Optimal synthesis of batch
separation processes
Taj Barakat and Eva Sørensen
University College London
iCPSE Consortium Meeting, Atlanta, 30-31 March 2006
Motivations
Many valuable mixtures are difficult to
separate
 Need to optimise efficiency of current
processes
 Select most economical separation process
 Explore novel techniques and alternatives

2
Objectives

Development of models/superstructure to
determine the best design configuration,
operating policy and control strategy for
hybrid separation (distillation/membrane)
processes.

Develop general guidelines for design,
operation and control of such processes
3
Project Features
Economics objective function
 Rigorous dynamic models
 Encompassing (most of) the available
decision variables
 Considering novel configurations

4
Outline
1.
2.
3.
Optimal synthesis of batch separation
processes
Multi-objective optimisation of batch
distillation processes
Concluding remarks
5
Optimal synthesis of
batch separation processes
6
Configuration Decisions
Separation problem
?
Process
Superstructure
Batch Distillation
Batch Pervaporation
7
Batch Hybrid
Design and Operation Decisions
Design Alternatives
Operational Alternatives
Min
capital
cost
• Trays
• Membrane stages
• Membrane modules
Min
running
cost
8
• Vapour loading rate
• Reflux/reboil ratios
• Recovery/No. batches
• Withdrawal rate
• Task durations
Process Superstructure
Nt
Ns , Nm,s
Rc
Rr
Qs
Lr
P
Fs
Retentate
Rp
Qr
Feed
9
Permeate
Offcut
Batch Distillation
Rc
Nt
Product 1
Offcut
Rp
Product 2
Qr
Reboiler
10
Batch Pervaporation
Ns
Separation Stage
Nm,s
Rr
Retentate
P
Feed
Qf
Rp
Permeate
Offcut
11
Hybrid Distillation I
Rc
Product
Feed
Nt
Ns Nm,s
Rp
P
Qr
Reboiler
Offcut
Permeate
12
Hybrid Distillation II
Ns Nm,s
Rc
Nt
P
Retentate
Rp
Feed
Qr
Offcut
Permeate
13
Hybrid Distillation III
Nt
Rc
Ns , Nm,s
Fs
Lr
Rr
P
Retentate
Rp
Feed
Permeate
Qr Rpr
14
Offcut
Problem Formulation – Objective Function
Maximise
Annual Profit =
Revenues – Operating Costs Av. Time – Capital Costs
Batch Processing Time
Nonlinear, (OC/CC, Guthrie’s correlations)
Subject to :
Model equations
Design variable bounds
Operational variable bounds
DAE/PDAE, nonlinear
discrete and continuous
continuous
To determine :
Design variables
Operation variables (time dependent)
15
Problem Formulation - Solution
• Mixed integer dynamic optimisation (MIDO) problem
• Complex search space topography (local optima, nonconvex)
• Need robust, stable and global solution method

DAE


gPROMS (Process Systems Enterprise Ltd., 2005)
MIDO

Genetic Algorithm (GA)
16
Optimisation Implementation
Genetic
Algorithm
Module
Genome Set
GAlib
Simulation Output
Batch
Distillation/Pervap gPROMS
Model
Physical Properties
Model State
Thermodynamics
Multiflash
Model
17
Case Study
18
Case Study ( Acetone – Water )

Separation of a binary tangent-pinch mixture
Acetone dehydration system ( 70 mol % acetone feed )
20,000 mole feed

Subject to:





≥ 97%
≥ 70%
Maximise:


Purity
Recovery
Annual profit
Assuming:


Single membrane stage
Single retentate recycle location
19
Case Study Superstructure
Nt
NsNm,s
Rc
Lr
P
Fs
Rp
Feed
Rr
Qr
Retentate
Permeate
Offcut
20
Optimal Process - Hybrid
Rr
1.00 – 1.8%
Nm = 2
0.83 – 96.3%
To = 330 K
0.24 – 1.9%
Lr =3
Fside = 2.5 mole/s
tf = 5119 s
Profit 18.07 M£/yr
Nt = 30
Rp
P = 300 Pa
Fs = 9
Retentate
1.00 – 96.3%
VReb = 5 mole/s
Permeate
Feed
Offcut
21
0.79 – 1.8%
0.88 – 1.9%
Fixed Configuration – Distillation only
Rr
1.00 – 0.10%
0.68 – 99.7%
Offcut
0.70 – 0.20%
Product 2
Nt = 30
tf = 8964 s
Profit 14.30 M£/yr
-26%
Product 1
Rp
VReb = 5 mole/s
1.00 – 0.10%
1.00 – 99.7%
0.00 – 0.20%
Reboiler
22
Case Study Summary



Approach for process selection based on overall
economics
Allows determination of best process alternative
for maximum overall profitability
Company specific costing can easily be included
23
Multi-objective optimisation of
batch distillation processes
24
Batch Distillation
Rc
Nt
Product 1
Offcut
Rp
Product 2
Qr
Reboiler
25
Problem Formulation – Objective Function
Minimise
Investment Costs
&
Minimise
Operating Costs
Subject to :
Model equations
Design variable bounds
Operational variable bounds
To determine :
Design variables
Operation variables (time dependent)
26
DAE/PDAE, nonlinear
discrete and continuous
continuous
f(x)
Optimisation
Single-objective optimisation:
To find a single optimal solution x*
of a single objective function f(x)
0
x*
x
Multi-objective optimisation:
To find array of “Pareto optimal” solutions with respect to
multiple objective functions
27
Multiobjective Optimization Problem
Maximize f (x)  ( f1 (x), f 2 (x), ..., f k (x))
subject to x  X
f 2 ( x)
Pareto Optimal
Solutions
Minimise
Several Pareto-optimal sets
Minimise
28
f1 (x)
Ranking
 k

f ( g )  2
3

nc
i 1 i
if solution is infeasible
if solution is feasible but dominated
if solution is feasible and nondominated
29
Ranking
Max = 1
F2
2
3
2
3
2
2
3
3
3
3
F1
better
30
Problem Formulation - Solution
• Multi-objective Mixed integer dynamic optimisation
(MO-MIDO) problem
• Need robust, stable and global solution method

DAE


gPROMS (Process Systems Enterprise Ltd., 2005)
MO-MIDO

Multi-Criteria Genetic Algorithm (MOGA)
31
Case Study
32
Case Study ( Acetone – Water )




Separation of a binary tangent-pinch mixture
Acetone dehydration system ( 70 mol % acetone feed )
20,000 mole feed
Subject to:



Purity
Recovery
≥ 97%
≥ 70%
Minimise:


Investment costs
Annual operating costs
33
Case Study Summary
34
Case Study Summary



Approach for multi-criteria process optimisation
using Genetic Algorithm
Allows determination of process alternatives
through Pareto optimality
Company specific costing can easily be included
35
Concluding Remarks
For hybrid batch separation processes:
 Optimum synthesis and design procedure
 Multi-criteria optimisation

Simple extension to continuous hybrid
processes
36