Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation
Metadata of CSIR Papers
View Archive Info| Field | Value | |
| Title |
Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation
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| Creator |
Cheema, JJS
Sankpal, NV Tambe, SS Kulkarni, BD |
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| Subject |
Biotechnology & Applied Microbiology; Food Science & Technology
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| Description |
This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.
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| Publisher |
AMER CHEMICAL SOCWASHINGTON1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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| Date |
2011-09-24T09:50:15Z
2011-09-24T09:50:15Z 2002 |
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| Type |
Article
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| Identifier |
BIOTECHNOLOGY PROGRESS
8756-7938 http://hdl.handle.net/123456789/24204 |
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| Language |
English
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