Improved Mixed Integer Optimization Approach for Data Rectification with Gross Error Candidates  

作　　者：李笕列 荣冈 

机构地区：[1]State Key Laboratory of Industrial Control Technology, Institute of Cyber System and Control, Zhejiang University Hangzhou 310027, China

出　　处：《Chinese Journal of Chemical Engineering》2009年第2期226-231,共6页中国化学工程学报（英文版）

基　　金：Supported by the National High Technology Research and Development Program of China (2007AA40702 and 2007AA04Z191)

摘　　要：Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.Mixed integer linear programming(MILP) approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material,energy,and other balance constrains.But the efficiency will decrease significantly when this method is applied in a large-scale problem because there are too many binary variables involved.In this article,an improved method is proposed in order to generate gross error candidates with reliability factors before data rectification.Candidates are used in the MILP objective function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates.Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.

关 键 词：data rectification gross error detection graphic theory Bayesian method 

分 类 号：TP302.7[自动化与计算机技术—计算机系统结构] O224[自动化与计算机技术—计算机科学与技术]