Deep quality-related stacked isomorphic autoencoder for batch process quality prediction

Abstract

Batch processes play an important role in modern chemical industrial and manufacturing production, while the control of product quality relies largely on online quality prediction. However, the complex nonlinearities of batch process and the dispersion of quality-related features may affect the quality prediction performance. In this paper, a deep quality-related stacked isomorphic autoencoder for batch process quality prediction is proposed. Firstly, the same raw input data is reconstructed layer-by-layer by isomorphic autoencoder and the raw data features are obtained. Secondly, the correlation between the isomorphic representations of each layer and the output is analyzed by maximum information coefficient to construct the relevant loss function and enhance the quality-related information. Thirdly, deep quality-related prediction model is constructed to predict the batch process quality variables. Finally, the effectiveness of the proposed method is verified by applying on penicillin fermentation process.