A mode partitioning method based on an undecimated lifting scheme packet-instantaneous frequence response function (ULSP-IFRF) for batch processes

Reasonable mode partitioning for batch processes can enhance the accuracy of the prediction model for multimodal quality variables, which is crucial for ensuring product quality. Considering the influence of nonlinearity, temporality, and dynamic characteristics of process data on the rationality of mode partitioning results, a mode partitioning method of batch processes based on an undecimated lifting scheme packet-instantaneous frequency response function (ULSP-IFRF) is proposed in this work. The data for batch processes is first decomposed in the time-frequency domain using an undecimated lifting scheme packet transform, and the discriminant rule of the optimal decomposition layer number is constructed by combining the information entropy and the evaluation index of mode partitioning. The transient frequency response function of the wavelet domain is then introduced to characterize the dynamic time-dependent characteristics of the batch process, and then the fuzzy C-means clustering algorithm is used to perform unsupervised clustering on the IFRF value in the wavelet domain, achieving mode partitioning of the batch process. Finally, the rationality of the mode partitioning results obtained using the proposed method is verified through experiments on the penicillin fermentation process.