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.
Highlights
Simulated moving bed (SMB) chromatographic separation technology is a new separation technology developed on the basis of traditional fixed bed adsorption operation and true moving bed (TMB) chromatographic separation technology [1]
According to the above simulation results, it can be seen that the input data spatial division and premise parameter determination are realized by the subtractive clustering algorithm and adaptive neural fuzzy inference system (ANFIS) soft sensor models based on the gradient, Kalman, and PseudoInv algorithms for optimizing conclusion parameters have the better prediction results on the key economic and technical indicators of the SMB chromatographic separation process
According to the above simulation results, it can be seen that the input data spatial division and premise parameter determination are realized by utilizing the fuzzy C-means (FCM) clustering algorithm and ANFIS soft sensor models based on the Kalman, Kaczmarz, and PseudoInv algorithms for optimizing conclusion parameters have the better prediction results on the key economic and technical indicators of the SMB chromatographic separation process
Summary
SMB chromatographic separation technology is a new separation technology developed on the basis of traditional fixed bed adsorption operation and true moving bed (TMB) chromatographic separation technology [1]. SMB chromatography technology is the cutting-edge technology in separation science, which preserves the high separation rate, low energy consumption, and low material consumption of the chromatogram It introduces the continuous, countercurrent, rectification, reflux, and other mechanisms of moving bed technology. Compared with the existing chemical separation technology (distillation, extraction, single-column chromatography), this technology can achieve automatic continuous separation, which can increase separation capacity and improve product yield, yield, and efficiency. It is the key technology for the chemical separation process and can reuse the stationary phase and mobile phase to reduce cost and energy consumption [2].
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