Data Selection and Regression Method and Its Application to Softsensing Using Multirate Industrial Data

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概要

• 論文の詳細を見る

• 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.

• 社団法人 化学工学会の論文
• 2008-05-01

著者

• Lakshminarayanan Samavedham

  Department Of Chemical And Biomolecular Engineering National University Of Singapore

• TUN May

  Department of Chemical and Biomolecular Engineering, National University of Singapore

• EMOTO Genichi

  Science and Technology Research Center, Mitsubishi Chemical Corporation

• Tun May

  Department Of Chemical And Biomolecular Engineering National University Of Singapore

• Emoto Genichi

  Science And Technology Research Center Mitsubishi Chemical Corporation

関連論文

• Data Selection and Regression Method and Its Application to Softsensing Using Multirate Industrial Data

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