MULTIPERIOD DESIGN OF AZEOTROPIC SEPARATION SYSTEMS Kenneth H. Tyner and Arthur W. Westerberg.

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Presentation transcript:

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

PROBLEM DESCRIPTION Design An Optimal Separation Plant Multiple Feeds –Flowrate –Composition –Operating Time Azeotropes A B CAz F1 F3 F2

PROBLEM DESCRIPTION A B CAz F1 F3 F2 F A B C Az

PROBLEM DESCRIPTION A B CAz F1 F3 F2 F A B C

PROBLEM DESCRIPTION FEED 1FEED 3FEED 2

PROBLEM DESCRIPTION FEED 1FEED 3FEED 2

PROBLEM DESCRIPTION FEED 1FEED 3FEED 2

PROBLEM DESCRIPTION FEED 1FEED 3FEED 2

PROBLEM DESCRIPTION FEED 1FEED 3FEED 2

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

MULTIPERIOD DESIGN Constraints: –Column Dimensions –Heat Exchanger Dimensions –Flooding Conditions

MULTIPERIOD DESIGN Collocation Models: –Number of Trays and Feed Location Variable –Variable Transformations

MULTIPERIOD DESIGN

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

INITIAL RESEARCH THRUSTS Synthesize Designs Evaluate Designs Optimize / Modify Designs

AZEOTROPIC SYNTHESIS A B CAz F F A B C

AZEOTROPIC SYNTHESIS A B CAz F A B C F

AZEOTROPIC SYNTHESIS A B CAz F F A B C

SIMULATION Zero Slack S S S

SIMULATION Solve / Optimize Initialize Modify Library

REVISED RESEARCH THRUSTS Collocation Error Detection Scaling Solver Design

SIMULATION Solve / Optimize Initialize Modify Library

SOLUTION APPROACH Approximation –Separation Task –Column Design and Operation Shortcut Costing Autonomous Agents

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

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

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

TASK APPROXIMATION Variables: –Compositions –Flowrates Relations: –Mass Balance –Lever Rule –Geometric Objects A B CAz F D / F D B

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

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

COLUMN APPROXIMATION Reflux = C 1 * exp(-C 2 * Trays) + C 3 Opt Feed Loc = C 4 * Trays + C 5 –Numerical Difficulties Gilliland Correlation

DATA COLLECTION Fix Trays and Task Find Optimal Reflux

DATA COLLECTION

A B CAz Store In Database Calculate Parameters

SIMULATION F A B C Az A B C F Database

SIMULATION F A B C Az A B C F Database

SIMULATION Zero Slack S S S

ASYNCHRONOUS TEAMS Independent Software Agents Shared Memory Trial Points Newton SolverGradient Solver

ASYNCHRONOUS TEAMS Independent Software Agents Shared Memory Trial Points Newton SolverGradient Solver

ASYNCHRONOUS TEAMS Independent Software Agents Shared Memory Trial Points Newton SolverGradient Solver

ASYNCHRONOUS TEAMS Independent Software Agents Shared Memory Trial Points Newton SolverGradient Solver

ASYNCHRONOUS TEAMS Independent Software Agents Shared Memory Advantages –Scalable –Ease of Creation / Maintenance –Cooperation

ASYNCHRONOUS TEAMS Applications –Train Scheduling –Travelling Salesman Problem –Building Design

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

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

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

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

TASK ASSIGNMENT

PATH SELECTION Sequential Selection Genetic Algorithm Active Constraint

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

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

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

MULTIPERIOD DESIGN OF AZEOTROPIC SEPARATION SYSTEMS Kenneth H. Tyner and Arthur W. Westerberg