Artificial neural-network-assisted stochastic process optimization strategies
Metadata of CSIR Papers
View Archive Info| Field | Value | |
| Title |
Artificial neural-network-assisted stochastic process optimization strategies
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| Creator |
Nandi, S
Ghosh, S Tambe, SS Kulkarni, BD |
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| Subject |
Engineering, Chemical
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| Description |
This article presents two hybrid robust process optimization approaches integrating artificial neural networks (ANN) and stochastic optimization formalisms-genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). An ANN-based process model was developed solely from process input - output data and then its input space comprising design and operating variables was optimized by employing either the GA or the SPSA methodology. These methods possess certain advantages over widely used deterministic gradient-based techniques. The efficacy of ANN-GA and ANN-SPSA formalisms in the presence of noise-free as well as noisy process data was demonstrated for a representative system involving a nonisothermal CSTR. The case study considered a nontrivial optimization objective, which, in addition to the conventional parameter design also addresses the issue of optimal tolerance design. Comparison of the results with those from a robust deterministic modeling/optimization strategy suggests that the hybrid methodologies can be gainfully employed for process optimization.
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| Publisher |
AMER INST CHEMICAL ENGINEERSNEW YORK3 PARK AVE, NEW YORK, NY 10016-5901 USA
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| Date |
2011-09-24T09:29:13Z
2011-09-24T09:29:13Z 2001 |
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| Type |
Article
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| Identifier |
AICHE JOURNAL
0001-1541 http://hdl.handle.net/123456789/24162 |
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| Language |
English
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