Developing an accurate model for severe nonlinear chemical processes presents a significant challenge, as it directly impacts profitability and optimal control strategies. The main aim of this work is to find an optimal model architecture capable of presenting all system modes and subsequently reducing control effort. Improving model accuracy while considering computational burden is identified as the most effective approach for achieving this goal. In this paper, the reactor is treated as a black-box model, and a new optimized model for bioreactors is proposed based on the combination of the Bee Colony Algorithm and Wavelet Neural Network (WNN). Input-output data for training and validation of the identified model are generated by applying a persistently exciting signal. A salient feature of the suggested hybrid Bee-WNN algorithm is the application of a global searching technique for optimizing the initial weights of the neural network, instead of stochastic selection through the Bee Colony approach. Additionally, the wavelet neural network algorithm is utilized to update the optimal weighting via the Levenberg Marquardt training method, thereby enhancing exploitation search abilities. To assess the usefulness of the demonstrated procedure in identification and modeling, a practical fermentation bioreactor is selected as a source of data. Furthermore, two methods, BP-ANN and ABC-WNN, which have already been presented in the research, are applied to equivalent datasets, and the obtained results are compared with those produced by the proposed methodology of the paper. Based on the results, the new extracted model improves the accuracy of the identified model more than 10 times compared to the previous training algorithm. Moreover, from a statistical perspective, the goodness of model fitting is enhanced by 0.03.
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