Optimal synthesis of batch separation processes

1. 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 • Optimal synthesis of batch separation processes • Multi-objective optimisation of batch distillation processes • Concluding remarks

6. Optimal synthesis of batch separation processes

7. Configuration Decisions Separation problem ? Process Superstructure Batch Pervaporation Batch Hybrid Batch Distillation

8. Design and Operation Decisions Design Alternatives Operational Alternatives Min capital cost • Vapour loading rate • Reflux/reboil ratios • Recovery/No. batches • Withdrawal rate • Task durations • Trays • Membrane stages • Membrane modules Min running cost

9. Process Superstructure Ns , Nm,s Rc Nt Rr Qs Lr P Fs Retentate Permeate Rp Feed Offcut Qr

10. Product 1 Rc Offcut Nt Rp Product 2 Qr Reboiler Batch Distillation

11. Batch Pervaporation Separation Stage Ns Nm,s Rr Retentate Permeate P Feed Rp Qf Offcut

12. Rc Product Feed Nt Ns Nm,s Rp P Qr Reboiler Permeate Offcut Hybrid Distillation I

13. Ns Nm,s Rc Nt P Retentate Rp Offcut Feed Qr Permeate Hybrid Distillation II

14. Rc Ns , Nm,s Nt Rr P Fs Lr Retentate Rp Permeate Feed Rpr Qr Offcut Hybrid Distillation III

15. 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 DAE/PDAE, nonlinear Design variable bounds discrete and continuous Operational variable bounds continuous To determine : Design variables Operation variables (time dependent)

16. 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)

17. Optimisation Implementation Genetic Algorithm Module GAlib Genome Set Simulation Output Batch Distillation/Pervap Model gPROMS Physical Properties Model State Thermodynamics Model 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. Ns Nm,s Rc Nt Rr P Lr Fs Rp Feed Qr Case Study Superstructure Retentate Permeate Offcut

21. Optimal Process - Hybrid Rr 1.00 – 1.8% 0.83 – 96.3% 0.24 – 1.9% Nm = 2 To = 330 K Lr =3 Fside = 2.5 mole/s Nt = 30 Rp 0.79 – 1.8% 1.00 – 96.3% 0.88 – 1.9% tf = 5119 s P = 300 Pa Retentate Fs = 9 Profit 18.07 M£/yr VReb = 5 mole/s Permeate Feed Offcut

22. Fixed Configuration – Distillation only Rr 1.00 – 0.10% 0.68 – 99.7% 0.70 – 0.20% Product 1 Offcut Product 2 Nt = 30 tf = 8964 s Rp 1.00 – 0.10% 1.00 – 99.7% 0.00 – 0.20% VReb = 5 mole/s Profit 14.30 M£/yr -26% Reboiler

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. Product 1 Rc Offcut Nt Rp Product 2 Qr Reboiler Batch Distillation

26. Problem Formulation – Objective Function Minimise Investment Costs Minimise Operating 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)

27. f(x) x 0 x* Optimisation Single-objective optimisation: To find a single optimal solution x* of a single objective functionf(x) Multi-objective optimisation: To find array of “Pareto optimal” solutions with respect to multiple objective functions

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

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

30. Ranking Max = 1 F2 2 3 better 2 3 2 2 3 3 3 3 F1 better

31. 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)

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