A multiphase information fusion strategy for data-driven quality prediction of industrial batch processes

As one of the most important modes of industrial production, the batch process often involves complex and continuous physicochemical reactions, making it challenging to pro-duce identical products between different batch runs even under the same working condi-tion. Optimization and parameter adjustments depend mainly on a good quality prediction model. However, this industrial process has "3M" characteristics of multiple process vari-ables, multiple production phases, and multiple quality indicators, which bring consider-able challenges to the accuracy and robustness of the model. This study proposes a multiphase information fusion strategy for data-driven quality prediction of industrial batch processes. Firstly, aiming for real-world industrial datasets with different sampling frequencies, two types of state variables are summarized, and the multiphase-based cumu-lative quality model is developed. Secondly, information theory with copula entropy is employed to characterize the association relationships between each state variable and the set of multiple quality indicators; thus, phase-specific critical variables are selected by ranking copula entropy. Lastly, a stacking multiway random forest algorithm is pro-posed to develop the prediction relationship between phase-specific critical variables and multiple quality indicators. Experiments on a real-world industrial dataset have demonstrated that the proposed method has better accuracy and stronger robustness than previous baseline methods.(c) 2022 Elsevier Inc. All rights reserved.