Quality Prediction Based on Sub-Stage LS-SVM for Batch Processes

摘要：

For multistage, nonlinear characteristic of batch process, a sub-stage least square support vector machines (LS-SVM) method is proposed for quality prediction. Firstly, using an clustering arithmetic, PCA P-loading matrices of time-slice matrices is clustered according to relevance and batch process is divided into several operation stages, the most relevant stage to the quality variable is defined, and then applying correlation analysis in un-fold stage data in order to get irrelevant input variables, and sub stage LS-SVM models are developed in every stage for quality prediction. The proposed method easily handles the following problems: (1) static single model; (2) process and its model do not match; (3) Linear method may not be efficient in compressing and extracting nonlinear process data. For comparison purposes a sub-MPLS quality model was establish. The results have demonstrated the effectiveness of the proposed method.

关键词： batch process sub-stage quality prediction least square- support vector machines (LS-SVM)

作者: Guo Xiaoping Zhao wendan Li yuan

作者单位: Information Engineering School, Shenyang Institute of Chemical Technology, 110142, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 5858-5862

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）