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Monitoring Chemical Processes Using Judicious Fusion of Multi-Rate Sensor Data

Continuous Improvement Center of Excellence, The Dow Chemical Company, Lake Jackson, TX 77566, USA
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Sensors 2019, 19(10), 2240; https://doi.org/10.3390/s19102240
Received: 8 April 2019 / Revised: 10 May 2019 / Accepted: 11 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Soft Sensors: Inference and Estimation)
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Abstract

With the emergence of Industry 4.0, also known as the fourth industrial revolution, an increasing number of hardware and software sensors have been implemented in chemical production processes for monitoring key variables related to product quality and process safety. The accuracy of individual sensors can be easily impaired by a variety of factors. To improve process monitoring accuracy and reliability, a sensor fusion scheme based on Bayesian inference is proposed. The proposed method is capable of combining multi-rate sensor data and eliminating the spurious signals. The efficacy of the method has been verified using a process implemented at the Dow Chemical Company. The sensor fusion approach has improved the process monitoring reliability, quantified by the rates of correctly identified impurity alarms, as compared to the case of using an individual sensor. View Full-Text
Keywords: process monitoring; sensor fusion; Bayesian approach; multi-rate measurements; spurious data; Industry 4.0 process monitoring; sensor fusion; Bayesian approach; multi-rate measurements; spurious data; Industry 4.0
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, Z.; Chiang, L. Monitoring Chemical Processes Using Judicious Fusion of Multi-Rate Sensor Data. Sensors 2019, 19, 2240.

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