By Kauko Leiviskä (auth.), Prof. Kauko Leiviskä (eds.)
Applications of sentimental Computing have lately elevated and methodological improvement has been robust. The e-book is a set of latest fascinating business functions brought by means of numerous study teams and business companions. It describes the foundations and result of commercial purposes of soppy Computing tools and introduces new chances to realize technical and fiscal advantages through the use of this system. The e-book indicates how fuzzy good judgment and neural networks were utilized in the Finnish paper and metallurgical industries placing emphasis on strategies, purposes and technical and financial results.
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Smith and Edwards (1991) propose the combination of simulation and a real time expert systems to the recovery boiler control. The recovery boiler model uses a steady-state modular simulator. A real time advisory control integrates process knowledge in the form of rules and simulated values with process data. Rules use statistical process control (SPC) methods to reveal when a variable is in abnormal condition. This makes easily understood detection of sensor problems and process upsets possible in real time.
K. (1993): An expert system to trouble-shoot quality problems in the continuous casting of steel billets. Iron & Steelmaker 20(9), 29-36. , Saarelainen E. (2000): Visual quality control of the vacuum tank degassing. , Vapaavuori, E. ): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. , Leung, K. (1991): Copper Flotation Expert System at Mount Isa Mines Limited. R. ) Proceedings Copper 91, pp. , Lappainen, H. -L. (1995): On-line determination of ore type using cluster analysis and neural networks.
Fuzzy logic control of the flue gas fan in the lime kiln was reported by (Haataja and Ruotsalainen 1994). FLC was originally implemented in the mill's automation system. It had seven inputs and one output. The rule base consisted of 22 rules that were formulated originally based on operator interviews and later tuned manually by trial and error. The results were encouraging; the 39 annual savings in energy approached 2 million Finnish Marks. The modelling of the burning temperature is a part of a larger study.