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

李笕列; 荣冈

doi:10.1016/S1004-9541(08)60198-6

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

Updated：2026-01-06

- Improved Mixed Integer Optimization Approach for Data Rectification with Gross Error Candidates
- Improved Mixed Integer Optimization Approach for Data Rectification with Gross Error Candidates
- 中国化学工程学报（英文版） 2009年17卷第2期页码：226-231
- Affiliations：
  
  State Key Laboratory of Industrial Control Technology, Institute of Cyber System and Control, Zhejiang University,Hangzhou,China,310027
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(08)60198-6
  中图分类号：
- 纸质出版：2009
- Accepted：
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李笕列, 荣冈. Improved Mixed Integer Optimization Approach for Data Rectification with Gross Error Candidates[J]. 中国化学工程学报（英文版）, 2009,17(2):226-231.

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]. 中国化学工程学报（英文版）, 2009,17(2):226-231. DOI： 10.1016/S1004-9541(08)60198-6.

LI Jianlie, RONG Gang. Improved Mixed Integer Optimization Approach for Data Rectification with Gross Error Candidates[J]. , 2009, 17(2): 226-231. DOI： 10.1016/S1004-9541(08)60198-6.

摘要

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.

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