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MULTIPERIOD DESIGN OF AZEOTROPIC SEPARATION SYSTEMS

This paper presents a comprehensive approach to designing optimal separation plants for azeotropic mixtures involving multiple feeds and flow rates. It addresses key challenges such as combinatorial complexity, nonlinear behavior, and recycling streams. The research highlights innovative methodologies, including task assignment, simulation, and the use of asynchronous teams and software agents. Economic factors are considered in the design through cost functions related to feed, distillate, trays, and reflux. The findings aim to enhance efficiency in separation processes while minimizing operational costs.

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

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

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

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

  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. Solve / Optimize Library Initialize SIMULATION Modify

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

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

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

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

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

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

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

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

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

  29. DATA COLLECTION

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

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

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

  33. S S S Slack Zero SIMULATION

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

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

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

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

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

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

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

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

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

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

  44. TASK ASSIGNMENT

  45. TASK ASSIGNMENT

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

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

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

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

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

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