An online prediction of quality variables based on DCC-RVM for multimode batch processes

Batch processes have multimode characteristics, and the existing methods of mode partition for batch processes ignore the causal relationship between the process characteristics and the mode centers, which directly affects the accuracy and reasonableness of the mode partition results and the prediction precision and generalizability of the models. A method for the online prediction of quality variables based on deep causal clustering-relevance vector machine (DCC-RVM) for multimode batch processes is proposed in this work. By combining density peak clustering, with the strong nonlinearity of the batch process data, the deep features of the process data are first extracted using a deep autoencoder. The process model for batch processes based on long short-term memory (LSTM) is then constructed, with the causal relationship between mode centers and the LSTM process model as a constraint, and the mode centers selection strategy is constructed to obtain reasonable mode centers; Subsequently, by considering the temporal features of the process data, the non-mode center samples are assigned to the corresponding modes based on the relative distance between samples, and the partition points and data sets are obtained for each mode; Finally, the quality variable prediction model for each mode is established using RVM, and the batch process quality variable is predicted online. The experimental results show that the mode partition by DCC is reasonable. The root mean square error (RMSE) and R² for predictiions using our DCC-RVM model are 0.0104 and 0.9995, respectively.