Please use this identifier to cite or link to this item:
|Title:||Data selection and regression method and its application to softsensing using multirate industrial data|
Partial least squares
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 23, 2019
WEB OF SCIENCETM
checked on Mar 13, 2019
checked on Sep 29, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.