Application of Large-Scale Database-Based Online Modeling to Plant State Long-Term Estimation

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  • Ogawa Masatoshi
    Waseda University, Information Production Systems Research Center
  • Ogai Harutoshi
    Graduate school of Information Production Systems, Waseda University

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  • 大規模データに基づく局所モデリングのプラント状態長期予測への応用
  • ダイキボ データ ニ モトズク キョクショ モデリング ノ プラント ジョウタイ チョウキ ヨソク エ ノ オウヨウ

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Recently, attention has been drawn to the local modeling techniques of a new idea called “Just-In-Time (JIT) modeling”. To apply “JIT modeling” to a large amount of database online, “Large-scale database-based Online Modeling (LOM)” has been proposed. LOM is a technique that makes the retrieval of neighboring data more efficient by using both “stepwise selection” and quantization. In order to predict the long-term state of the plant without using future data of manipulated variables, an Extended Sequential Prediction method of LOM (ESP-LOM) has been proposed. In this paper, the LOM and the ESP-LOM are introduced.

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