| 带显著误差集的改进MILP数据协调方法 |
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| 引用本文： | 李笕列,荣冈. 带显著误差集的改进MILP数据协调方法[J]. 中国化学工程学报, 2009, 17(2): 226-231. DOI: 10.1016/S1004-9541(08)60198-6 |
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| 作者姓名： | 李笕列 荣冈 |
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| 作者单位： | State Key Laboratory of Industrial Control Technology, Institute of Cyber System and Control, Zhejiang University, Hangzhou 310027, China |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 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.
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| 关 键 词： | data rectification gross error detection graphic theory Bayesian method |
| 收稿时间： | 2008-05-28 |
| 修稿时间： | 2008-5-28 |
Improved mixed integer optimization approach for data rectification with gross error candidates |
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| LI Jianlie,RONG Gang. Improved mixed integer optimization approach for data rectification with gross error candidates[J]. Chinese Journal of Chemical Engineering, 2009, 17(2): 226-231. DOI: 10.1016/S1004-9541(08)60198-6 |
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| Authors: | LI Jianlie RONG Gang |
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| Affiliation: | State Key Laboratory of Industrial Control Technology, Institute of Cyber System and Control, Zhejiang University, Hangzhou 310027, China |
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| 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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| Keywords: | data rectification gross error detection graphic theory Bayesian method |
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