Just-in-Time Kernel Learning with Adaptive Parameter Selection for Soft Sensor Modeling of Batch Processes

Abstract

An efficient nonlinear just-in-time learning (JITL) soft sensor method for online modeling of batch processes with uneven operating durations is proposed. A recursive least-squares support vector regression (RLSSVR) approach is combined with the JITL manner to model the nonlinearity of batch processes. The similarity between the query sample and the most relevant samples, including the weight of similarity and the size of the relevant set, can be chosen using a presented cumulative similarity factor. Then, the kernel parameters of the developed JITL-RLSSVR model structure can be determined adaptively using an efficient cross-validation strategy with low computational load. The soft sensor implement algorithm for batch processes is also developed. Both the batch-to-batch similarity and variation characteristics are taken into consideration to make the modeling procedure more practical. The superiority of the proposed soft sensor approach is demonstrated by predicting the concentrations of the active biomass and recombinant protein in the streptokinase fed-batch fermentation process, compared with other existing JITL-based and global soft sensors