A novel Bayesian inference soft sensor for real‐time statistic learning modeling for industrial polypropylene melt index prediction. Issue 40 (19th June 2017)

• Record Type: Journal Article
• Authors: Sun, Yuanmeng; Wang, Yalin; Liu, Xinggao; Yang, Chunhua; Zhang, Zeyin; Gui, Weihua; Chen, Xu; Zhu, Bochao
• Is Part Of: Journal of applied polymer science. Volume 134:Issue 40(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-06-19
• Subjects: sensors and actuators -- theory and modeling -- synthesis and processing techniques; Polymers -- Periodicals; Polymerization -- Periodicals; 668.9
• DOI: 10.1002/app.45384
• Languages: English
• ISSNs: 0021-8995

Abstract

ABSTRACT: A novel real‐time soft sensor based on a sparse Bayesian probabilistic inference framework is proposed for the prediction of melt index in industrial polypropylene process. The Bayesian framework consists of a relevance vector machine for predicting melt index and a particle filtering algorithm for soft sensor optimization. An online correcting strategy is also developed for improving the performance of real‐time melt index prediction. The method takes advantages of the probabilistic inference and using prior statistical knowledge of polymerization process. Developed soft sensors are validated with ten public databases from UCI machine learning repository and real data from industrial polypropylene process. Experimental results indicate the effectiveness of proposed method and show the improvement in both prediction precision and generalization capability compared with the reported models in literatures. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci.2017, 134, 45384. Abstract :