Optimal synthesis of batch separation processes Taj Barakat and Eva Sørensen University College London iCPSE Consortium Meeting, Atlanta, 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 PervaporationBatch Hybrid

8 Design and Operation Decisions Design Alternatives Operational Alternatives Min capital cost Min running 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 NtNt RcRc QrQr RpRp NsNs, N m,s P RrRr LrLr FsFs QsQs

10 Batch Distillation Product 1 Product 2 Offcut Reboiler NtNt RcRc QrQr RpRp

11 Batch Pervaporation Offcut Feed Separation Stage Retentate Permeate NsNs N m,s RrRr RpRp P QfQf

12 Hybrid Distillation I Feed Product Permeate Reboiler Offcut NtNt RcRc QrQr RpRp P NsNs N m,s

13 Hybrid Distillation II Feed Retentate Permeate Offcut NtNt RcRc QrQr RpRp P N s N m,s

14 Hybrid Distillation III Retentate Permeate Offcut Feed NtNt RcRc QrQr RpRp NsNs, N m,s P R pr LrLr FsFs RrRr

15 Problem Formulation – Objective Function Maximise Annual Profit = Revenues – Operating Costs Batch Processing Time Av. Time – Capital Costs Subject to : Model equationsDAE/PDAE, nonlinear Design variable boundsdiscrete and continuous Operational variable boundscontinuous To determine : Design variables Operation variables (time dependent) Nonlinear, (OC/CC, Guthrie’s correlations)

16 Problem Formulation - Solution n DAE n gPROMS (Process Systems Enterprise Ltd., 2005) n MIDO n 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 Genetic Algorithm Module Batch Distillation/Pervap Model Thermodynamics Model 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

21 Optimal Process - Hybrid Feed Retentate Permeate Offcut R p 0.79 – 1.8% 1.00 – 96.3% 0.88 – 1.9% R r 1.00 – 1.8% 0.83 – 96.3% 0.24 – 1.9% L r =3 N t = 30 F s = 9 V Reb = 5 mole/s F side = 2.5 mole/s P = 300 Pa N m = 2 Profit M£/yr t f = 5119 s T o = 330 K

22 Fixed Configuration – Distillation only Product 1 Product 2 Offcut Reboiler R p 1.00 – 0.10% 1.00 – 99.7% 0.00 – 0.20% R r 1.00 – 0.10% 0.68 – 99.7% 0.70 – 0.20% N t = 30 V Reb = 5 mole/s t f = 8964 s Profit 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 NtNt RcRc QrQr RpRp

26 Problem Formulation – Objective Function Minimise Investment Costs Subject to : Model equationsDAE/PDAE, nonlinear Design variable boundsdiscrete and continuous Operational variable boundscontinuous 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 x x* f(x)f(x) 0

28 Multiobjective Optimization Problem Maximize subject to Several Pareto-optimal sets Pareto Optimal Solutions Minimise

29 Ranking if solution is infeasible if solution is feasible but dominated if solution is feasible and non- dominated

30 Ranking 3 F2F2 F1F1 3 better Max =

31 Problem Formulation - Solution n DAE n gPROMS (Process Systems Enterprise Ltd., 2005) n MO-MIDO n 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 Remarks For hybrid batch separation processes: Optimum synthesis and design procedure Multi-criteria optimisation Simple extension to continuous hybrid processes