Please use this identifier to cite or link to this item: https://doi.org/10.1252/jcej.06WE047
Title: Data selection and regression method and its application to softsensing using multirate industrial data
Authors: Tun, M.S.
Lakshminarayanan, S. 
Emoto, G.
Keywords: Model adaptation
Multiple models
Multirate identification
Partial least squares
Soft sensors
Issue Date: 2008
Source: Tun, M.S., Lakshminarayanan, S., Emoto, G. (2008). Data selection and regression method and its application to softsensing using multirate industrial data. Journal of Chemical Engineering of Japan 41 (5) : 374-383. ScholarBank@NUS Repository. https://doi.org/10.1252/jcej.06WE047
Abstract: The estimation of difficult and infrequently measured variables (composition, melt flow index viscosity, etc.) using easily and frequently measured variables (temperatures, flow rates, pressure, etc.) is of industrial interest. From such multirate data (data available at different sampling rates), a mathematical model that relates the frequently measured variables to the infrequently measured variable is developed - this model is often referred to as the soft sensor. This work considers the development of soft sensors to predict the concentration of a hydrocarbon species R at the exit of a two-reactor train. Specifically, we examine the development of soft sensors (one for each reactor) using optimal window size and demonstrate the efficacy of multiple model based prediction. Copyright © 2008 The Society of Chemical Engineers, Japan.
Source Title: Journal of Chemical Engineering of Japan
URI: http://scholarbank.nus.edu.sg/handle/10635/63685
ISSN: 00219592
DOI: 10.1252/jcej.06WE047
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