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CLC number: TQ021.8

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Received: 2006-09-05

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.6 P.904~909

http://doi.org/10.1631/jzus.2007.A0904


Detection of gross errors using mixed integer optimization approach in process industry


Author(s):  MEI Cong-li, SU Hong-ye, CHU Jian

Affiliation(s):  National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   clmei@iipc.zju.edu.cn, hysu@iipc.zju.edu.cn

Key Words:  Data reconciliation, Detection of gross errors, Mixed integer linear programming (MILP), Novel MILP (NMILP) Quadratic programming (QP)


MEI Cong-li, SU Hong-ye, CHU Jian. Detection of gross errors using mixed integer optimization approach in process industry[J]. Journal of Zhejiang University Science A, 2007, 8(6): 904~909.

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author="MEI Cong-li, SU Hong-ye, CHU Jian",
journal="Journal of Zhejiang University Science A",
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pages="904~909",
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doi="10.1631/jzus.2007.A0904"
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%T Detection of gross errors using mixed integer optimization approach in process industry
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%A SU Hong-ye
%A CHU Jian
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0904

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T1 - Detection of gross errors using mixed integer optimization approach in process industry
A1 - MEI Cong-li
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A1 - CHU Jian
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SP - 904
EP - 909
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A0904


Abstract: 
A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information criterion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[9] Rollins, D., Davis, J., 1992. Unbiased estimation of gross errors in process measurements. Am. Inst. Chem. Eng. J., 38:563-572.

[10] Sanchez, M., Romagnoli, J., Jiang, Q., Bagajewicz, M., 1999. Simultaneous estimation of biases and leaks in process plants. Computers and Chem. Eng., 23:841-857.

[11] Soderstrom, T.A., Himmelblau, D.M., Edgar, T.F., 2001. A mixed integer optimization approach for simultaneous data reconciliation and identification of measurement bias. Control Eng. Practice, 9:869-876.

[12] Tong, H., Crowe, C.M., 1995. Detection of gross errors in data reconciliation by principal component analysis. Am. Inst. Chem. Eng. J., 41:1712-1722.

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