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Article

A Modified Expectation Maximization Approach for Process Data Rectification

1
State Key Laboratory of Safety and Control for Chemicals, SINOPEC Qingdao Research Institute of Safety Engineering, Qingdao 266071, China
2
College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
*
Author to whom correspondence should be addressed.
Processes 2021, 9(2), 270; https://doi.org/10.3390/pr9020270
Received: 26 November 2020 / Revised: 4 January 2021 / Accepted: 5 January 2021 / Published: 30 January 2021
(This article belongs to the Special Issue Learning for Process Optimization and Control)
Process measurements are contaminated by random and/or gross measuring errors, which degenerates performances of data-based strategies for enhancing process performances, such as online optimization and advanced control. Many approaches have been proposed to reduce the influence of measuring errors, among which expectation maximization (EM) is a novel and parameter-free one proposed recently. In this study, we studied the EM approach in detail and argued that the original EM approach is not feasible to rectify measurements contaminated by persistent biases, which is a pitfall of the original EM approach. So, we propose a modified EM approach here to circumvent this pitfall by fixing the standard deviation of random error mode. The modified EM approach was evaluated by several benchmark cases of process data rectification from literatures. The results show advantages of the proposed approach to the original EM in solving efficiency and performance of data rectification. View Full-Text
Keywords: data rectification; expectation maximization; bias detection data rectification; expectation maximization; bias detection
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MDPI and ACS Style

Jiang, W.; Li, R.; Cao, D.; Li, C.; Tao, S. A Modified Expectation Maximization Approach for Process Data Rectification. Processes 2021, 9, 270. https://doi.org/10.3390/pr9020270

AMA Style

Jiang W, Li R, Cao D, Li C, Tao S. A Modified Expectation Maximization Approach for Process Data Rectification. Processes. 2021; 9(2):270. https://doi.org/10.3390/pr9020270

Chicago/Turabian Style

Jiang, Weiwei, Rongqiang Li, Deshun Cao, Chuankun Li, and Shaohui Tao. 2021. "A Modified Expectation Maximization Approach for Process Data Rectification" Processes 9, no. 2: 270. https://doi.org/10.3390/pr9020270

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