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MULTIPERIOD DESIGN OF AZEOTROPIC SEPARATION SYSTEMS

MULTIPERIOD DESIGN OF AZEOTROPIC SEPARATION SYSTEMS. Kenneth H. Tyner and Arthur W. Westerberg. OVERVIEW. Problem Description Problem Challenges Previous Work Related Research Issues Solution Approach Conclusions. F1. F2. PROBLEM DESCRIPTION. B. Design An Optimal Separation Plant

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MULTIPERIOD DESIGN OF AZEOTROPIC SEPARATION SYSTEMS

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

  2. OVERVIEW • Problem Description • Problem Challenges • Previous Work • 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 DESCRIPTION FEED 1 FEED 2 FEED 3

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

  12. MULTIPERIOD DESIGN • Constraints: • Column Dimensions • Heat Exchanger Dimensions • Flooding Conditions

  13. MULTIPERIOD DESIGN • Collocation Models: • Number of Trays and Feed Location Variable • Variable Transformations

  14. MULTIPERIOD DESIGN

  15. EXTEND TO AZEOTROPIC MULTIPERIOD DESIGN? • Additional Feasibility Constraints • How Many Columns? • Large Number of Simulations • Stream Characteristics Change

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

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

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

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

  20. S S S Slack Zero SIMULATION

  21. Solve / Optimize Library Initialize SIMULATION Modify

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

  23. Solve / Optimize Library Initialize SIMULATION Modify

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

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

  26. ECONOMICS Cost = F( Feed, Distillate, Trays, Reflux ) Separation Task Contribution

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

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

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

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

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

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

  33. DATA COLLECTION

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

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

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

  37. S S S Slack Zero SIMULATION

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

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

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

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

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

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

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

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

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

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

  48. TASK ASSIGNMENT

  49. TASK ASSIGNMENT

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

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