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Optimal Design of Azeotropic Separation Systems

This paper presents a comprehensive overview of the design and optimization of azeotropic separation systems. It addresses problem challenges, related research issues, and solution approaches, focusing on multi-feed compositions, flowrates, and azeotropes. The study includes simulation, economics, approximation techniques, and the use of asynchronous teams for efficient design processes.

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Optimal 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 • 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

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