| 间歇过程的统计建模与在线监测 |
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| 引用本文： | 陆宁云, 王福利, 高福荣, 王姝. 间歇过程的统计建模与在线监测. 自动化学报, 2006, 32(3): 400-410. |
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| 作者姓名： | 陆宁云 王福利 高福荣 王姝 |
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| 作者单位： | 1.东北大学信息科学与工程学院131信箱,沈阳,110004;;;2.香港科学技术大学化学工程系,清水湾,九龙,香港 |
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| 基金项目： | 中国科学院资助项目;科技部科研项目 |
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| 摘 要： | 现代过程工业正逐渐倚重于生产小批量、多品种、高附加值产品的间歇过程.基于多元统计模型的过程监测是保障生产安全和产品质量的重要工具.从间歇过程独特的数据特性出发,将现有的多元统计建模方法进行合理的分类,简要回顾了各类方法的起源、发展及延伸的历程.除了阐述每种方法的基本原理,还详细讨论了各种方法的适用背景,相互关联及优缺点等内容,并对这一领域中依然存在的问题以及研究前景给出中肯的评述.
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| 关 键 词： | 间歇过程 多元统计模型 过程监测 主成分分析 偏最小二乘 三线性分解模型 |
| 收稿时间： | 2005-07-06 |
| 修稿时间： | 2005-11-02 |
Statistical Modeling and Online Monitoring for Batch Processes |
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| LU Ning-Yun, WANG Fu-Li, GAO Fu-Rong, WANG Shu. Statistical Modeling and Online Monitoring for Batch Processes. ACTA AUTOMATICA SINICA, 2006, 32(3): 400-410. |
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| Authors: | LU Ning-Yun WANG Fu-Li GAO Fu-Rong WANG Shu |
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| Affiliation: | 1. Box 131, the College of Information Science and Engineering, Northeastern University, Shenyang 10004Department of Chemical Engineering, Hong Kong University of Science &Technology, Clear Water Bay,Kowloon, Hong Kong |
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| Abstract: | The focus of modern process industry has shifted to the production of higher-value-added products through batch processes. Statistical process monitoring (SPM) has shown to be effective in ensuring process safety and product consistency for batch processes. This paper presents a review of multivariate statistical modeling and monitoring for batch process applications. Based on analysis of the nature of batch processes, this paper reviews each key method in terms of its motivation, development, and application prospective. The review ends with the authors' personal views of challenges and future directions for the area. |
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| Keywords: | Batch processes multivariate statistical model process monitoring principal component analysis (PCA) partial least squares (PLS) trilinear decomposition model |
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