Soft Sensing Modeling of the SMB Chromatographic Separation Process Based on the Adaptive Neural Fuzzy Inference System

Abstract

Simulated moving bed (SMB) chromatographic separation technology is a new adsorption separation technology with strong separation ability. Based on the principle of the adaptive neural fuzzy inference system (ANFIS), a soft sensing modeling method was proposed for realizing the prediction of the purity of the extract and raffinate components in the SMB chromatographic separation process. The input data space of the established soft sensor model is divided, and the premise parameters are determined by utilizing the meshing partition method, subtractive clustering algorithm, and fuzzy C-means (FCM) clustering algorithm. The gradient, Kalman, Kaczmarz, and PseudoInv algorithms were used to optimize the conclusion parameters of ANFIS soft sensor models so as to predict the purity of the extract and raffinate components in the SMB chromatographic separation process. The simulation results indicate that the proposed ANFIS soft sensor models can effectively predict the key economic and technical indicators of the SMB chromatographic separation process.