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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Data Reconciliation Based on an Improved Robust Estimator and NT-MT for Gross Error Detection
verfasst von : Shengxi Wu, Jinmeng Xu, Wei Liu, Xiaoying Wu, Xingsheng Gu
Erschienen in: Advanced Computational Methods in Life System Modeling and Simulation
Verlag: Springer Singapore
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Abstract
The quality of measurement data can be improved by data reconciliation. More accurate data will be provided for chemical process industry. However, the reconciliation results may be affected by gross errors. The influence of gross errors cannot be reduced effectively by classical method. Aimed at this problem, an improved robust NT-MT steady-state data reconciliation method is proposed in the paper. NT-MT method is used to detect suspicious nodes and variables with gross error. The suspicious variables are detected by critical value of adjustment detection. Robust estimator is used in data reconciliation. Finally, the measurement data is reconciled by the proposed robust estimator. The advantages of robust estimator and NT-MT method is combined together in this method. The simulation results show that the influence of gross error can be reduced effectively by the method proposed in the paper, thereby a better reconciliation results can be obtained.