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

›› 2009, Vol. 17 ›› Issue (2): 226-231.

• PROCESS SYSTEMS ENGINEERING • Previous Articles Next Articles

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

LI Jianlie, RONG Gang

State Key Laboratory of Industrial Control Technology, Institute of Cyber System and Control, Zhejiang University, Hangzhou 310027, China

Received:2008-05-28 Revised:2008-11-06 Online:2009-04-28 Published:2009-04-28
Supported by:
Supported by the National High Technology Research and Development Program of China (2007AA40702 and 2007AA04Z191)

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

李笕列, 荣冈

State Key Laboratory of Industrial Control Technology, Institute of Cyber System and Control, Zhejiang University, Hangzhou 310027, China

通讯作者: RONG Gang,E-mail:grong@mail.hz.zj.cn
基金资助:
Supported by the National High Technology Research and Development Program of China (2007AA40702 and 2007AA04Z191)

Abstract

Abstract: 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.

Key words: data rectification, gross error detection, graphic theory, Bayesian method

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 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

LI Jianlie, RONG Gang. Improved Mixed Integer Optimization Approach for Data Rectification with Gross Error Candidates[J]. , 2009, 17(2): 226-231.

李笕列, 荣冈. Improved Mixed Integer Optimization Approach for Data Rectification with Gross Error Candidates[J]. , 2009, 17(2): 226-231.

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[1]	JIANG Chunyang, QIU Tong, ZHAO Jinsong, CHEN Bingzhen. Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems [J]. , 2009, 17(3): 460-467.
[2]	MEICongli,SUHongye, CHUJian. An NT-MT Combined Method for Gross Error Detection and Data Reconciliation [J]. , 2006, 14(5): 592-596.

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

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

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