Processes synthesis and design of distillation sequences using modular simulators: a genetic algorithm framework
An optimization framework is proposed in this work for the synthesis and design of complex distillation sequences, based on a modified genetic algorithm (GA) coupled with a sequential process simulator. The use of a simulator facilitates the formulation of rigorous models for different process alter...
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| Veröffentlicht in: | Computers & chemical engineering Jg. 28; H. 8; S. 1223 - 1236 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
15.07.2004
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| ISSN: | 0098-1354, 1873-4375 |
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| Abstract | An optimization framework is proposed in this work for the synthesis and design of complex distillation sequences, based on a modified genetic algorithm (GA) coupled with a sequential process simulator. The use of a simulator facilitates the formulation of rigorous models for different process alternatives, while the genetic algorithm allows the solutions of the complex non-convex mathematical problem, involving discrete and continuous decisions. To reduce the computational requirements of the optimization procedure, several strategies are proposed, including a novel stopping criterion, which provides an efficient way to end the calculations when the optimal solution has been found. The implementation of these strategies resulted in reductions up to 60% in CPU time for the synthesis of complex distillation systems, succeeding in problems where deterministic mathematical algorithms had failed. |
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| AbstractList | An optimization framework is proposed in this work for the synthesis and design of complex distillation sequences, based on a modified genetic algorithm (GA) coupled with a sequential process simulator. The use of a simulator facilitates the formulation of rigorous models for different process alternatives, while the genetic algorithm allows the solutions of the complex non-convex mathematical problem, involving discrete and continuous decisions. To reduce the computational requirements of the optimization procedure, several strategies are proposed, including a novel stopping criterion, which provides an efficient way to end the calculations when the optimal solution has been found. The implementation of these strategies resulted in reductions up to 60% in CPU time for the synthesis of complex distillation systems, succeeding in problems where deterministic mathematical algorithms had failed. |
| Author | Acevedo, Joaquin Leboreiro, Jose |
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| Cites_doi | 10.1021/ie9802919 10.1002/aic.690470312 10.1002/aic.690450809 10.1002/aic.690480311 10.1016/S0098-1354(98)00059-3 10.1016/0098-1354(93)E0010-7 10.1016/S0098-1354(98)00252-X 10.1016/0098-1354(83)80009-X 10.1016/0098-1354(95)00156-V 10.1016/0098-1354(88)87003-0 10.1021/ie00023a069 10.1002/aic.690310908 10.1021/ie980308n 10.1016/0098-1354(91)85009-J 10.1016/S0098-1354(98)00254-3 10.2514/3.13069 10.1029/2000WR900231 10.1016/S0098-1354(00)00653-0 10.1016/0098-1354(92)80008-W |
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| Keywords | Process optimization Process simulation Genetic algorithms Distillation sequences |
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| SubjectTerms | Distillation sequences Genetic algorithms Process optimization Process simulation |
| Title | Processes synthesis and design of distillation sequences using modular simulators: a genetic algorithm framework |
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