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Indexed by:期刊论文

Date of Publication:2011-10-13

Journal:COMPUTERS & CHEMICAL ENGINEERING

Included Journals:Scopus、SCIE、EI

Volume:35

Issue:10

Page Number:2141-2151

ISSN No.:0098-1354

Key Words:Data reconciliation; Support vector regression; Parameter estimation; Gross error detection

Abstract:Process data measurements are important for process monitoring, control, optimization, and management decision making. However, process data may be heavily deteriorated by measurement biases and process leaks. Therefore, it is significant to simultaneously estimate biases and leaks with data reconciliation. In this paper, a novel strategy based on support vector regression (SVR) is proposed to achieve simultaneous data reconciliation and joint bias and leak estimation in steady processes. Although the linear objective function of the SVR approach proposed is robust with little computational burden, it would not result in the maximum likelihood estimate. Therefore, to ensure accurate estimates, the maximum likelihood estimate is applied based on the result of the SVR approach. Simulation and comparison results of a linear recycle system and a nonlinear heat-exchange network demonstrate that the proposed strategy is effective to achieve data reconciliation and joint bias and leak estimation with superior performances. (C) 2011 Elsevier Ltd. All rights reserved.