Monitoring and Quality Prediction of CPLS Batch Process Based on Kernel Entropy Projection

被引：0

作者：

Zhao X.-Q. ^{[1
,2
,3
]}

Zhou W.-W. ^{[1
,2
]}

Hui Y.-Y. ^{[1
,2
]}

机构：

[1] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou

[2] Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou

[3] National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou

来源：

Gao Xiao Hua Xue Gong Cheng Xue Bao/Journal of Chemical Engineering of Chinese Universities | 2018年 / 32卷 / 05期

关键词：

Batch process; GMM; Multimode characteristic; Projection to latent structures; Quality prediction;

D O I：

10.3969/j.issn.1003-9015.2018.05.026

中图分类号：

学科分类号：

摘要：

A "multi-way Gaussian mixture model-concurrent kernel entropy projection to latent structures" (MGMM-CKEPLS) algorithm was proposed to solve problems of nonlinearity, multimode and quality prediction of batch process. Low-dimension nonlinear data was first projected into high-dimensional kernel feature space by kernel entropy projection. Principal components were obtained by the size of Renyi entropy contribution, which can reduce principal component numbers and overcome the problem of computational complexity of traditional kernel methods. Data of each mode was then obtained by GMM, and CPLS models were established for different modes, which was more consistent with actual batch processes by considering the difference between each process. Finally, unified monitoring statistics were integrated to achieve online monitoring and quality prediction by modal weight coefficients. The model was verified in penicillin fermentation process and the results show that the proposed algorithm has better online monitoring effectiveness and higher accuracy in quality prediction than MKPLS algorithm. © 2018, Editorial Board of "Journal of Chemical Engineering of Chinese Universities". All right reserved.

引用

页码：1186 / 1193

页数：7

共 19 条

[1] Zhao X.-Q., Hui Y.-Y., Batch process monitoring based on TTGNPE algorithm, Control and Decision, 32, 3, pp. 557-562, (2017)
[2] Chang P., Wang P., Gao X.-J., Batch process monitoring for microbial fermentation based on multi-way kernel entropy component analysis, Journal of Chemical Engineering of Chinese Universities, 29, 2, pp. 395-399, (2015)
[3] Peng K.-X., Ma L., Zhang K., Review of quality-related fault detection and diagnosis techniques for complex industrial processes, Acta Automatica Sinica, 43, 3, pp. 349-365, (2017)
[4] Ge Z.Q., Song Z.H., Gao F.R., Review of recent research on data-based process monitoring, Industrial & Engineering Chemical Research, 52, 10, pp. 3543-3562, (2013)
[5] Deng X.G., Tian X.M., Multimode process fault detection using local neighborhood similarity analysis, Chinese Journal of Chemical Engineering, 22, 11, pp. 1260-1267, (2014)
[6] Zhao C.H., Phase analysis and statistical modeling with limited batches for multimode and multiphase process monitoring, Journal of Process Control, 24, 6, pp. 856-870, (2014)
[7] Yin S., Ding S.X., Haghani A., Et al., A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process, Journal of Process Control, 22, 9, pp. 1567-1581, (2012)
[8] Yu J., Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes, Industrial & Engineering Chemistry Research, 51, 40, pp. 13227-13237, (2012)
[9] Luo L., Bao S., Mao J., Et al., Quality prediction and quality-relevant monitoring with multilinear PLS for batch processes, Chemometrics & Intelligent Laboratory Systems, 150, pp. 9-22, (2016)
[10] Wang X.C., Wang P., Gao X.J., Et al., On-line quality prediction of batch processes using a new kernel multiway partial least squares method, Chemometrics & Intelligent Laboratory Systems, 158, pp. 138-145, (2016)

← 1 2 →