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To deal with the nonlinearity and dynamics in propylene polymerization processes, a novel soft sensor based on quality\u2010relevant slow feature analysis and Bayesian regression is proposed in this paper. The proposed method can handle the dynamics of the process better by extracting quality\u2010relevant slow features, which present both the slowly varying characteristic and the correlations with quality indices. Meanwhile, a Bayesian inference model is developed to predict the quality indices, which takes advantages of a probability framework with iterative maximum likelihood techniques for parameter estimation and a sparse constraint for avoiding overfitting. Finally, a case study is conducted with data sampled from a practical industrial propylene polymerization process to demonstrate the effectiveness and superiority of the proposed method.<\/jats:p>","DOI":"10.1155\/2021\/9985747","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T18:50:30Z","timestamp":1640890230000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Soft Sensor Development Based on Quality\u2010Relevant Slow Feature Analysis and Bayesian Regression with Application to Propylene 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