基于多模型外部分析和Greedy-KP1M的多工况过程监控

doi:10.3969/j.issn.0438-1157.2012.09.031

化工学报 ›› 2012, Vol. 63 ›› Issue (9): 2869-2876.DOI: 10.3969/j.issn.0438-1157.2012.09.031

基于多模型外部分析和Greedy-KP1M的多工况过程监控

王晓阳¹, 王昕^2,3, 王振雷¹, 钱锋¹

1. 华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237;
2. 上海交通大学电工与电子技术中心, 上海 200240;
3. 东北大学流程工业综合自动化国家重点实验室, 辽宁沈阳 110004

收稿日期:2012-06-14 修回日期:2012-06-21 出版日期:2012-09-05 发布日期:2012-09-05
通讯作者: 王振雷
作者简介:王晓阳(1988-),男,硕士研究生。
基金资助:
国家自然科学基金项目(61134007,61174118);国家高技术研究发展计划项目(2012AA040307); 上海市基础研究重点项目(10JC1403500); 上海市重点学科建设项目(B504); 流程工业综合自动化国家重点实验室开放课题基金项目。

Multiple models external analysis and Greedy-KP1M based process monitoring with multiple operation modes

WANG Xiaoyang¹, WANG Xin^2,3, WANG Zhenlei¹, QIAN Feng¹

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2. Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China;
3. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, Liaoning, China

Received:2012-06-14 Revised:2012-06-21 Online:2012-09-05 Published:2012-09-05
Supported by:
supported by the National Natural Science Foundation of China(61134007,61174118), the High-tech Research and Development Program of China(2012AA040307),the Major State Basic Research Development Program of Shanghai(10JC1403500),the Shanghai Leading Academic Discipline Project(B504)and the State Key Laboratory of Synthetical Automation for Process Industries.

摘要/Abstract

摘要： 传统的基于多元统计过程监控方法都是假设过程处于单一工况下,而随着进料负荷、产品组分等过程参数的改变,生产过程的工况也随之改变,传统方法便不再适用。针对工业过程中的多工况监控问题,提出了一种基于多模型外部分析和Greedy-KP1M的多工况过程监控方法。首先针对传统外部分析方法描述能力不足的问题,用多模型局部建模代替单一模型来获得更好的描述能力,同时获得监控残差,通过对残差进行监控从而去除多工况的影响,进而将核单簇可能性聚类(KP1M)用于对残差的监控上。该方法拥有和支持向量数据描述(SVDD)相当的监控效果,但计算复杂度却远远小于SVDD。同时,采用Greedy方法提取特征样本,进一步降低了算法计算复杂度。最后将上述方法应用在TE模型和乙烯裂解炉的监控上,结果证明了该方法的有效性。

关键词: 多工况, 多模型外部分析, Greedy特征样本提取, 核单簇可能性聚类

Abstract: Multivariable statistical process control(MSPC)is developed in order to extract useful information from process data and utilize them for process monitoring.But when the change in process feed load or product composition happens,the conventional MSPC method does not function well for the process with multiple operation modes.In order to solve these problems,a novel process monitoring method is proposed based on multiple model external analysis and Greedy-KP1M.First,based on the traditional external analysis,multiple models modeling method is introduced to have a better performance. The multiple operation modes of process are eliminated by multiple models external analysis,and the residual error is got for monitoring.Then,to monitor the residual error,the method called Kernel Possibilistic one-Mean clustering(KP1M)is proposed.KP1M has a good ability to monitor nonlinear process.Its performance is similar with support vector data description(SVDD).But the computation complexity of KP1M is far less than SVDD’s.Moreover,to reduce the computation complexity furthermore,Greedy method is adopted to extract the feature samples for KP1M modeling.In the end,the proposed method is applied to monitor the TE(Tennessee Eastman)process and the ethylene cracking furnace to show its efficiency.

Key words: multiple operation modes, multiple models external analysis, Greedy feature samples extraction, KP1M

中图分类号:

TP277

王晓阳, 王昕, 王振雷, 钱锋. 基于多模型外部分析和Greedy-KP1M的多工况过程监控[J]. 化工学报, 2012, 63(9): 2869-2876.

WANG Xiaoyang, WANG Xin, WANG Zhenlei, QIAN Feng. Multiple models external analysis and Greedy-KP1M based process monitoring with multiple operation modes[J]. CIESC Journal, 2012, 63(9): 2869-2876.

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基于多模型外部分析和Greedy-KP1M的多工况过程监控

Multiple models external analysis and Greedy-KP1M based process monitoring with multiple operation modes

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