Adaptive weighted relevant sample selection of just-in-time learning soft sensor for chemical processes

A new just-in-time learning (JITL) method using adaptive relevant sample selection strategy is proposed for online prediction of product quality in chemical processes. To overcome certain shortcomings in traditional JITL, such as the incomplete usage of primary variable information and inaccurate feature weights assignment, an adaptive sample selection approach is introduced. First, to keep both input and output information, a dual-objective optimization scheme with an adaptive parameter is considered. Then, an adaptive feature weight assignment strategy is present to construct a suitable similarity criterion for JITL. To illustrate the performance of the proposed method, it is applied to online prediction of the crude oil endpoint in an industrial fluidized catalytic cracking unit. The experimental results demonstrate that the proposed method can help improve the prediction performance.

[1]  S. Qin.  Recursive PLS algorithms for adaptive data modeling , 1998 .

[2]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[3]  Uwe Kruger,et al.  Recursive partial least squares algorithms for monitoring complex industrial processes , 2003 .

[4]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[5]  M. Chiu,et al.  A new data-based methodology for nonlinear process modeling , 2004 .

[6]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[7]  Klaus-Robert Müller,et al.  Incremental Support Vector Learning: Analysis, Implementation and Applications , 2006, J. Mach. Learn. Res..

[8]  Hanqing Lu,et al.  Face recognition using kernel scatter-difference-based discriminant analysis , 2006, IEEE Trans. Neural Networks.

[9]  Ping Li,et al.  Kernel classifier with adaptive structure and fixed memory for process diagnosis , 2006 .

[10]  Li Ping.  Selective Recursive LSSVR and its Applications in Process Modeling , 2008 .

[11]  Jeng-Shyang Pan,et al.  Kernel class-wise locality preserving projection , 2008, Inf. Sci..

[12]  Xi Zhang,et al.  Nonlinear Multivariate Quality Estimation and Prediction Based on Kernel Partial Least Squares , 2008 .

[13]  Mukta Paliwal,et al.  Neural networks and statistical techniques: A review of applications , 2009, Expert Syst. Appl..

[14]  Haiqing Wang,et al.  Soft Chemical Analyzer Development Using Adaptive Least-Squares Support Vector Regression with Selective Pruning and Variable Moving Window Size , 2009 .

[15]  U. Kruger,et al.  Moving window kernel PCA for adaptive monitoring of nonlinear processes , 2009 .

[16]  Luiz Augusto da Cruz Meleiro,et al.  ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process , 2009, Comput. Chem. Eng..

[17]  Manabu Kano,et al.  Soft‐sensor development using correlation‐based just‐in‐time modeling , 2009 .

[18]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[19]  Morimasa Ogawa,et al.  The state of the art in chemical process control in Japan: Good practice and questionnaire survey , 2010 .

[20]  Zhi-huan Song,et al.  Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes , 2011 .

[21]  Furong Gao,et al.  Bayesian migration of Gaussian process regression for rapid process modeling and optimization , 2011 .

[22]  Zhi-huan Song,et al.  Global–Local Structure Analysis Model and Its Application for Fault Detection and Identification , 2011 .