| Project/Area Number |
24760629
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Reaction engineering/Process system
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| Research Institution | The University of Tokyo |
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Principal Investigator |
KANEKO Hiromasa 東京大学, 工学(系)研究科(研究院), 助教 (00625171)
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2014: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | プロセス管理 / ソフトセンサー / モデルの劣化 / 適応型モデル / サポートベクター回帰 / アンサンブル学習 / ベイズの定理 / 予測誤差 / 時間差分 / SVR / 時間変数 |
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| Outline of Final Research Achievements |
A soft sensor predicts the values of some process variable y that is difficult to measure. To maintain the predictive ability of a soft sensor model, adaptation mechanisms are applied to soft sensors. However, even these adaptive soft sensors cannot predict the y-values of various process states in chemical plants, and it is difficult to ensure the predictive ability of such models on a long-term basis. Therefore, we propose a method that combines online support vector regression (OSVR) with an ensemble learning system to adapt to nonlinear and time-varying changes in process characteristics and various process states in a plant. Several OSVR models, each of which has an adaptation mechanism and is updated with new data, predict y-values. A final predicted y-value is calculated based on those predicted y-values and Bayes’ rule. We analyzed simulation datasets and real industrial datasets, and demonstrate the superiority of the proposed method.
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