optimal synthesis of batch separation processes
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Optimal synthesis of batch separation processes
Taj Barakat and Eva Sørensen
University College London
iCPSE Consortium Meeting, Atlanta, 30-31 March 2006
2
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
3
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
4
Project Features
Economics objective function Rigorous dynamic models Encompassing (most of) the available
decision variables Considering novel configurations
5
Outline
1. Optimal synthesis of batch separation processes
2. Multi-objective optimisation of batch distillation processes
3. Concluding remarks
6
Optimal synthesis of
batch separation processes
7
Configuration Decisions
Separation problem
Process Superstructure
?
Batch Distillation Batch Pervaporation Batch Hybrid
8
Design and Operation DecisionsDesign Alternatives Operational Alternatives
Mincapital
cost
Minrunning
cost
• Trays• Membrane stages• Membrane modules
• Vapour loading rate• Reflux/reboil ratios• Recovery/No. batches• Withdrawal rate• Task durations
9
Process Superstructure
Feed
Retentate
Permeate
Offcut
Nt
Rc
Qr
Rp
Ns , Nm,s
P
Rr
Lr
Fs
Qs
10
Batch Distillation
Product 1
Product 2
Offcut
Reboiler
Nt
Rc
Qr
Rp
11
Batch Pervaporation
Offcut
Feed
Separation Stage
Retentate
Permeate
Ns
Nm,s Rr
Rp
P
Qf
12
Hybrid Distillation I
Feed
Product
PermeateReboiler
Offcut
Nt
Rc
Qr
RpP
Ns Nm,s
13
Hybrid Distillation II
Feed
Retentate
Permeate
Offcut
Nt
Rc
Qr
Rp
P
Ns Nm,s
14
Hybrid Distillation III
Retentate
Permeate
Offcut
Feed
Nt
Rc
Qr
Rp
Ns
, Nm,s
P
Rpr
Lr
Fs
Rr
15
Problem Formulation – Objective Function
Maximise
Annual Profit = Revenues – Operating Costs
Batch Processing TimeAv. Time – Capital Costs
Subject to :
Model equations DAE/PDAE, nonlinear
Design variable bounds discrete and continuous
Operational variable bounds continuous
To determine :
Design variables
Operation variables (time dependent)
Nonlinear, (OC/CC, Guthrie’s correlations)
16
Problem Formulation - Solution
DAE gPROMS (Process Systems Enterprise Ltd., 2005)
MIDO Genetic Algorithm (GA)
• Mixed integer dynamic optimisation (MIDO) problem • Complex search space topography (local optima, nonconvex)• Need robust, stable and global solution method
17
Optimisation Implementation
GeneticAlgorithmModule
Batch Distillation/Pervap
Model
ThermodynamicsModel
Genome Set
Model State
Simulation Output
Physical Properties
GAlib
gPROMS
Multiflash
18
Case Study
19
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 ≥ 97% Recovery ≥ 70%
Maximise: Annual profit
Assuming: Single membrane stage Single retentate recycle location
20
Case Study Superstructure
Retentate
Permeate
Offcut
Feed
Nt
Rc
Qr
Rp
NsNm,s
P
Rr
Lr Fs
21
Optimal Process - Hybrid
Feed
Retentate
Permeate
Offcut
Rp
0.79 – 1.8%
1.00 – 96.3%
0.88 – 1.9%
Rr
1.00 – 1.8%
0.83 – 96.3%
0.24 – 1.9%Lr =3
Nt = 30
Fs = 9
VReb = 5 mole/s
Fside = 2.5 mole/s
P = 300 Pa
Nm = 2
Profit 18.07 M£/yr
tf = 5119 s
To = 330 K
22
Fixed Configuration – Distillation only
Product 1
Product 2
Offcut
Reboiler
Rp
1.00 – 0.10%
1.00 – 99.7%
0.00 – 0.20%
Rr
1.00 – 0.10%
0.68 – 99.7%
0.70 – 0.20%
Nt = 30
VReb = 5 mole/s
tf = 8964 s
Profit 14.30 M£/yr
-26%
23
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
24
Multi-objective optimisation of
batch distillation processes
25
Batch Distillation
Product 1
Product 2
Offcut
Reboiler
Nt
Rc
Qr
Rp
26
Problem Formulation – Objective Function
Minimise
Investment Costs
Subject to :
Model equations DAE/PDAE, nonlinear
Design variable bounds discrete and continuous
Operational variable bounds continuous
To determine :
Design variables
Operation variables (time dependent)
Minimise
Operating Costs&
27
Optimisation
Single-objective optimisation:
To find a single optimal solution x* of a single objective function f(x)
Multi-objective optimisation:
To find array of “Pareto optimal” solutions with respect to multiple objective functions
xx*
f(x)
0
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Multiobjective Optimization Problem
))(...,),(),(()( 21 xxxxf kfffMaximize
Xxsubject to
Several Pareto-optimal sets Pareto Optimal Solutions
Min
imis
e
Minimise)(1 xf
)(2 xf
29
Ranking
3
2)(1 i
ni k
gf
c if solution is infeasible
if solution is feasible but dominated
if solution is feasible and non-dominated
30
Ranking
3
F2
F1
3
better
bett
er
3
2
22
2
Max = 1
3
3
3
31
Problem Formulation - Solution
DAE gPROMS (Process Systems Enterprise Ltd., 2005)
MO-MIDO Multi-Criteria Genetic Algorithm (MOGA)
• Multi-objective Mixed integer dynamic optimisation (MO-MIDO) problem• Need robust, stable and global solution method
32
Case Study
33
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 ≥ 97% Recovery ≥ 70%
Minimise: Investment costs Annual operating costs
34
Case Study Summary
35
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
36
Concluding RemarksFor hybrid batch separation processes: Optimum synthesis and design procedure Multi-criteria optimisation
Simple extension to continuous hybrid processes