OPTIMAL DESIGN OF
INTEGRATED SEPARATION SYSTEMS
Zsolt Fonyo
Department of Chemical Engineering
Budapest University of Technology and Economics
and
Research Group of Technical Chemistry
Hungarian Academy of Sciences
23 March 2004, Trondheim, Norway
Research activities
• DISTILLATION AND ABSORPTION
•
•
•
•
•
Determination of Vapour-Liquid Equilibria and design of Packed Col
umns.
Development on distillation and absorption technologies
Modelling and calculation of thermodynamic properties
Modelling of batch and continuous countercurrent separation processes
• EXTRACTION AND LEACHING
•
•
Kinetics of Soxhlet-type and Supercritical Solid-Liquid Extraction of Natural Products.
Mathematical modelling and optimization of the process.
Supercritical fluid extraction equipment and R&D capabilities
• REACTIONS
•
Mathematical modelling of residence time distribution and chemical reactions
• MIXING OF LIQUIDS
• PROCESS DESIGN AND INTEGRATION
•
•
•
•
•
•
•
•
•
Feasibility of distillation for non/ideal systems
Hybrid separation systems
Reactive distillation
Design of Energy Efficient Distillation Processes
Energy integrated distillation system design enhanced by heat pumping
and dividing wall columns
Energy recovery systems
A global approach to the synthesis and preliminary design of
integrated total flow-sheets
Process Integration in Refineries for Energy and Environmental
Management
• CONTROL AND OPERABILITY
• Assessing plant operability during process design
• Transformation of Distillation Control Structures
• ENVIRONMENTALS
• Waste reduction in the Chemical Industry
•
•
•
•
•
CLEAN TECHNOLOGIES
Membrane separations
Cleaning of waste water with physico-chemical tools.
Solvent recovery
Synthesis of mass exchange networks with mixed integer nonlinear
programming
• Economic and controllability study of energy integrated separation
schemes
• Process synthesis of chemical plants
Analysis of energy integrated separations
(distillation based)
Synthesis of Mass Exchange Networks
Using Mathematical Programming
Solvent Recovery from Non-Ideal Quaternary
Mixtures with Extractive HeterogeneousAzeotropic Distillation
23 March 2004, Trondheim, Norway
Integrated process design
• Challenge in chemical engineering
• Economical and environmental aspects
• Heat integration (HEN) & mass integration
(MEN)
• Several synthesis strategies
• The design needs CAPE
Analysis of energy integrated separations
(distillation based)
(Economic and Controllability Analysis
of Energy-Integrated Distillation Schemes)
Budapest University of Technology and Economics
Department of Chemical Engineering
Aims of the work
• Separation of ternary mixture by energyintegrated distillation schemes
• Optimization of the schemes
• Economic evaluation and comparison of the
schemes
• Optimal schemes are investigated for
controllability features
• Estimation of the environmental effects
Case study
•Mixture: (Ethanol – n-Propanol– n-Butanol)
•Feed compositions:
Case 1: (0.45/ 0.10/0.45)
Case 2: (0.33/ 0.33/0.33)
Case 3: (0.10/ 0.80/0.10)
•Product purity specifications: 99 mole %
C
*Case 1
*Case 2
*Case 3
A
B
Composition Triangle
Techniques and assumptions
HYSYS Process Simulator
Modeling of the
schemes
Steady-state
simulation
Dynamic
simulation
•NRTL and UNIQUAC activity models are used
•The impurities in B product stream are symmetrically distributed.
•Total condensers and reboilers are used.
•Exchange min. approach temperature (EMAT)=8.5 oC.
•Valve trays (Glitsch type) are used as column internals.
Conventional distillation schemes
B
A
L1
ABC
L2
D1
D2
Direct sequence
Col.2
Col.1
BC
Base case for comparison
Q2
C
B2
A
Col.2
Col.1
ABC
AB
C
Indirect sequence
B
Conventional-heat integration
Q2
B
A
L1
`AB C
L2
D1
Col.1
D2
Col.2
Forward heat integration
direct sequence (DQF)
BC
B2
C
B
A
L1
Col.1
L2
D1
R1 =L1 /D1
D2
Backward heat integration
direct sequence (DQB)
Col.2
ABC
V2
Q2
C
BC
B2
BR2 =V2 /B2
Thermo-coupling
L
V12
A
D
R=L/ D
L21
Col.2
Col.1
ABC
S
B
SR
V 21
L 12
V
Q
C
BR=V/ B
B
Petlyuk column (SP)
Sloppy separation sequences
Q
L
D
A
AB
Col.2
Col.1
ABC
S
B
BC
B
C
Forward heat integration (SQF)
A
L
D
AB
Col.2
Col.1
ABC
S
B
BC
Q
C
B
Sloppy separation sequence,
backward heat integration (SQB)
The objective function is total annual cost (TAC),
which includes annual operating and capital costs.
Utilities cost data
High utility prices (a)
Low utility prices
Utility
LP-steam
MP-steam
Cooling
water
Electricity
(b)
Temperature
(C)
160
184
30-45
Price
($/ton)
17.7
21.8
0.0272
Temperature
(C)
160
184
30-45
Price
($/ton)
6.62
7.31
0.0067
--------
0.1 $/kwh
---------
0.06
$/kwh
(a) Based on European prices
(b) Based on U.S. prices
Estimation of capital cost
Douglas, J. M., Conceptual design of chemical
processes, McGraw-Hill Book Company
Marshall & Swift index: (1056.8/280)
Project life: 10 years
Sizing of columns and heat exchangers are estimated by
HYSYS flowsheet simulator
Optimal fractional recovery of the middle component
Comparison of theoretical and optimal fractional recovery of the middle
component
Schemes
Case 1,
Case 2,
Case 3,
(0.45/0.10/0.45)
(0.33/0.33/0.33)
(0.10/0.80/0.10)
*
*

o

o
*
o
Petlyuk
0.36
0.36
0.35
0.37
0.33
0.33
column (SP)
TAC ($/yr) 8.42E+05 8.42E+05 1.05E+06 1.04E+06 1.31E+06 1.31E+06
SQF scheme
0.39
0.32
0.37
0.33
0.35
0.35
TAC ($/yr) 1.03E+06 1.01E+06 1.01E+06 9.62E+05 9.80E+05 9.80E+05
SQB scheme
0.36
0.35
0.35
0.35
0.33
0.33
TAC ($/yr) 1.03E+06 1.02E+06 9.59E+05 9.59E+05 1.19E+06 1.19E+06
Case study
Mixture: ethanol
n-propanol
n-butanol
Equimolar feed composition
(0.333, 0.333, 0,333)
Product purity specification: 99 m%
Detailed results of economic studies
Table 3. Results of the economic optimization
Description
D
I
DQF
DQB
SP
SQF
Col.1 Col.2 Col.1 Col.2 Col.1 Col.2 Col.1 Col.2 Col. 1 Col.2 Col. 1 Col.2
Bottom temperature (oC) 102.32 117.22 117.19 96.93 153.60 117.21 102.32 134.11 102.37 117.19 150.45 117.24
Column pressure (kPa) 101.33 101.33 101.33 101.33 511.00 101.33 101.33 178.50 101.33 101.33 451.00 101.33
Column diameter (m)
0.84 0.82 0.96 0.78 0.76 0.87 0.84 0.85 0.72 0.98 0.65 0.74
Reflux ratio
2.25 1.67 0.90 1.87 2.56 2.38 2.25 2.20 0.67 2.99 0.82 1.92
Overall column efficiency 0.52 0.49 0.46 0.49 0.63 0.49 0.52 0.53 0.51 0.49 0.59 0.48
Actual number of trays
91
98
93
88
93
55
92
61
87
145
57
147
Total actual trays
189
181
148
153
232
204
Heating rate (kJ/hr)
8.01E+06
8.99E+06
5.19E+06
4.56E+06
5.28E+06
3.91E+06
Cooling rate (kJ/hr)
7.88E+06
8.85E+06
4.78E+06
4.19E+06
5.15E+06
3.78E+06
Main HX duty (kJ/hr)
…………..
…………..
3.94E+06
4.26E+06
…………..
2.89E+06
Auxiliary heat exchanger …………..
…………..
TC,MP
TR,LP
LP
TC,MP
Steam cost ($/yr)
5.45E+05
6.11E+05
4.52E+05
3.10E+05
3.59E+05
3.41E+05
C.W cost ($/yr)
2.73E+04
3.07E+04
1.66E+04
1.45E+04
1.79E+04
1.31E+04
Operating cost ($/yr)
5.72E+05
6.41E+05
4.69E+05
3.25E+05
3.77E+05
3.54E+05
Capital cost ($/yr)
7.54E+04
7.85E+04
7.80E+04
8.14E+04
8.18E+04
7.62E+04
TAC ($/yr)
6.48E+05
7.20E+05
5.47E+05
4.06E+05
4.59E+05
4.30E+05
Capital cost saving (%)
0
-4
-3
-8
-8
-1
Operating cost saving (%)
0
-12
18
43
34
38
TAC saving (%)
0
-11
16
37
29
34
SQB
Col. 1 Col.2
103.82 158.77
101.33 367.00
0.71 0.71
0.67 1.61
0.49 0.56
79
143
223
3.94E+06
3.00E+06
3.07E+06
TC,MP
3.43E+05
1.04E+04
3.54E+05
7.98E+04
4.33E+05
-6
38
33
Comparison of TAC savings (%)
40
30
%
20
10
I
0
-10
-20
D
base
case
DQF
DQB
SP
SQF
SQB
Heat loads of the studied schemes
Case 1 (0.45/0.1/0.45)
3.5E+07
Case 2 (0.33/0.33/0.33)
Case 3 (0.1/0.8/0.1)
Heat load (kJ/h)
3.0E+07
2.5E+07
2.0E+07
1.5E+07
1.0E+07
5.0E+06
0.0E+00
D
DQB
SP
Studied distillation schemes
SQF
SQB
Comparison of costs ($/yr)
Case (2), (0.33/0.33/0.33)
8.E+05
7.E+05
6.E+05
SP SQF SQB
5.E+05
Capital cost
4.E+05
Utility cost
3.E+05
Total annual cost
2.E+05
1.E+05
0.E+00
D
I DQF DQB
TAC savings of studied schemes
60
Case 1 (0.45/0.1/0.45)
Case 2 (0.33/0.33/0.33)
Case 3 (0.1/0.8/0.1)
TAC savings (%)
50
40
30
20
10
0
D
DQB
SP
Studied distillation schemes
SQF
SQB
Results of the economic study
 Energy-integrated schemes are more economical than the best
conventional schemes.
 Operating cost proved to be dominant on TAC.
 Petlyuk column is the best in TAC saving at low concentration of the
middle component (case 1) with 33 % .
 The heat requirements for the separation increases with increasing
concentration of the middle component, and heat-integrated schemes
prove to be the best.
 The maximum TAC saving is achieved in case 3 with 53 % by
sloppy sequence with backward heat integration.
Controllability study
•Selection of controlled variables & manipulated variables,
•Degrees of freedom analysis
Steady-state control
indices
•Niederlinski index (NI)
•Morari index (MRI)
•Condition number (CN)
•Relative gain array (RGA)
Dynamic
simulations
Open-loop
Closed-loop
Steady state controllability indices
Steady state controllability indices for the optimized schemes
Studied Sche mes
NI
MRI
CN
11
22
33
D, (D1 -L2 -B2 )
1.137
0.099
8.890
1.000
0.880
0.880
D, (L1 -D2 -Q2 )
1.995
0.065
32.113
1.000
0.500
0.500
D, (L1 -D2 -B2 )
1.865
0.234
4.934
0.580
0.540
0.920
DQF, (D1 -L2 -B2 )
1.136
0.024
36.320
1.000
0.880
0.880
DQF, (L1 -D2 -Q 2 )
1.890
0.033
21.290
1.000
0.530
0.530
DQF, (L1 -D2 -B2 )
1.678
0.226
5.240
0.586
0.595
1.020
DQB, (D1 -L2 -B2 )
1.093
0.023
39.660
1.000
0.910
0.910
DQB, (L1 -D2 -Q 2 )
2.283
0.040
18.110
1.000
0.440
0.440
DQB, (L1 -D2 -B2 )
1.540
0.246
5.040
0.647
0.645
1.000
SP, (D-S-Q)
3.515
0.182
6.890
1.000
0.320
0.280
SP, (L-S-B)
7.438
0.089
14.380
0.130
0.570
0.990
SQF, (D-S-Q)
6.470
0.010
137.400
1.000
0.250
0.150
SQF, (L-S-B)
4.030
0.008
158.100
0.250
0.250
0.998
SQB, (D-S-Q)
5.080
0.038
33.310
0.997
0.470
0.196
SQB, (L-S-B)
1.287
0.022
64.388
0.770
0.827
1.000
Evaluation of steady state indices
• Base case D and heat-integrated schemes (DQF and DQB)
show less interactions.
• (D1-L2-B2) manipulated set proves to be better than (L1-D2-Q2)
and (L1-D2-B2) for D, DQF and DQB.
• Serious interactions can be expected for the sloppy schemes (SQF,
SQB and SP).
• (L-S-B) manipulated set proves to better than (D-S-Q) for SP,
SQF and SQB schemes.
Dynamic simulations
•Feed rate disturbance: 100
100.5 kmol/h
•Feed composition disturbance:
(0.33/0.33/0.33)
(0.30/0.40/0.30)
1. Open composition control loops:
Composition control loops are not installed
Results of open loop dynamic simulation
Ethanol
Propanol
Butanol
0.993
105.5
0.989
103.5
102.5
0.985
101.5
Feed rate (kmol/h)
Product mole fraction
104.5
0.981
100.5
Feed rate disturbance
0.977
99.5
0
10
20
30
40
50
Time (unit)
60
70
80
90
Heat integrated (DQB) column, open loop, feed rate
disturbance
0.995
Ethanol
Propanol
Butanol
0.43
0.4
0.985
0.37
0.98
0.34
Feed composition disturbance
0.975
Feed mole fraction
Product mole fraction
0.99
0.97
0.31
0
10
20
30
40
Time (unit)
50
60
70
Heat integrated (DQB) column, open loop, feed
composition disturbance
0.995
Ethanol
Propanol
Butanol
104.5
103.5
102.5
0.985
101.5
Feed rate (kmol/hr)
Product mole fraction
0.990
0.980
100.5
Feed rate disturbance
0.975
99.5
0
10
20
30
40
Time (unit)
50
60
70
Petlyuk column, open loop, feed rate disturbance
Ethanol
Propanol
Butanol
0.994
0.43
0.4
0.986
0.982
0.37
0.978
0.34
Feed composition disturbance
0.974
0.97
0.31
0
10
20
30
40
50
60
70
80
Time (unit)
Petlyuk column, open loop, feed composition
disturbance
Feed mole fraction
Product mole fraction
0.99
Summary of open-loop simulation results
Open loop performance for feed rate disturbance
Ethanol (XA)
n-Propanol (XB )
n-Butanol (XC ) Average time
Studied
Time constant
Time constant
Time constant
constant
schemes
(time unit)
(time unit)
(time unit)
(time unit)
D
16
8
3
9
DQF
20
11
2
11
DQB
14
9
6
10
SP
16
5
6
9
SQF
16
3
5
8
SQB
23
13
11
16
•quite similar dynamic behaviour but
• sloppy backward heat integrated (SQB) is the slowest scheme
2. Closed composition control loops:
Composition and level controller are installed
P-controller
PI-controller
For level control
For composition control
Controllers tuning by Tyerus-Luyben cycling method
Overshoot, settling time, and their product are evaluated.
Results of closed-loop dynamic simulation
Ethanol (A)
Propanol (B)
Butanol (C)
105.5
0.9900
103.5
0.9898
101.5
Feed rate (kmol/h)
Product mole fraction
0.9903
Feed rate disturbance
0.9895
99.5
0
5
10
15
20
25
30
35
40
45
50
55
Time (unit)
Heat integrated (DQB) column, closed loop (D1-L2-B2),
feed rate disturbance
Product mole fraction
0.43
0.9900
0.4
0.9898
0.37
0.9896
Feed composition disturbance
Feed mole fraction
Ethanol (A)
Propanol (B)
Butanol (C)
0.9902
0.34
0.9894
0.31
0
5
10
15
20
25
30
35
Time (unit)
Heat integrated (DQB) column, closed loop (D1-L2-B2), feed
composition disturbance
0.9902
Ethanol(A)
Propanol(B)
Butanol(C)
108
0.9900
105
0.9899
102
Feed rate (kmol/hr)
Product mole fraction
0.9901
0.9898
Feed rate disturbance
0.9897
99
0
5
10
15
20
25
30
35
40
Time (unit)
Petlyuk column, closed loop (L-S-B), feed rate disturbance
Ethanol(A)
Propanol(B)
Butanol(C)
0.9901
0.43
0.4
0.9899
0.37
0.9897
Feed mole fraction
Product mole fraction
0.9903
0.34
Feed composition disturbance
0.9895
0.31
0
5
10
15
20
25
30
35
40
Time (unit)
Petlyuk column, closed loop (L-S-B), feed composition
disturbance
Summary of closed-loop simulation results
Closed-loop performance for feed rate disturbance
 ST
 OS
 PSO
FBL
D (D1 -L2 -B2 )
10.580
0.010
0.075
1.3
D (L1 -D2 -B2 )
32.740
0.011
0.227
1.0
DQF (D1 -L2 -B2 )
11.620
0.006
0.051
1.3
DQF (L1 -D2 -Q 2 )
20.100
0.012
0.178
1.0
DQF (L1 -D2 -B2 )
36.550
0.012
0.346
1.0
DQB (D1 -L2 -B2 )
23.330
0.024
0.310
3.0
DQB (L1 -D2 -Q 2 )
26.750
0.033
0.751
1.0
DQB (L1 -D2 -B2 )
34.250
0.034
0.677
1.0
SP (L-S-B)
30.610
0.023
0.238
2.0
SP (D-S-Q)
39.440
0.020
0.262
5.0
SQF (L-S-B)
40.500
0.057
0.813
2.0
SQF (D-S-Q)
59.960
0.042
0.903
3.0
SQB (L-S-B)
70.150
0.030
0.799
6.0
SQB (D-S-Q)
150.000
0.031
1.564
20
Studied schemes
Closed-loop performance for feed composition disturbance
Studied schemes
FBLT
 ST
 OS
 PSO
D (D1 -L2 -B2 )
14.200
0.006
0.044
1.3
D (L1 -D2 -B2 )
21.200
0.021
0.264
1.0
DQF (D1 -L2 -B2 )
13.160
0.003
0.025
1.3
DQF (L1 -D2 -Q 2 )
23.340
0.041
0.631
1.0
DQF (L1 -D2 -B2 )
31.950
0.081
1.951
1.0
DQB (D1 -L2 -B2 )
16.530
0.019
0.133
3.0
DQB (L1 -D2 -Q 2 )
37.850
0.130
3.790
1.0
DQB (L1 -D2 -B2 )
44.840
0.130
3.512
1.0
SP (L-S-B)
28.390
0.043
0.441
2.0
SP (D-S-Q)
42.680
0.064
0.907
5.0
SQF (L-S-B)
41.600
0.017
0.261
2.0
SQF (D-S-Q)
68.290
0.030
0.789
3.0
SQB (L-S-B)
104.850
0.076
3.398
6.0
SQB (D-S-Q)
107.310
0.083
4.654
20
Conclusions of closed-loop dynamic simulations
•Simple energy integration (heat integration) doesn’t influence
dynamic behaviour compared to the non-integrated base case
•Higher detuning factor is needed due to stronger interactions in
complex distillation systems (they became slower in closed loop)
•The cases, where material and energy flows (energy integration)
go into the same direction (DQF, SQF), are better than the opposite
•Since the sloppy schemes show similar economic parameters,
their controllability features make the decision to the favour of SQF !
•Petlyuk columns controllability parameters are between the ones of
the heat-integrated and the sloppy schemes
Estimation of the flue gas emissions
The main gaseous pollutants that are considered in this
work are: CO2, SO2, and NOx
The flue gas emissions of Case 1
Schemes
Heating rate
(MW)
Fuel type
CO2 emissions
(kg/hr)
SO x emissions
(kg/hr)
NOx emissions
(kg/hr)
Total emissions
(kg/hr)
Emissions
saving %
D
DQB
SP
SQF
SQB
5.5
3.7
3.5
3.4
3.5
Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil
gas
gas
gas
gas
gas
1030
1447
691
971
659
926
689
968
716
1006
0.75
3.57
0.50
2.39
0.48
2.28
0.50
2.39
0.52
2.48
0.16
0.66
0.11
0.44
0.11
0.42
0.17
0.47
0.18
0.49
1031
1451
692
974
660
928
689
971
717
1009
0
33
36
33
31
Final conclusions
with energy-integration about 53 % TAC saving can be
realised in case of SQF scheme
Petlyuk column has a limited TAC saving of 30-33 % in all
the three feed composition cases
conventional heat-integration shows the best economic and
controllability features considering all the three feed
compositions
sloppy schemes show good economic features but the
selection is made according to their different controllability
features (SQF has better features than SQB)
economic and controllability features are to be handled
simultaneously during process design.
Closed loop dynamic simulations
•Simple energy integration (heat integration) doesn’t influence
dynamic behaviour compared to the non-integrated base case
•more complicated systems: higher detuning factor is needed
due to stronger interactions (they became slower in closed loop)
•The cases, where material and energy flows (energy integration)
go into the same direction (DQF, SQF), are better than the opposite
•Since the sloppy schemes show similar economic parameters, their
controllability features make the decision to the favour of SQF (!)
•Petlyuk (dividing wall) column‘s controllability parameters are
between the ones of the heat integrated and the sloppy structures.
Synthesis of Mass Exchange Networks
Using Mathematical Programming
Budapest University of Technology and Economics
Department of Chemical Engineering
Outline
I.
Mass Exchange Network Synthesis (MENS)
A Extension of the MINLP model of Papalexandri et al. (1994)
B Comparison of the advanced pinch method of Hallale and Fraser (2000)
and the extended model of Papalexandri et al.
C New, fairly linear MINLP model for MENS
II.
Rigorous MINLP model for the design of
distillation-pervaporation systems
III. Rigorous MINLP model for the
design of wastewater strippers
Approach:
Mixed Integer Nonlinear Programming (MINLP)
optimisation software: GAMS / DICOPT
I. Mass Exchange Network Synthesis
El-Halwagi and Manousiouthakis, AIChE Journal, Vol 35, No.8, pp. 1233-1244
RICH STREAMS
LEAN STREAMS (MSAs)
xsj
NS=NSE+NSP
1 2
Mass integration for the analogy
of the concept of heat integration.
Absorber, extractor etc. network synthesis
. . .
Gi
1
2
ysi
.
.
.
MASS EXCHANGE
NETWORK
yti
NR
xtj
yi=f(xj)
The synthesis task:
Stream data + equipment data +
equilibrium data + costing
Network structure
lean stream flow rates
min (Total Annual Cost, TAC)
Previous work:
early pinch methods (no supertargeting)
Water pinch: Wang & Smith (1994, 1995), Kuo & Smith (1998)
El-Halwagi & Manousiouthakis (1989a)
El-Halwagi (1997)
advanced pinch method (includes supertargeting)
Hallale & Fraser (1998, 2000)
sequential mathematical programming methods
El-Halwagi (1997), Garrison et al. (1995)
Alva-Argaez et al. (1999)
simultaneous mathematical programming models
Papalexandri et al. (1994)
Papalexandri & Pistikopoulos (1995, 1996)
Comeaux (2000); Wastewater: Benkő, Rév & Fonyó (2000)
I/A Extensions of the MINLP model of Papalexandri et al. (1994)
• Integer stage numbers
• Generation of feasible initial values
• Kremser equation:
  1  y  m x  bij  1 
 
ln  1  
  A  y  m x  bij  A 



N A1  
ln  A
in
i
out
i
in
ij j
in
ij j
f(x)
 f  x  if x  c
f x    1
not defined if x  c
x LO  x  xUP
yiin  yiout
N A1  out
yi  mij x inj  bij
f1(x)
xLO
yi*=mijxj*+bij
A
Lj
mij Ri
c
xUP
x
Removable discontinuity at A=1
• Previous mathematical programming models for MENS
assumed that A is always greater than 1
• Numerical difficulty when using GAMS
Handling of removable discontinuities in MINLP models:
Example: The Kremser-equation
ifIf A1
ifIf A=1
NTPA1
NTPA1

 yiin  m j x inj  b j
1

ln  1   out
 y  m x in  b

A

j j
j
 i
 
ln  A
yiin  yiout
 out
yi  m j x inj  b j
 1
 
 A


A
Lj
m jGi
• The usual form of the Kremser equation has a removable discontinuity at A=1
• Using only the first form of the Kremser equation in MINLP models leads to a
division by zero error or gives solutions that have no physical meaning
• Restricting all the values of A under or over 1 very likely excludes the real optimal
solution from the search space
Different possibilities for handling the discontinuity of the
Kremser equation:
• For all mass exchangers in the superstructure both formulas are used
to calculate the theoretical number of stages
• A binary variable Y has to be generated to be able to choose
between the two calculated stage numbers
Y=1  A=1
Y=0  A1
then
NTP  Y  NTPA1   1  Y   NTPA1 
The binary variable Y can be generated in the following ways:
Methods for calculating Y taken from the literature:
Three (or two, y3=1-y1-y2) binary variables are used to denote the
interval in which the actual value of A lies
y1
0.01
y2
0.99
y1=1 when
0.99A0.01 etc.
y3
1.01
100
y1+y2+ y3=1
Big-M method:
A  0.99  M 1  (1  y1 )
;
A  1.01  M 2  (1  y 2 )
;
A  100  M 3  (1  y 3 )
 A  0.01  M 1  (1  y1 )
;  A  0.99  M 2  (1  y 2 ) ;
 A  0.01  M 3  (1  y 3 )
M 1  99.01 ; M 2  98.99 ; M 3  1
0.01  A  100 ;
y1  y 2  y 3  1 ;
yi  0 or 1 ;
Y  y2
Multi-M method:
A  0.99  M 1, 2  (1  y1 ) ;
A  1.01  M 2,1  (1  y 2 ) ;
A  100  M 3,1  (1  y 3 )
 A  0.01  M 1,3  (1  y1 ) ;  A  0.99  M 2,3  (1  y 2 ) ;
 A  0.01  M 3, 2  (1  y 3 )
M 1, 2  99.01 ; M 1,3  0 ; M 2,1  98.99; M 2,3  0.98; M 3,1  0;
M 3,2  1
0.01  A  100 ;
Y  y2
y1  y 2  y 3  1 ;
yi  0 or 1 ;
Convex hull:
A1  0.99  y1 ;
A2  1.01  y 2 ;
A3  100  y 3
 A1  0.01  y1 ;
 A2  0.99  y 2 ;
 A3  1.01  y 3
0  Ai  100 ;
0.01  A  100 ;
A1  A2  A3  A
y1  y 2  y 3  1 ;
yi  0 or 1 ;
Y  y2
A formulation of Raman & Grossmann:
99 z1  0.01  A  1
z1  z 2  y 3  1
A  1  991  z1 
z1  y3  0
 11  z 2   A  1
z2  y3  0
A  1  99 z 2  0.01
Y  y3
A simple logical formulation (L-formulation):
0.01  y1  0.99  y 2  1.01  y 3  A  0.99  y1  1.01  y 2  100  y 3
y1  y 2  y 3  1 ;
y i  0 or 1 ;
Y  y2
The main drawback of the methods taken from the literature is, that they use
three binary variables for each removable discontinuity or mass exchanger.
Solving MEN synthesis problems this may mean that in case of large
superstructures the problem size exceeds the practical solvability limit
(approx. 80-100 binary variables in an MINLP model).
New formulation for handling removable discontinuities:
( A  c)  V  y
V is continuous
y is a binary variable
n=2
n
For the Kremser equation: (A-1)2=Vy
y=1
y=0
(A-c)2
y=1
0.01  A  100
2 VLO
104  V  9810
Y=1-y
A=c
A
This method uses only one
binary variable for calculating
the binary variable Y.
Several mass exchange network synthesis problems were solved using our method.
It proved to be fast and well applicable.
N  Y  N A1   1  Y  N A1 
Adopted literature methods
• Big-M formulation
• Multi-M formulation
• a Convex-hull like formulation
• Raman & Grossmann (1991)
• Simple logic formulation
y1
0 0.01
y2
0.99
y3
1.01
100
are linear but use 3 binary variables
New method ( A  c) 2  V  y
ALO  A  AUP
VLO  V  VUP
Advantages:
1. faster
2. larger problems can be solved
nonlinear but uses 1 binary variable only
(the models are nonconvex anyway)
Large nonconvex MINLP problems solved by DICOPT++:
There exists a critical upper limit of the number of binary variables
I/B
Comparison of the advanced pinch method of
Hallale and Fraser (2000)
and the extended model of Papalexandri et al. (1994)
Example
Objective
Pinch solution of
MINLP
(CMINLP-CPinch) /
function
Nick Hallale
Solution
CMINLP *100
Target / Design
3.1
CAP
830 000 / 860 000
1 044 285
+17.6 %
3.2
CAP
448 000 / 455 000
453 302
-0.4 %
3.3
CAP
819 000 / 751 000
637 280
-17.8 %
3.4
CAP
591 760 / 637 000
637 000
0.0 %
4.1
CAP
296 000 / 298 000
255 068
-16.8 %
5.1
TAC
226 000 / 228 000
226 000
-0.9 %
5.2
TAC
226 000 / 228 000
226 000
-0.9 %
5.3
TAC
226 000 / 228 000
226 000
-0.9 %
5.4
TAC
49 000 / 49 000
50 279
+2.5 %
5.5
TAC
524 000 / 526 000
527 000
+0.2 %
6.1
TAC
692 000 / 706 000
720 000
+1.9 %
6.2
TAC
28 000 / 28 000
32 000
+12.5 %
6.3
CAP
591 000 / 539 000
536 000
-0.6 %
TAC-total annual cost in USD/yr,
CAP–annualised capital cost in USD,
13 example
problems
have been solved
C - cost
The two methods perform more or less the same.
Why are the MINLP solutions not always better? The MINLP model is nonconvex.
I/C
New, fairly linear MINLP model for MENS
concentration
location 1
x 1,T
y 1,S
R1
x 1,1
k=1
R1-L1
R1-L1
R2-L1
R2-L1
x 2,S
y 2,3
y 2,2
R2-L2
x 1,S
x 2,3
x 2,2
y 2,1
x 1,3
L1
y 1,3
R1-L2
R2
concentration
location 3
y 1,T
R1-L2
x 2,1
y 2,S
x 1,2
k=2
y 1,2
y 1,1
x 2,T
concentration
location 2
L2
y 2,T
R2-L2
Similar to the HEN superstructure of Yee & Grossmann (1990)
The stagewise superstructure enables almost linear mass balance formulation
Model equations
mass balances
  me
R  y  y
i  i, k
i, k  1   i, j , k
j

R  y s  y
me
i i
i, last  
i, j, st
j, st
  me
L  x
x
j  j, k
j , k  1   i, j , k
i
L  x
 x s    me
j  j, first
j
i, j, st
i, st
concentration constraints
y y
i, k
i, k  1
x
j, k
x
j, k  1
y
 YT
i, last
i
 XT
j, first
j
y
Ys
i, first
i
x
x
j, last
 Xs
j
minimise
 c L
TAC   f  mass
i, j, k   j j

i, j , k
j
s.t.
big-M constraints for the existenxe of the units
me
 z
0
i, j , k
i, j i, j , k
driving force constraints
1  z

dy
 y m x
b 
i, j , k
i, k
i, j j , k i, j
i, j, k 
i, j, k 
1  z

dy
y
m x
b 

i, j , k  1 i, k  1
i, j j , k  1 i, j
i, j , k 
i, j, k 
constraints on the number of existing units
max
 zi, j , k  U
i, j , k
U min   z
i, j , k
i, j , k
Chen’s approximation for the log mean conc differences
1/ 3

 dy
 / 2
lmcd
 dy
dy
 dy
i, j, k  i, j, k i, j , k  1 i, j, k
i, j, k  1  
calculation of the mass of the exchangers
mass
 K  lmcd
 me
i, j , k W
i, j , k
i, j , k
Only the lean stream mass balances are bilinear
Example problems
Example 4.1 (Hallale, 1998)
3.66e-3
2.5e-3
4.05e-3
1.059e-2
298,000 USD
Solution obtained by using the
R2
3.5kg/s
0.01
network
5e-3
Pinch solution of Nick Hallale (1998)
R1
4 kg/s
5e-3
2.5e-3
Capital cost of the
2 kg/s
5e-3
3.26e-3
1e-3
2.5e-3
9.16e-3
1e-2
R4
0.5kg/s
8e-3
4.08e-3
R3
1.5kg/s
R5
255,068 USD
0
MINLP model of Papalexandri et al. (1994)
3.77e-3
1.64e-3
S3
1.7e-2
7.79e-3
2.48 kg/s
Solution obtained by the
284,440 USD
1 kg/s
S2
new MINLP model
S1
2.5e-3
8.48e-3
5.82e-3
3.86e-3
3.63e-3
1.7e-3
1.8 kg/s
7.1e-3
Capital cost, based on exchanger mass: 284,440 USD
Extensions: stagewise exchangers, multiple components
0.9 kg/s
Authors
Total annual cost of the MEN
MINLP solution of
917,880 USD/yr.
Advanced pinch solution of
N=4.93
N=2.88
N=2.73
N=3.25
R2
427,000 USD/yr.
1.752 kg/s
Hallale and Fraser (2001)
Solution obtained using the
0.062 kg/s
R1
0.1 kg/s
Papalexandri et al. (1994)
N=4.23
0.022 kg/s
S1
436,289 USD/yr
new MINLP model
2.169 kg/s
0.487 kg/s
S2
The new model is most suitable for solving single component
MENS problems, where packed columns are used exclusively.
In this case, no special initialisation is needed.
TAC=436,289 USD/yr
Two component example
0.566 kg/s
II. Rigorous MINLP model for the design of
distillation-pervaporation systems
Distillation
column
The synthesis task
is to determine:
Vacuum vessel
Pervaporation
unit
Inlet ethanol
~80 m/m% EtOH
retentate
(dehydrated
ethanol)
• Nth of the column
• feed tray position
• reflux ratio
• membrane structure
• reflux scheme
permeate
(mainly water)
Rigorous modelling: Dist. Column: 1 bar, MESH equations, tray by tray, Margules activity coeff.
for the liquid phase, ideal vapour phase, latent heat enthalpy
Membrane unit: transport calculation is based on experimental data
1/3 m2 flat membranes, costing - industrial practice
Adequate costing equations, utility prices
Superstructure
max n pieces of
membrane
modules
1
N
N-1
column
feed
mixer
feed
RF
F
retentate
distillate
from the
column
P1
permeate
feed pump
refi
imin
recycled
permeate
ifeed
permeate consplitter denser
to the next
section of
membranes
2
n
heat
exchanger
P3
pump
i=1…m
ibmax
to the vacuum pump
permeate
condensate
bui
1/3 m2 flat
PVA membranes
in blocks
ethanol
product
max m sections
like this
The blocks (or modules)
can be connected in both
series or parallel
2
1
P4
recycled
permeate
P2
column bottom
product
Distillation column superstructure:
Viswanathan & Grossmann (1993)
Membrane superstructure: new
Multiple level optimisation (successive refinement)
enables reducing the number of binary model variables
Modelling of the membranes is based on experimental data
Industrial example
12 x 81 pieces
of 1/3 m2 flat membranes
=324 m2 total
(fixed industrial configuration)
feed
80 mass%
EtOH
total permeate recycling
D=0.875 m
min=97.5%
12 x 107 pieces
of 1/3 m2 flat membranes
= 428 m2 total
84 1046.3 kg/hr
theor. 91.44 mass%
stages
total permeate recycling
D=0.679 m
1175
kg/hr
1175
kg/hr
7
4
membrane capital investment : 69,058 USD
membrane replacement
: 110,758 USD
column capital investment : 13,931 USD
column operating cost
: 134,377 USD
membrane capital investment : 52,362 USD
membrane replacement
: 83,936 USD
column capital investment : 18,05 USD
column operating cost
: 219,472 USD
1
1
recycled permeate
72 kg/hr
28.96 mass% EtOH
bottom product
254.3 kg/hr
0.087 mass% EtOH
bottom product
254.3 kg/hr
0.087 mass% EtOH
recycled permeate
125.6 kg/hr
30.86 mass% EtOH
TAC=373,820 USD/yr
Optimised
Base case
12% savings in the TAC
base case
optimally designed
system
400
400
3,5
membrane capital investment
membrane replacement
column capital investment
column operational cost
TAC
reflux ratio
350
350
3
300
300
plant membrane cost
plant TAC
optimised membrane cost
optimised TAC
optimised column cost
plant column cost
250
200
TAC (thousand USD/yr)
TAC (thousand USD/yr)
Other calculations
using the MINLP
model
TAC=328,124 USD/yr
2,5
250
200
2
reflux ratio
feed
80 mass%
EtOH
min=97.5%
80
992.7 kg/hr
theor. 94.56 mass%
stages
retentate (product): 920.7 kg/hr
99.7 mass % EtOH

reflux ratio:
1.38
retentate (product): 920.7 kg/hr
99.7 mass % EtOH

reflux ratio:
3.262
150
1,5
100
150
1
50
100
94,5
95
95,5
96
96,5
97
97,5
98
98,5
99
specified ethanol yield (%)
99,5
0
300
0,5
350
400
450
500
overall membrane surface in square meters
Ethanol yield - TAC
Membrane surface - TAC
OPTIMISATION OF HYBRID
ETHANOL DEHYDRATION SYSTEM
Z. Fonyo, Z. Lelkes, Z. Szitkai, E. Rev
• Introduction & problem statement
• MINLP model and superstructure
• Membrane model
• Industrial case study
• Conclusions
Department of Chemical Engineering, H-1521 Budapest, Hungary
retentate
(abs. EtOH product)
inlet stream
1000 kg/hr
94 mass% EtOH
plant membrane configuration
12 sections in series
each consisted of 81 pieces
of 1/3 m2 flat membranes
in parallel
measured:
940 kg/hr
99.6-99.7 mass% EtOH
calculated:
921.5 kg/hr
99.6 mass% EtOH
permeate
measured: 60 kg/hr 15 mass% EtOH
calculated: 78.5 kg/hr 28 mass% EtOH
Calculated and measured output stream properties
for the fixed industrial inlet stream and membrane configuration
retentate (product): 920.7 kg/hr
99.7 mass % EtOH

reflux ratio:
3.262
feed
80 mass%
EtOH
min=97.5%
80
992.7 kg/hr
theor. 94.56 mass%
stages
12 x 81 pieces
of 1/3 m2 flat membranes
=324 m2 total
(fixed industrial configuration)
total permeate recycling
D=0.875 m
1175
kg/hr
4
membrane capital investment : 52,362 USD
membrane replacement
: 83,936 USD
column capital investment : 18,05 USD
column operational cost
: 219,472 USD
1
recycled permeate
72 kg/hr
28.96 mass% EtOH
bottom product
254.3 kg/hr
0.087 mass% EtOH
TAC=373,82 USD/yr
Base case: optimised hybrid ethanol dehydration plant
with fixed industrial membrane structure
retentate (product): 920.7 kg/hr
99.7 mass % EtOH

reflux ratio:
1.38
feed
80 mass%
EtOH
min=97.5%
84 1046.3 kg/hr
theor. 91.44 mass%
stages
12 x 107 pieces
of 1/3 m2 flat membranes
= 428 m2 total
total permeate recycling
D=0.679 m
1175
kg/hr
7
membrane capital investment : 69,058 USD
membrane replacement
: 110,758 USD
column capital investment : 13,931 USD
column operational cost
: 134,377 USD
1
recycled permeate
125.6 kg/hr
30.86 mass% EtOH
bottom product
254.3 kg/hr
0.087 mass% EtOH
TAC=328,124 USD/yr
Optimised hybrid ethanol dehydration plant
with optimised membrane structure
400
TAC, industrial
TAC (thousand USD/yr)
350
TAC, optimised
300
plant membrane cost
plant TAC
optimised membrane cost
optimised TAC
optimised column cost
plant column cost
250
200
150
100
94,5
95
95,5
96
96,5
97
97,5
98
98,5
99
99,5
specified ethanol yield (%)
Influence of the specified ethanol yield on the TAC
optimised system vs. plant existing in the industry
350
300
TAC
TAC (thousand USD/yr)
membrane capital investment
membrane replacement
250
column capital investment
column operational cost
200
TAC
150
100
50
0
94,5
95
95,5
96
96,5
97
97,5
98
98,5
99
Specified ethanol yield in %
Influence of the specified ethanol yield on the TAC,
optimised systems only
99,5
industrial case
optimised
400
3,5
membrane capital investment
membrane replacement
column capital investment
column operational cost
TAC
reflux ratio
350
3
2,5
250
200
2
reflux ratio
TAC (thousand USD/yr)
300
150
1,5
100
1
50
0
300
0,5
350
400
450
500
overall membrane surface in square meters
Dependence of the TAC and the reflux ratio
on the overall membrane surface
Shepard’s metric interpolation:



c ( P) 

r
 ( P)
rj ( P )
( cT )i 
4
i j
i
Sheppard's metric interpolation
alpha=2
3,5
T
3
j
i j
depending on the value of alpha:
• local minima
• step function
• peaks
2,5
cT ,mass %
i
cT calculated by differential equations
2
cT, metric interpolated
1,5
1
0,5
0
Power function:
0
5
10
15
20
25
30
j0, kg/hr (c0=4.06 mass%)
0,145
CT  0,55  C0  J 0
J T  0,999  J 0  0,031  C0
parameter fitting: method of least squares
35
40
45
50
334
TAC (thousand USD/yr)
332
330
328
326
324
TAC, optimised
322
320
318
316
314
94
95
96
97
98
99
Specified ethanol yield in %
Influence of the specified ethanol yield on the TAC
100
Results:
• Design tool to optimise the hybrid
ethanol dehydration process
• Large, but solvable MINLP model
• In case of an industrial dehydration plant:
12% saving in TAC is possible
by addition of 32% more membrane surface
• Sensitivity analysis on membrane replacement cost,
membrane surface and ethanol yield
III. Rigorous MINLP model for the design of
wastewater strippers
Wastewater cleaning by stripping
Minor quantities of acetone,
methanol, and ethanol in water
total condenser
top product
feed
20
5 mol/s
xacetone = 0.05
xmethanol= 0.04
xwater = 0.90
xethanol = 0.01
Superstructure
19
18
17
.
.
.
Nth=?
boil-up vapour
VLE calculation
3
2
1
bottom product
Similar to the distillation column
superstructure of Viswanathan &
Grossmann (1993)
Xwater0.999
water85%
Wilson binary interactions
Ideal vapour phase
Theoretical stages
1 bar
Latent heat enthalpy
Antoine vapour pressure
Conclusions:
Complex evaluation of distillation based heat
integrated separation schemes is presented. New
sloppy structures proved to be competitive.
New, fairly linear, MINLP modell for MENS is
developed and succesfully tested for literature
examples and industrial case studies.
.
Utility prices
Utility
Temperature
Price
level (ºC)
($/ton, kWh)
Low pressure steam
160
17.7
Middle pressure steam
184
21.8
Cooling water
30-45
0.0272
Electricity
--------
0.1 $
Controllability investigations,
design
– interactive and challenging part of process
design or development.
Control structure synthesis
* control targets are defined,
* the sets of controlled variables and possible manipulated
variables are determined (degrees of freedom)
* pairing of the controlled and manipulated variables: steady
state control indices, dynamic behaviours in the cases of
open and closed control loops of the promising control
structures.
Demonstration of interaction
between design and control
• comprehensive design of five energy
integrated separation schemes
• three-component-alcohol-mixture is
separated in five distillation based energy
integrated two-column separation systems:
– two heat integrated distillation schemes
– fully thermally coupled distillation column
(Petlyuk, Kaibel)
– sloppy separation sequences
Solvent Recovery
from Non-Ideal Quaternary Mixtures
with
Extractive Heterogeneous-Azeotropic Distillation
Budapest University of Technology and Economics
Chemical Engineering Department
Motivation
• Industrial companies ( printing, pharmaceutical) have
waste streams of solvents (quaternary mixtures)
• 4 groups of solvents with different VLLE, azeotropes
• Separation of non-ideal quaternary mixtures is less
studied
Goal
Guideline for the design of separation schemes for
non-ideal quaternary mixtures
Heterogeneous-azeotropic distillation
Extractive distillation
Extractive heterogeneous – azeotropic distillation
D1
F2
Extr. agent
Feed
W1
Group 1
Acetone, ETOH, MEK, Water
Acetone, ETAC, ETOH, Water
One volatile component forming no azeotropes
Acetone
Binary azeotropes
Binary azeotropes
Water-ETOH
Water-ETOH
Water-MEK
Water-ETAC
ETOH-MEK
ETOH-ETAC
Ternary azeotrope
Ternary azeotrope
Water-ETOH-MEK
Water-ETOH-ETAC
Investigated separation schemes
A
Aceton
D2
F2
C2
D1
Water
mix
F
B2
C1
Feed
Group 1
ETOH
95 w%
W1
H2O
MEK
(ETAC)
Representation of separation in Column 1
Water addition
F
W1
Separation in C1
Hypothethical feed
D1
Water – Acetone - MEK
Ternary mixture
Acetone
1
D2
0.9
0.8
0.7
0.6
0.5
F2
0.4
0.3
0.2
0.1
B2
MEK
MEK 0
R
Phase sep.
0
BB2
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Water
1
Investigated separation schemes
B
D2
Group 1
MEK
(ETAC)
Water
ACETONE
C2
Feed
C1
D1
B1
Water
ETOH
MEK(ETAC)
B2
ETOH
95 w%
H2O
Water – ETOH – MEK
Ternary mixture
ETOH
1
0.9
0.8
0.7
0.6
0.5
0.4
F2
Water addition
0.3
0.2
0.1
MEK
D2
Phase sep.
0.4
0.5
B2
C3
0
MEK
0
0.1
0.2
0.3
0.6
0.7
0.8
0.9
1Water
Economic comparison of structures
A and B
6
Total Annual Cost
[1e5 €]
5
A
4
3
B
2
0
0.1
0.2
0.3
0.4
mole fraction Acetone in Feed
0.5
0.6
Group 2
ETAC, ETOH, IPAC, Water
ETOH, MEK, IPAC, Water
Binary azeotropes
Binary azeotropes
Water-ETOH
Water-ETOH
Water- ETAC
Water-MEK
Water-IPAC
Water-IPAC
ETOH-ETAC
ETOH-MEK
ETOH-IPAC
ETOH-IPAC
Ternary azeotropes
Ternary azeotropes
Water-ETOH-IPAC
Water-ETOH-MEK
Water-ETOH-IPAC
Water-ETOH-IPAC
Investigated separation schemes for
the mixtures of Group 2
mix
D3
Water
F1
D2
B2
IPAC
ETOH
95 w%
W1
H2O
C3
F2
C2
D1
Group 2
C1
Feed
B3
ETAC
(MEK)
The VLLE Data and representation of separation
Hypothetical feed
Water
B1
R
Water feed addition
Separation in C1
Operating line of
phase separator
F
F1
IPAC
D1
R1
F2
ETOH
ETAC
Water – ETAC – IPAC
Ternary mixture
ETAC
1
B3
0.9
D2(F3)
0.8
0.7
D3(R1)
F2
0.6
0.5
0.4
0.3
Immiscibility region
0.2
0.1
B2
0
IPAC 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 Water
Group 3
ETAC, ETOH, MEK, Water
ETAC, IPOH, MEK, Water
Binary azeotropes
Binary azeotropes
Water-ETAC
Water-ETAC
ETOH-ETAC
Water-MEK
Water-MEK
Water-IPOH
ETOH-MEK
IPOH-ETAC
ETAC-MEK
IPOH-MEK
Water - ETOH
ETAC-MEK
Ternary azeotropes
Ternary azeotropes
Water-ETOH-ETAC
Water-IPOH-MEK
Water-ETOH-MEK
Water-ETAC-MEK
Water-ETAC-MEK
MEK-IPOH-ETAC
Separation schemes for the mixtures
of Group 3
R1
C1
Water
Feed F
F2
ETAC
MEK
Water
R2
B2
ETAC
95 w%
D3
C3
V1
D2
C2
Water
Group 3
B1
ETOH 95 w%
(IPOH 85 w%)
Water
Water
MEK
93 w%
Representation of extractive heterogeneous-azeotropic distillation
for the separation of mixtures of Group 3
ETOH
Water addition
MEK
F
F2 D1
B1
R
ETAC
Water
Operating line of Separation in C1
Hypothetical
phase separator
feed
Water – ETAC – MEK
Ternary mixture
MEK
1
0.9
MEK
0.8
Phase sep.
0.7
D3
0.6
0.5
C3
F2
0.4
0.3
0.2
B2
0.1
ETAC
D2
R3
C2
Phase sep.
B3
0
ETAC 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
R2 1 Water
Group 4
ETOH, MEK, N-Heptane, Water
Total possible combination
Binary azeotropes
Water-ETOH
Water-MEK
Water- N-Heptane
ETOH-MEK
ETOH- N-Heptane
MEK – N-Heptane
Ternary azeotropes
Water-ETOH-MEK
Water-ETOH- N-Heptane
Water – MEK – N-Heptane
ETOH – MEK – N-Heptane
Separation schemes for mixture of Group 4
C3
Water
R1
C1
Water
Feed F
F2
B1
N-Heptane
MEK
Water
ETOH
95 w%
Water
MEK
Water
N-Heptane
C4
B3
C2
V1
MEK
93 w%
Water
The VLLE Data
ETOH
N-Heptane
MEK
Water
Water – N-Heptane – MEK
Ternary mixture
MEK
1
0.9
MEK
0.8
Phase sep.
D2
0.7
R3
0.6
Water addition
F2
0.5
D3
D1
0.4
C4
C2
C3
0.3
0.2
0.1
0
N-Heptane 0
B3
B2
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Water
Classification of processes
Group 2
Group 1
P1
R1
P2
Water
R
Water
F
C2
C1
F
F1
D2
C2
C3
C1
P3
P2
B2
B1
Group 3
Group 4
Water
P2
Water
R
Water
F
C2
C1
B2
B1
P3
C3
B3
C3
R
Water
F
P3
C2
C4
C1
P2
B1
P4
Thank you for your attention!