Title :
Long-term prediction of industrial furnace by Extended Sequential Prediction method of LOM
Author :
Ogawa, Masatoshi ; Yeh, Yichun ; Kawanari, Syou ; Ogai, Harutoshi
Author_Institution :
Inf. Production Syst. Res. Center, Waseda Univ., Fukuoka, Japan
Abstract :
Recently, attention has been drawn by the local modeling techniques of a new idea called “Just-In-Time (JIT) modeling” or “Lazy Learning”. To apply “JIT modeling” to a large amount of database online, “Large-scale database-based Online Modeling (LOM)” has been proposed. LOM is such a technique that makes the retrieval of “neighboring” data more efficient by using “stepwise selection” and quantization. This paper reports an Extended Sequential Prediction (ESP) method of LOM with the local regression model. The ESP method is able to predict process variables over a long period by modeling the operator and the plant based on LOM, the approach is to repeat a process that predicts the process variables of the next step by using the predicted variables of the previous step. The method is applied to a dynamic industrial furnace with several deeply-intertwined physical phenomena; practical effectiveness of the method is verified. As a result, the method has predicted the process variables with satisfactory accuracy.
Keywords :
flow production systems; furnaces; information retrieval; just-in-time; learning (artificial intelligence); regression analysis; LOM; data retrieval; extended sequential prediction; industrial furnace; just in time; large scale database based online model; lazy learning; regression model; Accuracy; Data models; Databases; Furnaces; Input variables; Mathematical model; Predictive models; ESP; JIT modeling; LOM; industrial furnace; operation support; sequential prediction;
Conference_Titel :
SICE Annual Conference 2010, Proceedings of
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7642-8