Neural net model of batch processes and optimization based on an extended genetic algorithm

The authors investigated the use of neural networks for modeling batch processes. A cascade neural network offered a solution from the experimental data which did not require the detailed knowledge of process kinetics. An extended genetic algorithm was adopted to generate the optimal trajectory for improving the desired process performance. The rule-inducer genetic algorithm is proposed for dynamic optimization of batch processes. The simulation study of a typical biochemical batch process showed that the proposed technique was capable of modeling and optimization of the batch process, properly accounting for the lack of the detailed knowledge of the complicated batch reactor and the complexity of the batch processes.<
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