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Soft Computing Applications in Mineral and Metal Industries

  • Chapter
Industrial Applications of Soft Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 71))

Abstract

Soft Computing has many industrial application areas, also in mineral and metal processing. Intelligent methods are used in Software Sensors to make the existing measurements more efficient or to replace the non-existing measurements with software systems that form the measurement signals e.g. from other, existing measurements. Both fuzzy logic and neural networks have been used. Another area is the monitoring of the measurement systems and assuring the good condition of the system.

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References

  • Alaraasakka, M., Leiviskä, K., Seppänen, M. (1998): Neural network based classification of blast furnace gas distribution. In: Proceedings TOOLMET ‘88 Symposium — Tool environments and development methods for intelligent systems. Control Engineering Laboratory, University of Oulu, Oulu, pp. 40–44

    Google Scholar 

  • Aldrich, C., van Deventer, J.S.C. (1994): The use of connectionist systems to reconcile inconsistent process data. The Chemical Engineering Journal 54, 125–135

    Google Scholar 

  • Aldrich, C., yen Deventer, J.S.C. (1995): Comparison of different artificial neural nets for detection and location of gross errors in process. Ind. Eng. Chem. Res. 34, 216–224

    Google Scholar 

  • Angstenberger, J. (1996): Blast furnace analysis with neural networks. In: Bulsari, A.B. (ed.): International Conference on Engineering Applications of Neural Networks Proceedings (EANN ‘86), London, UK, 37–40

    Google Scholar 

  • Bartolacci, G., Bouajila, A. (2000): Application of multivariable tools to mineral processing data analysis and modelling: Flotation case. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 188–193

    Google Scholar 

  • Benford, P., Meech, J.A. (1992): Advising flotation operators using a real-time expert system. Minerals Engineering 5 (10–12), 1325–1331

    Article  Google Scholar 

  • Bergh, L.G., Achurra, G.H., Chacana, B. (2000): Coal rate injection control in a slag cleaning furnace. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 183–187

    Google Scholar 

  • Bergh, L.G., Yinatos, J.B., Leiva, C.A. (1998): Fuzzy supervisory control of flotation columns. Minerals Engineering 11 (8), 739–748

    Article  Google Scholar 

  • Bogdanoff, A., Yliniemi, L., Leiviskä, K. (1998): Prototype fuzzy logic controller for electric arc furnace. In: Proceedings of TOOLMET ‘88 Symposium — Tool environments and development methods for intelligent systems. Control Engineering Laboratory, University of Oulu, Oulu, pp. 49–53

    Google Scholar 

  • Bourassa, M., Lanthier, R. (1995): Grinding circuit optimization at Mine Doyon. In: 27th Annual Meeting of the Canadian Mineral Processors. CIM Publications, Montreal, Canada, pp. 294–310

    Google Scholar 

  • Carvalho, M.T., Durao, F. (1999): Performance of a flotation column fuzzy controller. In: Mastorakis, N. (ed.): Computers and Computational Engineering in Control. World Scientific and Engineering Society Press, Athens, Greece, pp. 220–225

    Google Scholar 

  • Carvalho, M.T., Durao, F. (2000): Supervisor fuzzy controller of a flotation column. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 200–205

    Google Scholar 

  • Chojcan, J., Czogala, E., Tyma, G. (1995): On fuzzy control of the ventilation process in the Longwalls with methane emissions in coal mines. In: EUFIT’95 — Third European Congress on Intelligent Techniques and Soft Computing Proceedings. Verlag Mainz, Aachen, Germany, pp. 1036–1040

    Google Scholar 

  • Cipriano A., Guarini, M., Soto, A., Briceno, H., Mery, D. (1997): Expert supervision of flotation cells using digital image processing. In: Hoberg, H., von Blottnitz, H. (eds.): Proceedings of the 20th International Mineral Processing Conference. GDMB Publications, Aachen, Germany, pp. 281–292

    Google Scholar 

  • Cipriano, A., Medina, A., Martinez, C., Gonzalez, G. (1991): Supervisor and control strategy for a flotation plant. In: Proceedings IFAC Workshop on Expert Systems in Mining and Metals Processing. Finnish Society of Automation, Helsinki, Finland, pp. 281–285

    Google Scholar 

  • Cortez, L., Durao, F. (1995): An expert neural network to control a mineral flotation process. In: 25th International Symposium on the Application of Computers and Operations Research in the Mineral Industry (APCOM) Proceedings. AusIMM Publications, Carlton, Victoria, pp. 431–436

    Google Scholar 

  • Denby, B., Schofield, D. (1994): Open-Pit Design and Scheduling by Use of Genetic Algorithms. Trans (Section A: Mining Industry), IMM 103, A21 - A26.

    Google Scholar 

  • Denby, B., Schofield, D. (1995): The Use of Genetic Algorithms in Underground Mine Scheduling. In: 25th APCOM Proceedings. AusIMM Publications, Carlton, Victoria, pp 389–394

    Google Scholar 

  • Van Deventer, J.S.J., Bezuidenhout, M., Moolman, D.W. (1997): On-line visualisation of flotation performance using neural computer vision of the froth texture. In: Hoberg, H., von Blottnitz, H. (eds.): 20th International Mineral Processing Congress. GDMB Publications, Aachen, Germany, pp. 315–326

    Google Scholar 

  • Dominigues, S., Campoy, P., Aracil, R. (1994): A neural network based quality control system for steel strip manufacturing. In: Artificial Intelligence in Real Time Control, Proceedings, Valencia, Spain, pp. 185–190

    Google Scholar 

  • Du, Y.G., del Villar, R., Thibault J. (1997): Neural net-based softsensor for dynamic particle size estimation in grinding circuits. International Journal of Mineral Processing 52 (2–3), 121–135

    Article  Google Scholar 

  • Dupond, F., Odet, C., Carton, M., Alexandre, P. (1993): Automatic recognition and analysis of surface defects on pickled steel. In: SPIE 1993 Proceedings, Orlando, FL, USA, pp. 78–89

    Google Scholar 

  • Durao, F., Cortez, L.A. (1995): Neural network controller of a flotation process. In: 19th International Mineral Processing Conference (IMPC) Proceedings. SME Publications, Littleton, Co., pp. 245–250

    Google Scholar 

  • Ebner, J., Gould, L., Raml, H., Stiasny, H., Wasner, N., Fuerschuss H. (1997): Fuzzy logic control improves sinter plant performance at VA Stahl Linz. Steel Times International, January 1997, pp. 35–38

    Google Scholar 

  • Edwards, R., Mular, A.L. (1992): An Expert System Supervisor of a Flotation Circuit. CIM Bulletin 84 (959), 69–76

    Google Scholar 

  • Eggert, J., Folinsbee, J., Benford, P. (1994). SAG Mill Control at Dome Mines Using an Expert System, In: 26th Canadian Mineral Processors Proceedings. CIM Publications, Montreal, Canada, pp. 16–22

    Google Scholar 

  • Flament, F., Thibault, J., Hodouin, D. (1993): Neural network based control of mineral plants. Minerals Engineering 6 (3), 235–249

    Article  Google Scholar 

  • Frank, P.M. (1997): Implementation of a fuzzy logic based level control concept for continuous casting at Hüttenwerke Krupp Mannesmann. In: ERUDIT TC A Workshop: “Applications in Steel Industry”’, Aachen, Germany, 14 p

    Google Scholar 

  • Funabashi, M., Maenda, A. (1995): A hybrid and neural hybrid expert systems: synergetic AI. IEEE Expert, August 1995, 32–40

    Google Scholar 

  • Furukawa, K., Amo, K., Watanabe, S., Itahishi, M., Kosakai, I. (1992): Development of CC mould level control system by neural network. In: Current advances in Materials and Processes, Japan.

    Google Scholar 

  • Hatanaka, K., Tanaka, T., Kominami, H. (1993): Breakout forecasting system based on multiple neural networks for continuous casting in steel production. Fujitsu Scientific and Technical Journal, 29, 265–270

    Google Scholar 

  • Lida, O., Ushijima, Y., Sawada, T. (1995): Application of AI techniques to blast furnace operations. Iron and Steel Engineering 72 (10), 24–28

    Google Scholar 

  • ISIS. 1997. http://www.ento.vt.edu/Facilities/ OnCampus/ISISInfo.html

    Google Scholar 

  • Jämsä-Jounela, S.-L., Vapaavuori, E., Salmi, T., Gränbärj, M., Vermasvuori, M. (2000): Fault diagnosis system for the Outokumpu flash smelting process. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 441 118

    Google Scholar 

  • Karr, C.L. (1996): Strategy for adaptive process control for a column flotation unit. In: Ramani, R.V. (ed.): 26th International Symposium on the Application of Computers and Operations Research in the Mineral Industry (APCOM) Proceedings. SME Publications, Littleton, Co., pp. 303–307

    Google Scholar 

  • Koskinen, J., Yliniemi, L. (1996): Fuzzy control of a rotary dryer, In: Proceedings of TOOLMET’96 Symposium - Tool environments and development methods for intelligent systems. Control Engineering Laboratory, University of Oulu, Oulu, pp. 205217

    Google Scholar 

  • Koskinen, J., Yliniemi, L., Leiviskä, K. (1998): Fuzzy modelling of a pilot plant rotary dryer. In: International Conference on Control ‘88 Proceedings. University of Wales, Swansea, UK, 515–518

    Google Scholar 

  • Kumar, S., Meech, J.A., Samarasekera, I.V., Brimacombe, J.K. (1993): An expert system to trouble-shoot quality problems in the continuous casting of steel billets. Iron & Steelmaker 20 (9), 29–36.

    Google Scholar 

  • Kämäräinen, J.K., Kälviäinen H., Terho, K., Saarelainen E. (2000): Visual quality control of the vacuum tank degassing. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 379–384

    Google Scholar 

  • Lacouture, B, Russell, C., Griffin, P., Leung, K. (1991): Copper Flotation Expert System at Mount Isa Mines Limited. In: Dobby, G.S., Argyropoulos S.A., Rao, S.R. (eds.) Proceedings Copper 91, pp. 429–437

    Google Scholar 

  • Laine, S., Lappainen, H. Jämsä-Jounela, S.-L. (1995): On-line determination of ore type using cluster analysis and neural networks. Minerals Engineering 8 (6), 637–648

    Google Scholar 

  • Lassus, L., Saxén, H. (1998): System for pattern recognition of blast furnace wall temperatures. In: Proceedings of TOOLMET ‘88 Symposium–Tool environments and development methods for intelligent systems. Control Engineering Laboratory„ University of Oulu, Oulu, pp. 45–48

    Google Scholar 

  • Lee, D., Lee, Y. (2000): Application of neural network for improving accuracy of roll force model in hot-rolling mill. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 240–244

    Google Scholar 

  • Leiviskä, K. (1992): Application Viewpoints of Expert Systems in Mineral and Metal Processing. In: Proceedings of the IFAC Workshop Expert Systems in Mineral and Metal Processing. Pergamon Press, Oxford, pp. 191–195

    Google Scholar 

  • Leiviskä, K. (1996): Applications of intelligent methods in the Control Engineering Laboratory, University of Oulu. In: Proceedings of TOOLMET ‘86 Symposium–Tool environments and development methods for intelligent systems. Control Engineering Laboratory, University of Oulu, Oulu, pp. 28–34

    Google Scholar 

  • Leven, J., Jonsson, N.G., Wiklund, O. (1995): An artificial neural network for rolling applications. Steel Times 223 (4), 137–138

    Google Scholar 

  • Lu, C. (1998): Application of ANN in a combination with mathematical model in prediction of rolling load of the finishing stands in HSM. In: 7th International Conference on Steel Rolling Proceedings, Chiba, Japan, pp. 87–92

    Google Scholar 

  • Maki, Y., Takashima, N., Obata, H., Iida, O., Nakahima, K., Sawada, T. (1991): Application of fuzzy theory to ironmaking process control. Kawasaki Steel Technical Report, No. 24, pp. 8–10

    Google Scholar 

  • McDermott K., Clyle P., Hall, M., Harris, C.A. (1992): An Expert System for Control of No.4 Autogenous Mill Circuit at Wabush Mines. In: Canadian Mineral Processors Proceedings. CIM Publications, Montreal, Canada, Paper 24, pp. 20–24

    Google Scholar 

  • Meech J.A. (1995): AI Applications in the Mining Industry into the 21st Century. In: 25th International Symposium on the Application of Computers and Operations Research in the Mineral Industry (APCOM) Proceedings. AusIMM Publications, Carlton, Victoria. Also in: (http://www.mining.ubc. ca /faculty/meech/apcom.htm)

    Google Scholar 

  • Meech J.A. and Harris, C.A. (1992): Expert Systems for Gold Processing Plants. In: Randol Gold Forum, Vancouver, B.C., pp. 45–61

    Google Scholar 

  • Osorio, P., Perez-Correa, J.R., Cipriano, A. (1999): Assessment of expert fuzzy controllers for conventional flotation plants. Minerals Engineering 12 (11), 1327–1338

    Article  Google Scholar 

  • Otsuka, Y., Matsuda, K., Hanaoka, K. (1992): Application of neural network systems to pattern recognition of blast furnace operation data. Kobleco Technology Review, No. 15, 12–16

    Google Scholar 

  • Paris, A., Cipriano, A. (1991): Supervisor Expert Control of a semi autogenous grinding circuit. In: IFAC Workshop on Expert Systems in Mineral and Metals Processing. Finnish Society of Automation, Helsinki, pp. 211–216

    Google Scholar 

  • Palmu, P. (1998): Experience of fuzzy logic control system at Rautaruukki Raahe Steel. In: Proceedings of TOOLMET ‘88 Symposium — Tool environments and development methods for intelligent systems. Control Engineering Laboratory, University of Oulu, Oulu, pp. 32–39

    Google Scholar 

  • Perry, R., Hall, M. (1994): Les Mines Selbaie semi-autogenous grinding circuit expert system. In: 26th Annual Meeting of the Canadian Mineral Processors Proceedings. CIM Publications, Montreal, Canada, pp. 294–310

    Google Scholar 

  • Poirier, P., Raabe, H., Meech, J.A. (1993): Using froth identification in an advisory expert system for copper flotation operations. In: 23th Canadian Mineral Processors Conference Proceedings. CIM Publications, Montreal, Canada, pp. 14–19

    Google Scholar 

  • Poirier, P.J., Meech, J.A. (1993): Using fuzzy logic for on-line trend analysis. In: 2nd IEEE Conf. on Control Applications Proceedings, Vancouver, B.C., pp. 83–86

    Google Scholar 

  • Poppe, T., Schlang, M., Obradovic, D. (1995): Neural network: reducing energy and material requirements. Siemens Review 4, 24–27

    Google Scholar 

  • Portmann, N.F., Lindhoff, G., Sörgel, G., Gramckow, O. (1995): Application of neural networks in rolling mill automation. Iron and Steel Engineering 72 (2), 33–36

    Google Scholar 

  • Rakocevic, V.M., Meech, J.A. (1995): An artificial neural network to interpret froth images from a flotation process. CIM Bulletin 88, 16–22

    Google Scholar 

  • Rantala, A., Virtanen, H., Saloheimo, K., Jämsä-Jounela, S.-L. (2000): Using principal component analysis and self-organising map to estimate the physical quality of cathode copper. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 373–378

    Google Scholar 

  • Rauma, T., Nyberg, J., Herranen, J. (2000): Fuzzy control of furnace temperature in a zinc roaster. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 161–165

    Google Scholar 

  • Rausch, H. (1997): Control of blast furnace performance using neural networks. In: ESIT `97 — First European Symposium on Applications of Intelligent Technologies Proceedings. Verlag Mainz, Aachen, Germany, pp. 37–44

    Google Scholar 

  • Ruokonen, E. (1996): Application of the expert system based on neural network at the Kemi concentrator. In: Proceedings of TOOLMET’96 Symposium — Tool environments and development methods for intelligent systems. Control Engineering Laboratory. University of Oulu, Oulu, pp. 102–110

    Google Scholar 

  • Rämet, P.; Lehtola, S.; Leiviskä, K.; Mannermaa, J-P. (1996): The fluidized bed granulation process modelling and control using neural networks and fuzzy control. In: Zimmermannn, H.-J. (ed.): EUFIT’96 — Fourth European Congress on Intelligent Techniques and Soft Computing — Proceedings. Verlag Mainz, Aachen, Germany, pp. 1924–1928

    Google Scholar 

  • Schlang, M., Jansen, M., Lang, B., Poppe, T., Runkler, T., Weinzierl, K. (2000): Current and future developments in neural computation in steel processing. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 27–37

    Google Scholar 

  • Siemens: Newsletter Metals, Mining and More, Edition 3/97. Available on-line at: http://www.atd.siemens.de/metals_mining_more/index.html. 25.9.1998

    Google Scholar 

  • Takada, H., Hamada, K., Kaya, K. (1994): A design environment for neural networks and its applications. Control Engineering Practice 2 (2), 123–128

    Article  Google Scholar 

  • The intelligent arc furnace controller. Available on-line at http://www.neural.com /IAFController/IAFController.htm. 16.4.1998

    Google Scholar 

  • Thibault, J., Flament, F., Hoduin, D. (1991): Modelling and control of mineral processing plants using neural networks. In: IFAC Workshop on Expert Systems in Mineral and Metals Processing. Finnish Society of Automation, Helsinki, pp. 281–288

    Google Scholar 

  • Thomas, G.S. (1996a): Optimisation and scheduling of open pits via genetic algorithms and simulated annealing. In: Proceedings of the 1st International Symposium on Mine Simulation via the Internet in December 1996

    Google Scholar 

  • Thomas, G.S. (1996b): Pit optimisation and mine production scheduling — The way ahead. In: Ramani, R.V. (ed.): 26th International Symposium on the Application of Computers and Operations Research in the Mineral Industry (APCOM) Proceedings. SME Publications, Littleton, Co., pp 221–228

    Google Scholar 

  • Thomson, S.J., Peart, R.M. (1986): An expert system for soil moisture-based scheduling of center pivot irrigation. ASAE. Paper # 86, 4518

    Google Scholar 

  • Tolwinski B., Underwood R. (1992): An algorithm to estimate the optimal evolution of an open pit mine, In: The 23rd International Symposium on the Application of Computers and Operations Research in the Mineral Industry (APCOM) Proceedings. SME Publications, Littleton, Co., pp 399–409

    Google Scholar 

  • Vermeulen, W., Bodin, A., Zwaag, S. (1997): Prediction of measured temperature after the last finishing stands using artificial neural networks. Steel Research 68 (1), 20–26

    Google Scholar 

  • Wu, M., She, J.H., Nakano, M., Gui, W. (2000): Expert control and fault diagnosis of the leaching process in zinc hydrometallurgy plant. In: Jämsä-Jounela, S.-L., Vapaavuori, E. (eds.): Future Trends in Automation in Mineral and Metal Processing, IFAC Workshop Preprints. Finnish Society of Automation, Helsinki, pp. 351–356

    Google Scholar 

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  1. Control Engineering Laboratory, University of Oulu, Oulun yliopisto, P.O.Box 4300, FIN-90014, Oulu, Finland

    Kauko Leiviskä

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  1. Department of Process Engineering, University of Oulu, Oulun yliopisto, P.O. Box 4300, 90014, Finland

    Kauko Leiviskä

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Leiviskä, K. (2001). Soft Computing Applications in Mineral and Metal Industries. In: Leiviskä, K. (eds) Industrial Applications of Soft Computing. Studies in Fuzziness and Soft Computing, vol 71. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1822-2_2

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  • DOI: https://doi.org/10.1007/978-3-7908-1822-2_2

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