Data-Driven Quality Prediction of Batch Processes Based on Minimal-Redundancy-Maximal-Relevance Integrated Convolutional Neural Network

For batch processes that are extensively applied in modern industry and characterized by nonlinearity and dynamics, quality prediction is significant to obtain high-quality products and maintain production safety. However, some quality variables and key performance indicators are difficult to measure online. In addition, the mechanism-based model for batch processes is usually tough to acquire due to the strong nonlinearity and dynamics, which makes quality prediction a challenge. With the accumulation of historical process data, data-driven methods for quality prediction gain increasing attention, among which convolutional neural network (CNN) is quite successful for its automatic feature extraction of nonlinear features from raw data. Considering that most CNN-based methods mainly take the variety of extracted features into account and ignore the redundancy between them, this paper introduces the minimal-redundancy-maximal-relevance algorithm to select features obtained by original CNN and further improves it with a feature selection layer to form the proposed method referred as mRMR-CNN. Then, a quality prediction model is established based on mRMR-CNN and the effectiveness of it is verified on the penicillin fermentation process, where the proposed method shows remarkable performance.