Cheema Jitender Jit Singh, Sankpal Narendra V, Tambe Sanjeev S, Kulkarni Bhaskar D
Chemical Engineering Division, National Chemical Laboratory, Pune 411008, India.
Biotechnol Prog. 2002 Nov-Dec;18(6):1356-65. doi: 10.1021/bp015509s.
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.
本文提出了两种用于葡萄糖转化为葡萄糖酸间歇生物过程建模与优化的混合策略。在这些混合方法中，首先使用一种新颖的人工智能形式主义，即遗传编程（GP），仅根据历史过程输入 - 输出数据来开发过程模型。在下一步中，使用两种随机优化（SO）形式主义，即遗传算法（GA）和同时扰动随机逼近（SPSA），对基于GP的模型的输入空间（代表过程操作条件）进行优化。这些SO形式主义相对于常用的基于梯度的优化技术具有某些独特优势。GP - GA和GP - SPSA混合技术的主要优点是，无需调用过程现象学的详细知识，就可以仅根据过程输入 - 输出数据进行过程建模和优化。GP - GA和GP - SPSA技术已用于葡萄糖转化为葡萄糖酸生物过程的建模和优化，并将由此获得的优化过程操作条件与使用另外两种整合人工神经网络（ANN）和GA/SPSA形式主义的混合建模 - 优化范式获得的条件进行了比较。最后，通过实验验证，GP - GA方法给出的总体优化操作条件使葡萄糖酸产量有了显著提高。本文提出的混合策略本质上是通用的，可用于各种间歇和连续生物过程的建模与优化。
