Online Product Quality Prediction for Multi-phase Based on Local Model

Authors

Li Yuan, 1. College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China;;
Yayun Yan, 1. College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China;;
Xiaochu Tang, 2. Shenyang Aerospace University, Automation College, Shenyang 110136, China;

Abstract

Abstract: For offline quality prediction accuracy for batch process, an online prediction method for multi-phase product quality was proposed based on the local model. According to the repetitive cycle of batch process, batch process could be divided into stable phase and transitional phase using the repeatability factor. The least squares support vector machine (LSSVM) model was established in stable phase using the time slice of same phase position, and the LSSVM model was established in transitional phase using optimal subset based on diffusion distance, which made the natural properties of current stable phase and transitional phase be similar to the natural properties of historical stable phase and transitional phase respectively, and the product quality which is difficult to be measured could be obtained. The application to penicillin fermentation process generated in Pensim simulation platform shows that the method based on multi-phase online prediction has better predictive performance than overall offline prediction.

Recommended Citation

Yuan, Li; Yan, Yayun; and Tang, Xiaochu (2020) "Online Product Quality Prediction for Multi-phase Based on Local Model," Journal of System Simulation: Vol. 28: Iss. 4, Article 27.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss4/27

First Page

966

Revised Date

2015-02-15

DOI Link

https://doi.org/

Last Page

971

CLC

TP29

Recommended Citation

Li Yuan, Yan Yayun, Tang Xiaochu. Online Product Quality Prediction for Multi-phase Based on Local Model[J]. Journal of System Simulation, 2016, 28(4): 966-971.