聚合过程贝叶斯统计学习质量模式监测

doi:10.16085/j.issn.1000-6613.2018-0595

化工进展 ›› 2018, Vol. 37 ›› Issue (12): 4558-4564.DOI: 10.16085/j.issn.1000-6613.2018-0595

聚合过程贝叶斯统计学习质量模式监测

高爽, 郑年年, 栾小丽, 刘飞

江南大学自动化研究所, 轻工过程先进控制教育部重点实验室, 江苏无锡 214122

收稿日期:2018-03-25 修回日期:2018-08-07 出版日期:2018-12-05 发布日期:2018-12-05
通讯作者: 栾小丽,教授,博士生导师,研究方向为复杂系统建模、控制及优化。
作者简介:高爽(1995-),女,硕士研究生,研究方向为贝叶斯统计学习过程模式监测。
基金资助:
国家自然科学基金项目（61722306，61473137）。

Quality pattern monitoring for polymerization process based on Bayesian statistical learning

GAO Shuang, ZHENG Niannian, LUAN Xiaoli, LIU Fei

Key Laboratory of Advanced Process Control for Light Industry of the Ministry of Education, Institute of Automation, Jiangnan University, Wuxi 214122, Jiangsu, China

Received:2018-03-25 Revised:2018-08-07 Online:2018-12-05 Published:2018-12-05

摘要/Abstract

摘要： 聚合过程由于反应机理复杂、过程非线性强等特点，使得其质量监控问题成为过程控制领域的研究难点和热点。本文结合聚合反应过程的特点，利用聚合生产过程中与聚合物的物性参数相关的过程参数数据，引入质量模式监测的概念，创新性地提出了基于模式指标的贝叶斯统计学习方法，以解决聚合过程的质量模式监测问题。首先利用主成分分析将观测空间数据的本质特征投影到低维空间，得到模式指标；然后，基于贝叶斯统计学习算法，根据后验概率对构建的质量模式进行判别；最后利用某化工厂提供的聚合反应釜的生产数据对所提方法进行验证。由于模式指标相比于质量指标如转化率、聚合速率等，更能反映聚合反应过程的一致性和产品品质，因此基于模式指标的贝叶斯判别法较基于参数指标的贝叶斯判别法更加准确，更能实现对聚合过程的质量监控。

关键词: 聚合过程, 模式监测, 质量监控, 贝叶斯, 主元分析, 实验验证

Abstract: Due to the strong non-linearity and complexity of mechanisms, the quality control of the polymerization process becomes a difficult and hot topic in the field of process control. By combining the characteristics of the polymerization process and using the process parameter data related to the physical properties of the polymer produced in the process of polymerization production, the concept of quality pattern monitoring was firstly introduced. This paper innovatively proposed a Bayesian statistical learning method based on pattern index to solve the quality pattern monitoring problem of the polymerization process. Firstly, the principal component analysis was used to project the essential features of the observational spatial data into the low-dimensional space to obtain the pattern index. Then, the Bayesian statistical learning method was applied to distinguish the constructed quality pattern based on posterior probability. Finally the proposed method was verified using the production data of the polymerization reactor provided by a chemical plant. Compared with the quality index such as conversion rate and polymerization rate, the pattern index can better reflect the consistency and product quality of the polymerization process. Therefore, the Bayesian discriminant method based on the pattern index has higher accuracy in comparison with the process parameters, and it is more effective on quality monitoring for polymerization process.

Key words: polymerization process, pattern monitoring, quality control, Bayesian, principal component analysis, experimental validation

中图分类号:

TQ325.3

高爽, 郑年年, 栾小丽, 刘飞. 聚合过程贝叶斯统计学习质量模式监测[J]. 化工进展, 2018, 37(12): 4558-4564.

GAO Shuang, ZHENG Niannian, LUAN Xiaoli, LIU Fei. Quality pattern monitoring for polymerization process based on Bayesian statistical learning[J]. Chemical Industry and Engineering Progress, 2018, 37(12): 4558-4564.

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聚合过程贝叶斯统计学习质量模式监测

Quality pattern monitoring for polymerization process based on Bayesian statistical learning

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