A novel robust data reconciliation method for industrial processes. (February 2019)

• Record Type: Journal Article
• Authors: Xie, Sen; Yang, Chunhua; Yuan, Xiaofeng; Wang, Xiaoli; Xie, Yongfang
• Is Part Of: Control engineering practice. Volume 83(2019)
• Page Start: 203
• Page End: 212
• Publication Date: 2019-02
• Subjects: Data reconciliation -- Robust estimation -- Industrial processes -- Gross error detection; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2018.11.006
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Data reconciliation has played a significant role in rectifying process data which can meet the conservation laws in industrial processes. Generally, the actual measurements are often easily contaminated by different gross errors. Thus, it is essential to build robust data reconciliation methods to alleviate the impact of gross errors and provide accurate data. In this paper, a novel robust estimator is proposed to improve the robustness of data reconciliation method, which is based on a new robust estimation function. First, the main robust properties are analyzed with its objective and influence functions for the proposed robust estimator. Then, the effectiveness of the new robust data reconciliation method is demonstrated on a linear numerical case and a nonlinear example. Moreover, it is further used to a practical industrial evaporation production process, which also demonstrates that the process data can be better reconciled with the proposed robust estimator.