2014
DOI: 10.1021/ie4041252
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Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes

Abstract: The principal component regression (PCR) based soft sensor modeling technique has been widely used for process quality prediction in the last decades. While most industrial processes are characterized with nonlinearity and time variance, the global linear PCR model is no longer applicable. Thus, its nonlinear and adaptive forms should be adopted. In this paper, a just-in-time learning (JITL) based locally weighted kernel principal component regression (LWKPCR) is proposed to solve the nonlinear and time-varian… Show more

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Cited by 135 publications
(71 citation statements)
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References 37 publications
(53 reference statements)
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“…The Tennessee Eastman (TE) process was proposed by Downs and Vogel based on a realistic standard chemical industrial process, and has been extensively applied to test and compare the performance of various monitoring approaches and control schemes . The TE model includes five major unit operations: a reactor, a condenser, a flash separator, a stripper, and a recycle compressor.…”
Section: Case Studiesmentioning
confidence: 99%
“…The Tennessee Eastman (TE) process was proposed by Downs and Vogel based on a realistic standard chemical industrial process, and has been extensively applied to test and compare the performance of various monitoring approaches and control schemes . The TE model includes five major unit operations: a reactor, a condenser, a flash separator, a stripper, and a recycle compressor.…”
Section: Case Studiesmentioning
confidence: 99%
“…16,17 In contrast to the conventional adaptive modeling method where the initial model is constructed offline through a global manner, the JITL model is built online when the query sample is coming. Such modeling approach can cope with nonlinearity as well as changes of process characteristics 5 successfully.…”
Section: Just-in-time-learning (Jitl)mentioning
confidence: 99%
“…The main purpose of soft sensors is to predict the hard-to-measure key process variables through those easy-to-measure ones, which can provide important information for process control and monitoring [4][5][6][7]. A lot of soft sensor modeling methods were developed in the past years [8][9][10][11][12]. Generally, they can be categorized into two different kinds: model-driven and data-driven soft sensors.…”
Section: Introductionmentioning
confidence: 99%