文献类型：期刊文献

中文题名：一种基于聚类方法的多阶段间歇过程监控方法

英文题名：Multi-stage batch process monitoring based on a clustering method

作者：张子羿[1];胡益[1];侍洪波[1]

机构：[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

年份：2013

卷号：64

期号：12

起止页码：4522

中文期刊名：化工学报

外文期刊名：CIESC Journal

收录：CSTPCD;;EI(收录号：20140717330930);Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2013_2014】；

基金：上海市重点学科建设项目(B504);国家自然科学基金项目(61374140)~~

语种：中文

中文关键词：间歇过程;过程控制;故障诊断;阶段不等长;k均值聚类;主元分析;间歇式

外文关键词：batch processes; process control; fault diagnosis; uneven-length stage; k-means clustering;principal component analysis; batchwise

摘要：针对阶段不等长的多阶段间歇过程,提出了一种基于k-均值聚类方法的阶段分段策略,可以将不等长的阶段准确分类。首先,将间歇过程的三维训练数据按变量方向展开成二维矩阵,再通过k-均值聚类的方法按照相关性将数据聚成多类并运用主元分析(PCA)方法分别对每一类建立模型。在线监控时,通过计算样本与模型之间的相似系数以选择最合适的模型进行在线监控。此方法可以将不同批次在同一采样时刻的过程数据按照相关性分到多个阶段,更符合生产过程中常见的过程数据阶段不等长的情况。最后利用青霉素仿真验证了该方法的有效性。
Batch process plays an important role in the processing of specialty chemical, semi-conductor, food and biology industries for producing high-value-added products to meet rapidly changing market, which causes their monitoring and control to emerge as essential techniques. A novel method for uneven- length-stage batch process was proposed by using a new stage segmentation strategy based on k means clustering method. First, the three-dimension data array is unfolded in variable ways, resulting in two dimension forms. Then according to data correlations, two-dimension training data set is divided into many clusters by applying the conventional k-means clustering method, and sequentially principal component analysis （PCA） is performed for each separated cluster. For online monitoring, a similarity index is introduced to choose the most suitable local model. The effectiveness and utility of the proposed method was validated through the simulation benchmark of fed-batch penicillin production.