MULTIPERIOD DESIGN OF AZEOTROPIC SEPARATION SYSTEMS

1. MULTIPERIOD DESIGN OFAZEOTROPIC SEPARATIONSYSTEMS Kenneth H. Tyner and Arthur W. Westerberg

2. OVERVIEW • Problem Description • Problem Challenges • Related Research Issues • Solution Approach • Conclusions

3. F1 F2 PROBLEM DESCRIPTION B • Design An Optimal Separation Plant • Multiple Feeds • Flowrate • Composition • Operating Time • Azeotropes F3 A Az C

4. F1 F2 PROBLEM DESCRIPTION B A C F B Az F3 A Az C

5. F1 F2 PROBLEM DESCRIPTION B A C F B F3 A Az C

6. PROBLEM DESCRIPTION FEED 1 FEED 2 FEED 3

7. PROBLEM DESCRIPTION FEED 1 FEED 2 FEED 3

8. PROBLEM DESCRIPTION FEED 1 FEED 2 FEED 3

9. PROBLEM DESCRIPTION FEED 1 FEED 2 FEED 3

10. PROBLEM CHALLENGES • Highly Combinatorial • Separation Pathways • Process Units • Task Assignment • Difficult Subproblems • Large Models • Highly Nonlinear • Recycle Streams • Shared Equipment

11. INITIAL RESEARCH THRUSTS • Synthesize Designs • Evaluate Designs • Optimize / Modify Designs

12. AZEOTROPIC SYNTHESIS B A C F B Az F A Az C

13. AZEOTROPIC SYNTHESIS B A C F B Az F A Az C

14. AZEOTROPIC SYNTHESIS B A C F B F A Az C

15. SIMULATION

16. S S S Slack Zero SIMULATION

17. Solve / Optimize Library Initialize SIMULATION Modify

18. REVISED RESEARCH THRUSTS • Collocation Error Detection • Scaling • Solver Design

19. SOLUTION APPROACH • Approximation • Separation Task • Column Design and Operation • Shortcut Costing • Autonomous Agents

20. ECONOMICS Cost = F( Feed, Distillate, Trays, Reflux )

21. ECONOMICS Cost = F( Feed, Distillate, Trays, Reflux ) Separation Task Contribution Column Design and Operation Contributions

22. F TASK APPROXIMATION B • Variables: • Compositions • Flowrates • Relations: • Mass Balance • Lever Rule • Geometric Objects B D A Az C

23. F D / F TASK APPROXIMATION B • Variables: • Compositions • Flowrates • Relations: • Mass Balance • Lever Rule • Geometric Objects B D A Az C

24. COLUMN APPROXIMATION • Cost = F(Feed, Distillate, Trays, Reflux) • Reflux = F(Trays, Feed Location)

25. COLUMN APPROXIMATION • Cost = F(Feed, Distillate, Trays, Reflux) • Reflux = F(Trays) • Optimal Feed Location = F(Trays)

26. Numerical Difficulties COLUMN APPROXIMATION • Gilliland Correlation • Reflux = C1 * exp(-C2 * Trays) + C3 • Opt Feed Loc = C4 * Trays + C5

27. DATA COLLECTION • Fix Trays and Task • Find Optimal Reflux

28. DATA COLLECTION

29. Calculate Parameters Store In Database DATA COLLECTION B A Az C

30. F Database SIMULATION A C B F B Az A Az C

31. F Database SIMULATION A C B F B Az A Az C

32. Slack Zero SIMULATION S S S

33. Newton Solver Gradient Solver Trial Points ASYNCHRONOUS TEAMS • Independent Software Agents • Shared Memory

34. ASYNCHRONOUS TEAMS • Independent Software Agents • Shared Memory Newton Solver Gradient Solver Trial Points

35. ASYNCHRONOUS TEAMS • Independent Software Agents • Shared Memory Newton Solver Gradient Solver Trial Points

36. ASYNCHRONOUS TEAMS • Independent Software Agents • Shared Memory • Advantages • Scalable • Ease of Creation / Maintenance • Cooperation

37. ASYNCHRONOUS TEAMS • Applications • Train Scheduling • Travelling Salesman Problem • Building Design

38. ASYNCHRONOUS TEAMS Approximation Agents Database Approximation Data Problem Description Designs Design Agents

39. MINLP DESIGN AGENT • Fixed: • Separation Pathways • Intermediate Streams • Variable: • Task Assignment • Number of Columns • Column Dimensions • Operating Policy

40. MINLP DESIGN AGENT • Fixed: • Separation Pathways • Intermediate Streams • Variable: • Task Assignment • Number of Columns • Column Dimensions • Operating Policy

41. TASK ASSIGNMENT

42. TASK ASSIGNMENT

43. TASK ASSIGNMENT

44. PATH SELECTION • Sequential Selection • Genetic Algorithm • Active Constraint

45. MINLP DESIGN AGENT • Fixed: • Separation Pathways • Intermediate Streams • Variable: • Task Assignment • Number of Columns • Column Dimensions • Operating Policy

46. GENERAL BENEFITS • Alternative to Hierarchical Design • Persistent Data • Scenario Analysis • Human Agents