A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry |
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Authors: | Shima Khatibisepehr Biao Huang Fangwei Xu Aris Espejo |
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Affiliation: | 1. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2G6;2. Syncrude Canada Ltd., Fort McMurray, Alberta, Canada T9H 3L1 |
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Abstract: | In the context of process industries, online monitoring of quality variables is often restricted by inadequacy of measurement techniques or low reliability of measuring devices. Therefore, there has been a growing interest in the development of inferential sensors to provide frequent online estimates of key process variables on the basis of their correlation with real-time process measurements. Representation of multi-modal processes is one of the challenging issues that may arise in the design of inferential sensors. In this paper, Bayesian procedures for the development and implementation of adaptive multi-model inferential sensors are presented. It is shown that the application of a Bayesian scheme allows for accommodating the overlapping operating modes and facilitating the inclusion of prior knowledge. The effectiveness of the proposed procedures are first demonstrated through a simulation case study. The efficacy of the method is further highlighted by a successful industrial application of an adaptive multi-model inferential sensor designed for real-time monitoring of a key quality variable in an oil sands processing unit. |
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