| Project/Area Number |
13650815
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
化学工学一般
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| Research Institution | Kyushu University |
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Principal Investigator |
TSUGE Yoshifumi Kyushu University, Faculty of Engineering, Associate Professor, 大学院・工学研究院, 助教授 (00179988)
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| Project Period (FY) |
2001 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2003: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2002: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2001: ¥2,300,000 (Direct Cost: ¥2,300,000)
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| Keywords | Operation Management / Operation Know-How / Data Mining / Time Series Data / Process Trend |
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| Research Abstract |
We have developed a method for automatic extraction of operational know -how from historical information concerning plant operations. In this method, an operational know-how is extracted as an if-then rule by using C4.5, which is a one of tools for data mining. Then, special preprocessing should be necessary to deal with such a time series data as historical information concerning plant operations because events or records treated by C4.5 are naturally independent each other and the 'order of records is meaningless. In this study, we pay attention that operators watch not only present values of variables but also trends of variables and make a decision about their operations by judging whether trends are qualitatively increased or decreased. So, we have proposed to use qualitative values of differences of variables between sampling periods. As a result, it has been definitely that operational know-how can be extracted from historical information concerning plant operations if adequate selection and preprocessing of variables may be performed. However, C4.5 sometimes extracted wrong rules that were explained by inverse relations between cause and effect. In future work, we will combine C4.5 with a concept that can explain cause and effect relationship such as correlation coefficients.
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