Index
1. UPC and my Thesis work presentation
2. Complex distillation columns with energy
savings
3. The work
3.1 Design
3.2 Dynamic aspects
3.3 Control
4. Conclusions and future work
Universitat Politècnica de
Catalunya (UPC).
• Founded in 1971, it has:
–9 schools and faculties (Industrial Engineering)
–8 technical colleges
–7 associate schools
–38 departments (Chemical Engineering)
–21 diplomas, 8 degrees: 30.000 students last year
–44 Ph.D. programs: 149 thesis during 1996-1997
–budget 1998: 260,00 M$can
The Chemical Engineering
Department
• 90 teachers and researchers
• 95 Ph.D. students
• Main goals:
– chemical process optimisation, security and
accident modelisation, reactors, water
technology, fluid-particle systems, alimentary
technology, waste treatment, contaminants
analysis, environmental studies, molecular
engineering, polymer synthesis and structure.
The thesis work
• Title: Energy optimisation in complex
distillation columns
• Objective: study complex designs for
energy savings already described to bring
them closer to implementation
– design, operation and control
• Status:
– Petlyuk Column: centre of my studies till now
– some design, some control, some operation
– 60% of work done
The Petlyuk Column origin
• Wright (1949) proposed a promising design
alternative for separating ternary mixtures
• Petlyuk (1965) studied the scheme
theoretically
• Most important literature since Petlyuk:
Fidkowski and Krolikowski / Glinos and
Malone / Triantafyllou and Smith / Kaibel /
Wolf and Skogestad
The Petlyuk Column structure
A
A + B
A B C
B
B + C
PREFRACTIONATOR
C
MAIN C OLUM N
Conventional designs
1
1
2
4
6
8
10
12
S2
4
1
6
S1
8
10
14
16
T1
17
S3
16
20
T2
S5
17
S3
S4
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
12
14
T1
2
S4
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
S2
S1
1
T2
20
S5
INDIRECT TRAIN
DIRECT TRAIN
Distillation process in a Petlyuk
Column
Petlyuk feed column
1
0,9
0,9
0,8
0,8
0,7
0,7
molar fraction
1
0,6
0,6
X-benzene
0,5
X-toluene
0,4
X-oxylene
0,3
Y-benzene
0,2
Y-toluene
0,2
Y-toluene
Y-oxilene
0,1
Y-oxilene
0,1
X-benzene
0,5
X-toluene
0,4
X-oxylene
0,3
Y-benzene
0
tray number
17
16
15
14
13
12
11
9
10
8
7
6
5
4
3
2
0
1
molar fraction
First column direct train
1
2
3
4
5
6
tray number
7
8
9
10
Petlyuk Column features
• No more than one component is stripped out
in each section, key components A and C:
– reversibility during mixing of streams in feed
location (pinch zone)
– no remixing effect
• Thermal coupling
– no thermodynamic losses in heat exchanges of
prefractionator reboiler and condenser
– reversibility during mixing of streams at ends
of columns
Reported 30% of energy savings
The Divided Wall Column
Thermodynamical equivalence in only one shell
A
LI QUID
SPLIT
B
ABC
VAPOR
SPLIT
C
Extension to other multicomponent distillations
A
B
ABCD
C
D
Distinguishing features
• n(n-1) sections required for an n-component
separation
• Only one condenser and one reboiler
• Key components in each column are not
two adjacent ones, but the ones with
extreme volatility
Design of the Petlyuk Column
Work presented at AIChE Meeting, Los Angeles, 1997
• Degrees of freedom
– design: number of trays per section and feed
trays
– operation: flowrates or flowrate ratios. Two
extra DOF used to optimise the process
• Main design decision: separation to be
carried out by the prefractionator.
– Two levels of specification:
• two specified variables
• three specified variables
Short-cut methods facing
multicomponent systems
Most of numerical
correlations used by shortcut methods solve
distillation columns based
on required recoveries of
just key components
Ability to play
only with two
recoveries
Importance of all three prefractionator recoveries over the
global economic performance of a complex distillation column
Proposed design heuristic method
Balance between prefractionator and main column and
between upper and down main column
• Decision of A and C recoveries. Design
following short-cut indications (simplified
model). Rigorous simulations.
• Change of feed tray to minimise the larger
vapour flow between flows at COL2 bottom
and COL3 top
• Repeat till vapour flows are equal
• Change recoveries of A and C
Simplified model of the Petlyuk Column
Work presented at Congreso Mediterraneo de Ingenieria Quimica, 1996
A
A+B
ABC
COL2
B
B
B+C
PREFRACTIONATOR
COL3
C
Determination of mixtures that take
major profit of the Petlyuk Column
• Case study with pro-II simulations:
– Studied separations:
• different quantities of B in feed (+33%, 33%, -33%)
• different Easy Separation Index (<1, 1, >1)
– Savings compared to the best train of columns:
• more B in feed, more savings (23%, 20 %, 14%)
• more savings when ESI is close to 1 (34%)
Dynamic behaviour
• SPEEDUP model
• Neural Network simulation
• MATLAB model
– linearised model: transfer functions
• Model approximations
– constant relative volatility throughout the
column, equimolar overflow, no heat
losses equilibrium in each plate, constant
pressure, liquid and vapour flow
dynamics, tray hydraulics...
Dynamic features
• Interaction
• Speed, magnitude and shape of response: stiff
"MIDDLE_PROD_ "X_OUT1(1)
"-
sim2
"MIDDLE_PROD_ "X_OUT1(2)
"-
1
0.9
"MIDDLE_PROD_ "X_OUT1(3)
"-
0.8
"MIX_LIQ_FEED. "X_IN1(1) ""MIX_LIQ_FEED. "X_IN1(2) "-
0.6
"MIX_LIQ_FEED. "X_IN1(3) "-
0.5
0.4
"REBOIL. "X_OUT(1) "-
0.3
"REBOIL. "X_OUT(2) "-
0.2
0.1
"REBOIL. "X_OUT(3) "-
0
-0.1
32.3
29.8
27.3
24.8
22.3
19.8
17.3
14.8
12.3
9.8
7.4
4.9
2.4
"SPLIT_TANK. "X_OUT1(1) "-
0
composicions
0.7
"SPLIT_TANK. "X_OUT1(2) "-
temps
"SPLIT_TANK. "X_OUT1(3) "-
Neural Network simulation - MPC?
Work presented at III Congresso de Redes Neuronais, 1997
• The used NN
– three layer
– feedforward with autoregressive neurones
connected to the output
• Sampling frequency from lowest time
constant of all outputs: C in feed to B in
sidestream, 6 min
• Training of the NN
– PRBS signal applied to all inputs (until 3
manipulated variables and 3 disturbances)
NN forecasting example
1.00E+00
9.80E-01
9.60E-01
9.40E-01
9.20E-01
9.00E-01
8.80E-01
8.60E-01
8.40E-01
8.20E-01
8.00E-01
20000 epochs
Netw ork output:
past/future
3, 6, 1 neurons
SPEEDUP data
Sigm., linear
autoregressive param. = 1
877
804
731
658
585
512
439
366
293
220
time intervals of 0.1 hour
9.60E-01
bottom product purity
147
74
shift param. = 1
1
bottom product purity
902 patterns
9.55E-01
9.50E-01
Netw ork output:
past/future
9.45E-01
9.40E-01
SPEEDUP data
9.35E-01
9.30E-01
9.25E-01
9.20E-01
863 868 873 878 883 888 893 898 903
tim e intervals of 0.1 hour
9.45E-01
0.355
0.345
9.35E-01
0.34
9.30E-01
0.335
0.33
9.25E-01
0.325
9.20E-01
0.32
9.15E-01
0.315
1 2 3 4 5 6 7 8 9 10 11 12 13 14
tim e intervals of 0.1 hour
input profile for forecasting
bottom product purity forecast
0.35
9.40E-01
Neural netw ork forecast
SPEEDUP data
Molar fraction of A in feed
Molar fraction of B in feed
Molar fraction of C in feed
Control problem
• Control product compositions
– 3 composition specifications (holes in some
operation regions)
– inventory control
•
•
•
•
Control to minimise energy consumption
Robustness?
Linearity far from nominal steady state?
Disturbances rejection and set point changes
achievement?
Descentralised control
Work presented at CHISA ’98
• Skogestad: acceptable control seems feasible
(no energy control, linear model)
• Study of descentralised control with MATLAB
models
Tyreus method:
– Design and test inventory control
• 7 control valves - 5 steady state DOF = 2 inventory loops
– Design composition control
– Design optimisation control (energy minimisation)
Diagonal control for the Petlyuk Column
Control of A, B, and C purity:
• For each inventory control (D-B, L-B, D-B)
– Transfer function
– MRI, CN, Intersivity Index
• For the decided control structure: D,B; L, S, V
– Chose one pairing
• For the decided pairing: L-A, S-B, V-C
– BLT tuning procedure:
• controller gains: 0.74, -2.33, 0.65
• controller reset times: 14.16 for all loops
(L-A, S-B, V-C) Controlled
system MATLAB simulation
Set point change in A purity example
0.995
0.99
0.985
0.98
0.975
0.97
0.965
0
500
1000
1500
2000
2500
3000
3500
4000
No instability problem was found, better tunning can be achieved
MIMO feedback control
• Controllability analysis in frequency domain
–
–
–
–
bandwidth
RGA, CN, singular values
stability (Nyquist plots)
poles and zeros
• MIMO robustness
Self-optimising control
Work to be presented at PRES, 1999
• Published works from NTNU
• Problem: once the minimum is located, control
is required to keep the operating point at the
minimum when disturbances are loaded
• Solution: Improve robustness with feedback
control to careful selected outputs
• Require: measurable output variable which
when kept constant keeps minimum energy
consumption (self-optimising control)
Studied controlled variables for
indirect energy minimisation
• For each candidate, sensitivity to
disturbances in feed composition and liquid
fraction is computed:
–heavy key fraction in vapour leaving top of
prefractionator
–middle component recovery in prefractionator
–main column flow balance
–Temperature profile symmetry
–others
• The best?
Conclusions
•
•
•
•
•
•
A design method
Mixture characterisation for Petlyuk Column
Dynamic features
NN are able to simulate the Petlyuk Column
Diagonal control works in our simplified model
Self-optimising control fits the Petlyuk Column
Future work
• Better characterisation of mixtures fitting
different complex distillation columns
• Other designs to compare with. Energy
integration
• Robustness for different nominal steady-states
• HYSYS dynamic rigorous simulations
• Design and control together
• NN simulation into Model Predictive Control