.C. Vu, N.H. Trung, B.X. Thanh, H. De Wever, M. Goemans,
L. Diels, Laboratory-scale membrane up-concentration and co-anaerobic digestion
for energy recovery from sewage and kitchen waste, Water Sci. Technol. 73 (2016)
597–606.
[9] R. Wickham, B. Galway, H. Bustamante, L.D. Nghiem, Biomethane potential valuation of co-digestion of sewage sludge and organic wastes, Int. Biodeterior.
Biodegradation 113 (2016) 3–8.
[10] B.R. Dhar, E. Elbeshbishy, H. Hafez, G. Nakhla, M.B. Ray, Assessing the optimum
Acknowledgements
The authors would like to acknowledge Mr. Jerry Cheshuk, Regina
Wastewater Treatment Plant, for providing the laboratory data required
for the BioWin simulation.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in the
online version, at https://doi.org/10.1016/j.jece.2019.103091.
11
Journal of Environmental Chemical Engineering 7 (2019) 103091
W. Yang, et al.
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30] C. Benito, Startup and Stabilization of Anaerobic Membrane Bioreactor at Ambient
Temperature (Master’s thesis), University of Lund, Sweden, 2015.
[31] M. Hatzigeorgiou, B. Owsenek, T. Alkema, R. Sieger, K. Pallansch, Startup of
anaerobic mesophilic digesters, Proceedings of the Water Environment Federation
(2006) WEFTEC 2006.
[32] R. Alcantara-Hernandez, S. Carlos-Pinedo, N. Tas, A. Duran-Moreno, L.I. Flacon,
Microbial dynamics in anaerobic digestion reactors for treating organic urban residues during the start-up process, Lett. Appl. Microbiol. 64 (2017) 438–445.
[33] X. Goux, M. Calusinska, M. Fossépré, E. Benizri, P. Delfosse, Start-up phase of an
anaerobic full-scale farm reactor – appearance of mesophilic anaerobic conditions
and establishment of the methanogenic microbial community, Bioresour. Technol.
212 (2016) 217–226.
[34] M.E. Griffin, K.D. McMahon, R.I. Mackie, L. Raskin, Methanogenic population dynamics during start-up of anaerobic digesters treating municipal solid waste and
biosolids, Biotechnol. Bioeng. 57 (1998) 342–355.
[35] H. Yu, Q. Wang, Z. Wang, E. Sahinkaya, Y. Li, J. Ma, Z. Wu, Start-up of an anaerobic
dynamic membrane digester for waste activated sludge digestion: temporal variations in microbial communities, PLoS One 9 (4) (2014) 1–9.
[36] S. Jablonski, M. Lukaszewicz, Mathematical modeling of methanogenic reactor
start-up: importance of volatile fatty acids degrading population, Bioresour.
Technol. 174 (2014) 74–80.
[37] A. Normak, J. Suurpere, I. Suitso, E. Jogi, E. Kokin, P. Pitk, Improving ADM1 model
to simulate anaerobic digestion start-up with inhibition phase based on cattle
slurry, Biomass Bioenergy 80 (2015) 260–266.
[38] B.-H. Zhao, Y. Mu, F. Dong, B.-J. Ni, J.-B. Zhao, G.-P. Sheng, H.Q. Yu, Y.-Y. Li,
H. Harada, Dynamic modeling the anaerobic reactor startup process, Ind. Eng.
Chem. Res. 49 (2010) 7193–7200.
[39] E.A. Cassell, C.N. Sawyer, A method of starting high-rate digesters, Sewage Ind.
Waste. 31 (2) (1959) 123–132.
[40] J.W. Filbert, Procedures and problems of digester start-up, J. Water Pollut. Control
Fed. 39 (3) (1967) 367–372.
[41] A. Schnürer, I. Bohn, J. Moestedt, Protocol for start-up and operation of CSTR
biogas processes, Hydrocarbon and Lipid Microbiology Protocols, Protocol
Handbooks, (2017) 171-200.
[42] Technical Practice Committee-Subcommittee on Sludge Digestion, Anaerobic
sludge digestion-manual of practice 16, Water Pollut. Control Fed. 38 (12) (1966)
1925–1943.
[43] W. Yang, BioWin Modeling of the Startup of an Anaerobic Digester Used in
Wastewater Treatment Plants (Master’s thesis), University of Regina, Saskatchewan,
2014.
[44] B.E. Rittmann, P.L. McCarty, Environmental Biotechnology: Principles and
Applications, 3rd ed, McGraw-Hill, 2001.
[45] L. Appels, J. Baeyens, J. Degreve, R. Dewil, Principles and potential of the anaerobic
digestion of waste-activated sludge, Prog. Energy Combust. Sci. 34 (2012) 755–781.
[46] G. Tchobanoglous, H.D. Stensel, R. Tsuchihashi, Wastewater Engineering:
Treatment and Reuse, 5th ed, Metcalf and Eddy | AECOM., New York, NY, 2014.
[47] R. DiLallo, O.E. Alberston, Volatile acids by direct titration, J. Water Pollut. Control
Fed. 33 (1961) 356–365.
[48] V. Mota, F. Santos, T. Araujo, M. Amaral, Evaluation of titration methods for volatile fatty acids measurements: effect of the bicarbonate interference and feasibility
for the monitoring of anaerobic reactors, Water Pract. Technol. 10 (3) (2015)
486–495.
[49] P.F.F. Cavalcanti, A. van Haandel, Comparison between the titration methods of
Kapp and DiLallo for the determination of alkalinity and VFA, Revista Engenharia
Sanitária e Ambiental 5 (1) (2000) 47–52.
SRT for anaerobic digester with sludge pre-treatment for sulfide control,
Proceedings of the Water Environment Federation (2013) WEFTEC 2013.
B.R. Dhar, E. Elbeshbishy, H. Hafez, G. Nakhla, B.R. Madhumita, Thermo-oxidative
pretreatment of municipal waste activated sludge for volatile sulfur compounds
removal and enhanced anaerobic digestion, Chem. Eng. J. 174 (2011) 166–174.
E. Elbeshbishy, A. Nakevsku, H. Hafez, M. Ray, G. Nakhla, Simulation of the impact
of SRT on anaerobic digestibility of ultrasonicated hog manure, Energies 3 (2010)
974–988.
J.H. Long, T.N. Aziz, F.L. de los Reyes III, J.J. Ducoste, Anaerobic co-digestion of
fat, oil, and greases (FOG): a review of gas production and process limitations,
Process Saf. Environ. Prot. 90 (2012) 231–245.
S.-E. Oh, S. Van Ginkel, B.E. Logan, The relative effectiveness of pH control and heat
treatment for enhancing biohydrogen gas production, Environ. Sci. Technol. 37 (22)
(2003) 5186–5190.
W.J. Parker, R. Jones, H. Zhu, S. Murthy, Integrated modeling of thermal hydrolysis
pretreatment and anaerobic digestion of wastewater treatment sludges, Proceedings
of the Water Environment Federation (2009) WEFTEC 2009.
D. Dursun, J. Jimenez, J. Bratby, Integrating anaerobic digester with liquid stream
treatment: modeling approach, Proceedings of the Water Environment Federation
(2011) WEFTEC 2011.
I. Hamawand, C. Baillie, Anaerobic digestion and biogas potential: simulation of lab
and industrial-scale processes, Energies 8 (2015) 454–474.
R.M. Jones, P.L. Dold, Misconceptions and realities of anaerobic digestion process
performance, Proceedings of the Water Environment Federation (2013) WEFTEC
2013.
E. Liwarska-Bizukojc, D. Olejnik, R. Biernacki, S. Ledakowicz, Calibration of a
complex activated sludge model for the full-scale wastewater treatment plant,
Bioprocess Biosyst. Eng. 34 (2011) 659–670.
M.A. Sedran, A.S. Mehrotra, A.B. Pincince, The danger of uncalibrated activated
sludge simulation packages, Proceedings of the Water Environment Federation
(2006) WEFTEC 2006.
J.T. Kraemer, D.M. Bagley, Simulation of the impact of higher ammonia recycle
loads caused by upgrading anaerobic digesters, Water Qual. Res. J. Canada 40 (4)
(2005) 491–499.
R. Jones, I. Takacs, Modeling the impact of anaerobic digestion on the overall
performance of biological nutrient removal wastewater treatment plants,
Proceedings of the Water Environment Federation (2004) WEFTEC 2004.
H.H.P. Fang, I.W.C. Lau, Startup of thermophilic (55 °C) UASB reactors using different mesophilic seed sludges, Water Sci. Technol. 34 (1996) 445–452.
R. Fia, E. Pereira, F. Fia, D. Enboaba, E. Gomes, Start-up of anaerobic reactors for
slaughterhouse wastewater treatment, J. Braz. Assoc. Agric. Eng. 35 (2) (2015)
331–339.
M.M. Ghangrekar, S.R. Asolekar, S.G. Joshi, Characteristics of sludge developed
under different loading conditions during UASB reactor start-up and granulation,
Water Res. 39 (2005) 1123–1133.
K.V. Lo, C. Chiu, P.H. Liao, Anaerobic treatment of molasses wastewater I. Start-up
and steady-state performance, Can. Agric. Eng. 33 (1991) 295–301.
K.Y. Show, J.H. Tay, L.M. Yang, Y. Wang, C.H. Lua, Effects of stressed loading on
startup and granulation in upflow anaerobic sludge blanket reactors, J. Environ.
Eng. ASCE 130 (2004) 743–750.
K.Y. Show, Y. Wang, S.F. Foong, J.H. Tay, Accelerated startup and enhanced
granulation in upflow anaerobic sludge blanket reactors, Water Res. 2004 (38)
(2004) 2293–2304.
R. Alkarimiah, S. Mahat, A. Yuzir, M. Din, S. Chelliapan, Performance of an innovative multistage anaerobic reactor during start-up period, Afr. J. Biotechnol. 10
(54) (2011) 11294–11302.
12