Data-Driven Soft Sensing for Batch Processes Using Neural Network-Based Deep Quality-Relevant Representation Learning

Soft sensors provide a means to reliably estimate unmeasurable variables, thereby playing a prevalent role in formulating closed-loop control in batch processes. In soft sensor development, enhancing quality-relevant information and eliminating quality-irrelevant information are important. This study proposes a neural network-based deep quality-relevant representation learning approach to improve the soft sensing performance in dynamic batch processes. The structure of a deep neural network is optimized in a layer-by-layer manner. First, given the generally abundant predictor variables, the maximal relevance–minimal redundancy criterion is used to optimize the input layer, select the most beneficial variables, and eliminate modeling redundancy. Second, mutual information-based quality-relevant representation selection is performed in the middle layers to enhance the quality-relevant information and eliminate the influence of irrelevant representations. Third, deep quality-relevant representations are extracted, and a soft sensor model is developed. The proposed method is tested on a fed-batch penicillin fermentation process and an industrial injection molding process. Lastly, it shows that the proposed method outperforms several state-of-the-art approaches, thereby confirming its effectiveness.