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Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation
Author(s) -
Cheema Jitender Jit Singh,
Sankpal Narendra V.,
Tambe Sanjeev S.,
Kulkarni Bhaskar D.
Publication year - 2002
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp015509s
Subject(s) - gluconic acid , bioprocess , simultaneous perturbation stochastic approximation , computer science , mathematical optimization , rotation formalisms in three dimensions , genetic programming , process optimization , stochastic programming , artificial intelligence , mathematics , stochastic process , chemistry , engineering , statistics , geometry , organic chemistry , chemical engineering , environmental engineering
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.