Batch process quality monitoring based on temporal convolutional networks with depthwise separable coordinated attention module

Quality monitoring is an important tool for ensuring the safe operation of batch processes and the high quality of the final products. However, the inherent non-linear, dynamic, and batch characteristics make the quality monitoring of batch process still have some difficulties. To solve these problems, this paper proposes a batch quality monitoring model based on a temporal convolutional network with a depthwise separable coordinated attention module. Firstly, a method of data unfolding incorporating sliding windows is proposed to unfold and stack the data along the direction of the variables, and a variable selection method of maximum information coefficient fused with Monte Carlo sampling is proposed to select the process variables related to the quality variables. Secondly, we take the traditional temporal convolutional network as the base network and decouple the correlation between the batch data by using depthwise separable convolution. At the same time, we utilize coordinate attention to extract data features in different spatial directions to ensure the effectiveness of quality monitoring. Finally, the feasibility and robustness of the proposed model are verified by a nonlinear numerical example and an industrial-scale penicillin fermentation process. The experimental results show the proposed model has lower false alarm rate and false negative rate and can be used to maintain the product quality of the actual batch process.