Data Selection and Regression Method and Its Application to Softsensing Using Multirate Industrial Data
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- Tun May Su
- Department of Chemical and Biomolecular Engineering, National University of Singapore
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- Lakshminarayanan Samavedham
- Department of Chemical and Biomolecular Engineering, National University of Singapore
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- Emoto Genichi
- Science and Technology Research Center, Mitsubishi Chemical Corporation
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抄録
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.
収録刊行物
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- JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 41 (5), 374-383, 2008
公益社団法人 化学工学会
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詳細情報 詳細情報について
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- CRID
- 1390001204567519872
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- NII論文ID
- 10021110269
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- NII書誌ID
- AA00709658
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- ISSN
- 18811299
- 00219592
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- NDL書誌ID
- 9498288
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可