Reaction Modeling and Optimization Using Neural Networks and Genetic Algorithms: Case Study Involving TS-1-Catalyzed Hydroxylation of Benzene
Reaction Modeling and Optimization Using Neural Networks and Genetic Algorithms: Case Study Involving TS-1-Catalyzed Hydroxylation of Benzene
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使用神经网络和遗传算法进行反应建模和优化：涉及 TS-1 催化苯羟基化的案例研究
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发表时间：
2002
期刊：
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通讯作者：
B. Kulkarni
中科院分区：
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作者：
Somnath Nandi;Priyabrata Mukherjee;S. Tambe;R. Kumar;B. Kulkarni
本文提出了一种集成人工神经网络（ANN）和遗传算法（GAs）的混合过程建模和优化方法。由此产生的ANN-GA策略的优点是，它允许过程建模和优化的基础上，专门的过程输入-输出数据。在混合策略中，首先从输入输出过程数据开发基于ANN的过程模型。接下来，使用遗传算法优化表示过程输入变量的模型的输入空间，以同时最大化多个过程输出变量。遗传算法是一种随机优化方法，与常用的基于梯度的确定性算法相比具有某些独特的优势。的混合形式主义的功效已被评估为建模和优化沸石（TS-1）催化的苯羟基化苯酚反应，从而获得了几套优化的操作条件。并对几种优化方案进行了实验验证。
This paper proposes a hybrid process modeling and optimization formalism integrating artificial neural networks (ANNs) and genetic algorithms (GAs). The resultant ANN−GA strategy has the advantage that it allows process modeling and optimization exclusively on the basis of process input−output data. In the hybrid strategy, first an ANN-based process model is developed from the input−output process data. Next, the input space of the model representing process input variables is optimized using GAs, with a view to simultaneously maximize multiple process output variables. The GAs are stochastic optimization methods possessing certain unique advantages over the commonly used gradient-based deterministic algorithms. The efficacy of the hybrid formalism has been evaluated for modeling and optimizing the zeolite (TS-1)-catalyzed benzene hydroxylation to phenol reaction whereby several sets of optimized operating conditions have been obtained. A few optimized solutions have also been subjected to the experimenta...