Profitability related industrial-scale batch processes monitoring via deep learning based soft sensor development

Abstract

Data-driven soft sensor technology has been widely developed to estimate quality-related variables, while following difficulties still limit its application in batch processes, such as different initial conditions, uneven-length of batches, and the extraction of within-batch multiphase features. To address these problems, a quality prediction and monitoring framework is proposed. Variables related to quality-related variables are first selected, and a data stacked strategy is proposed to transform three-dimensional batch data into time-lagged sequences that can be fed into soft sensor models. Aiming to extract the multiphase features, a novel differential recurrent neural networks is established by embedding differential operations into long short-term memory neural networks. Moreover, to ensure profitability, prediction residuals are employed for quality monitoring. Case study on a simulation dataset and an industrial-scale penicillin fermentation process demonstrates the effectiveness of the proposed method and its applicability to batch process monitoring and control in both academic research and industrial operation.