[1] Lu Chunhong, Gu Xiaofeng,. Quality prediction of batch process using the global-localdiscriminant analysis based Gaussian process regression model [J]. Journal of Southeast University (English Edition), 2015, 31 (1): 80-86. [doi:10.3969/j.issn.1003-7985.2015.01.014]

Copy

Quality prediction of batch process using the global-localdiscriminant analysis based Gaussian process regression model()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

31

Issue:

2015 1

Page:

80-86

Research Field:

Automation

Publishing date:

2015-03-30

Info

Title:

Quality prediction of batch process using the global-localdiscriminant analysis based Gaussian process regression model

Author(s):

Lu Chunhong;  Gu Xiaofeng

Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China

Keywords:

quality prediction;  global-local discriminant analysis;  Gaussian process regression;  hidden Markov model;  soft sensor

PACS:

TP273

DOI:

10.3969/j.issn.1003-7985.2015.01.014

Abstract:

The conventional single model strategy may be ill-suited due to the multiplicity of operation phases and system uncertainty. A novel global-local discriminant analysis(GLDA)based Gaussian process regression(GPR)approach is developed for the quality prediction of nonlinear and multiphase batch processes. After the collected data is preprocessed through batchwise unfolding, the hidden Markov model(HMM)is applied to identify different operation phases. A GLDA algorithm is also presented to extract the appropriate process variables highly correlated with the quality variables, decreasing the complexity of modeling. Besides, the multiple local GPR models are built in the reduced-dimensional space for all the identified operation phases. Furthermore, the HMM-based state estimation is used to classify each measurement sample of a test batch into a corresponding phase with the maximal likelihood estimation. Therefore, the local GPR model with respect to specific phase is selected for online prediction. The effectiveness of the proposed prediction approach is demonstrated through the multiphase penicillin fermentation process. The comparison results show that the proposed GLDA-GPR approach is superior to the regular GPR model and the GPR based on HMM(HMM-GPR)model.

References:

[1] Kadlec P, Gabrys B, Strandt S. Data driven soft sensor in the process industry [J]. Computers and Chemical Engineering, 2009, 33(4): 795-814.
[2] Lin B, Recke J, Knudsen K H, et al. A systematic approach for soft sensor development [J]. Computers and Chemical Engineering, 2007, 31(5): 419-425.
[3] Sliškovic D, Grbic R, Nyarko E K. Data preprocessing in data based process modeling [C]//Proc of the IFAC International Conference on Intelligent Control Systems and Signal Processing. Istanbul, Turkey, 2009: 559-565.
[4] Chen J H, Hsu T-Y, Chen C-C, et al. Online predictive monitoring using dynamic imaging of furnaces with the combinational method of multiway principal component analysis and hidden Markov model [J]. Industrial & Engineering Chemistry Research, 2011, 50(5): 2946-2958.
[5] Viterbi A J. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm [J]. IEEE Transactions on Information Theory, 1976, 13(2): 260-269.
[6] Grbic R, Sliškovic D, Kadlec P. Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models [J]. Computers and Chemical Engineering, 2013, 58:84-97.
[7] Rasmussen C E, Williams C K I. Gaussian processes in machine learning [M]. Cambridge, MA, USA: The MIT Press, 2006.
[8] Kwak N. Kernel discriminant analysis for regression problems [J]. Pattern Recognition, 2012, 45(5): 2019-2031.
[9] Kwak N, Lee J W. Feature extraction based on subspace methods for regression problems [J]. Neurocomputers, 2010, 73(10/11/12):1740-1751.
[10] Birol G, Undey C, Cinar A. A modular simulation package for fed-batch fermentation: penicillin production [J]. Computers and Chemical Engineering, 2002, 26(11): 1553-1565.
[11] Alford J S. Bioprocess control: advances and challenges [J]. Computers and Chemical Engineering, 2006, 30(10/11/12):1464-1475.

Memo

Memo:

Biographies: Lu Chunhong(1982—), female, graduate; Gu Xiaofeng(corresponding author), male, doctor, professor, xgu@jiangnan.edu.cn.
Foundation items: The Fundamental Research Funds for the Central Universities(No.JUDCF12027, JUSRP51323B), the Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXLX12_0734).
Citation: Lu Chunhong, Gu Xiaofeng. Quality prediction of batch process using the global-local discriminant analysis based Gaussian process regression model[J].Journal of Southeast University(English Edition), 2015, 31(1):80-86.[doi:10.3969/j.issn.1003-7985.2015.01.014]

Common functions