Probabilistic combination of local independent component regression model for multimode quality prediction in chemical processes

• Local ICR model is developed under each operation mode of the industrial process.
• Different local ICR models are combined through Bayesian inference and analysis.
• A probabilistic multiple ICR model is formulated for multimode quality prediction.
• The efficiency of the new method is evaluated through two case studies.

In this paper, a probabilistic combination form of the local independent component regression (ICR) model is proposed for quality prediction of chemical processes with multiple operation modes. Through the introduction of the Bayesian inference strategy, the posterior probabilities of the data sample in different operation modes are calculated upon two monitoring statistics of the independent component analysis (ICA) model. Then, based on the combination of local ICR models in different operation modes, a probabilistic multiple ICR (MICR) model is developed. Meanwhile, the operation mode information of the data sample is located through posterior analysis of the new model. To evaluate the multimode quality prediction performance of the proposed method, two case studies are provided.