Developing a soft sensor with online variable reselection for unobserved multi-mode operations |
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| Affiliation: | 1. School of Food Science & Biotechnology, Kyungpook National University, Daegu 41566, Republic of Korea;2. Department of Food, Nutrition and Cook, Taegu Science University, Daegu 41453, Republic of Korea;3. Food Technology Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400 085, India;4. Institute of Food Science and Nutrition, University of Sargodha, Sargodha 40100, Pakistan;5. Advanced Radiation Technology Institute, Korea Atomic & Energy Research Institute, Jeungeup 56212, Republic of Korea;1. State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing), Beijing 102249, China;2. Sinopec Yangzi Petrochemical Company LTD, Nanjing 210048, China;1. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea;2. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA;3. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge 02139, MA, USA;4. Department of Chemical and Biological Engineering, University of British Columbia, 2360 East Mall, Vancouver BC V6T1Z3, Canada;1. Departamento de Ingeniería Química, Universidad de Guadalajara, Calz. Gral. Marcelino García Barragán 1451, Guadalajara, Jalisco 44430, Mexico;2. Department of Chemical Engineering, Queen''s University, Kingston, ON, Canada K7L 3N6;3. ICTEAM, Université Catholique de Louvain, Bâtiment EULER 4-6, av. Georges Lemaître, B-1348 Louvain-la-Neuve, Belgium |
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| Abstract: | Soft sensors are used to predict response variables, as these variables are difficult to measure, the prediction models use data of predictors that are relatively easier to obtain. Arranging time-lagged data of predictors and applying the partial least squares (PLS) method to the dataset is a popular approach for extracting the correlation between data of the responses and predictors of the process dynamic. Because irrelevant inputs deteriorate the prediction performance of the soft sensor, the selection of variables in the PLS-based model is a critical step for developing a robust and accurate model. Furthermore, it is necessary to reselect the important predictors of a soft sensor when the operating mode is changed. However, a switch in the operating mode may not be measured, directly. In this study, two statistics are proposed to detect a change of operating mode to enable the reselection of the predictors of the soft sensor. This work involved the development of a soft sensor based on operating data from the industrial ethane removal (de-ethane) process. The changeover of crude oil types cannot be observed from the data of process variables; however, the correlation between input and output variables is significantly affected by the different types of crude oil. The result shows that the use of a soft sensor with online variable reselection is capable of maintaining the accuracy and robustness of the inferential model, effectively. |
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| Keywords: | Soft sensors Multi-mode operations Online variable reselection Partial least squares |
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