| 基于混合分块DMICA-PCA的全流程过程监控方法 |
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| 引用本文： | 江伟,王振雷,王昕. 基于混合分块DMICA-PCA的全流程过程监控方法[J]. 化工学报, 2017, 68(2): 759-766. DOI: 10.11949/j.issn.0438-1157.20161309 |
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| 作者姓名： | 江伟 王振雷 王昕 |
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| 作者单位： | 1. 化学工程联合国家重点实验室, 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 |
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| 基金项目： | 国家自然科学基金重点项目（61134007）；国家自然科学基金青年项目（61403141）；上海市“科技创新行动计划”研发平台建设项目（13DZ2295300）；上海市自然科学基金项目（14ZR1421800）；流程工业综合自动化国家重点实验室开放课题基金资助项目（PAL-N201404）。 |
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| 摘 要： | 分块策略被广泛运用于全流程过程监控领域，以解决全流程过程变量关系复杂性较高的问题，但传统的分块策略与子块建模方法都未考虑过程的动态性问题，并且传统的分块策略都片面依赖于过程知识或过程数据信息，影响了过程监控的效果，为此提出了一种基于混合分块DMICA-PCA的过程监控方法。在分析过程的动态性后，先利用已知的部分过程知识进行变量的初步分块，接着利用各分块变量之间改进的广义Dice's系数（MGDC）进行进一步的分块。然后采用DMICA-PCA方法对每个子块进行建模得到子块的统计量，并通过加权方法得到总的联合指标进行故障检测。同时对每个子块采用改进的故障诊断方法，提高了诊断效果。最后将该方法应用在TE过程的过程监控中，证明了该方法的有效性。
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| 关 键 词： | 主元分析 过程控制 过程系统 混合分块 全流程 改进的广义Dice's系数 |
| 收稿时间： | 2016-09-19 |
| 修稿时间： | 2016-12-05 |
Plant-wide process monitoring based on mixed multiblock DMICA-PCA |
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| JIANG Wei,WANG Zhenlei,WANG Xin. Plant-wide process monitoring based on mixed multiblock DMICA-PCA[J]. Journal of Chemical Industry and Engineering(China), 2017, 68(2): 759-766. DOI: 10.11949/j.issn.0438-1157.20161309 |
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| Authors: | JIANG Wei WANG Zhenlei WANG Xin |
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| Affiliation: | 1. State Key Laboratory of Chemical Engineering, Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237, China;2. Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China |
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| Abstract: | Multiblock strategy is widely used in plant-wide process monitoring to solve problems with complicated relationships between process variables. Traditional multiblock strategies and sub-block modeling methods are not effective in plant-wide process monitoring, because dynamic characteristics of the process have not been considered and knowledge or data information of the process is exclusively exploited. A mixed multiblock DMICA-PCA method was proposed to improve process monitoring performance. First, variables were sliced into initial sub-blocks by obtained process knowledge after analysis of process dynamics and further sliced into final sub-blocks by modified general Dice's coefficient (MGDC) between variables of initial sub-blocks. Then, the DMICA-PCA method was used to establish model and acquire statistical values of variables in final sub-blocks and a combined overall index from weighted sum was developed for fault detection, which improved performances by simultaneous diagnosis on each sub-block. Effectiveness of the proposed method was validated on monitoring the Tennessee-Eastman (TE) process. |
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| Keywords: | principal component analysis process control process systems mixed multiblock plant-wide process modified general Dice's coefficient |
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