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Coupled fuzzy logic and experimental design application for simulation of a coal classifier in an industrial environment

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Design of experiments (DOE) is an effective method providing useful information about the interaction of operating variables and the way the total system works by using statistical analyses. However, its industrial application is limited because it is almost difficult to maintain variables in DOE matrix at desired constant levels in industrial environment. Thus, this paper aims to present a new mixed modeling method which is a combination of fuzzy logic and design of experiments methods to overcome such practical limitations. The method first uses a fuzzy model which is trained by practical data gathered from industry to predict DOE response corresponding to each run in DOE matrix. Then, a statistical parametric model is constructed for the prediction of process response to any change of operating parameters under real industrial conditions. The proposed mixed method was successfully validated by using data obtained from a coal hydraulic classifier at Zarand Coal Washing Plant (Kerman, Iran). The method also seems to be a promising tool for modeling all devices and processes in real industrial environment and allows researchers to benefit from all the advantages of experimental design and fuzzy logic methods simultaneously.
Rocznik
Strony
504--515
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Department of Mining Engineering, Higher Education Complex of Zarand, Zarand, Iran
  • Department of Mining Engineering, Higher Education Complex of Zarand, Zarand, Iran
  • Department of Mining Engineering, Higher Education Complex of Zarand, Zarand, Iran
Bibliografia
  • ABKHOSHK, E., KOR, M., REZAI, B., 2010. A study on the effect of particle size on coal flotation kinetics using fuzzy logic, Expert Syst. Appl. 37(7), 5201–5207.
  • AZADEH, A., SABERI, M., ASADZADEH, S.M., KHAKESTANI, M., 2011. A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: The cases of USA, Canada, Singapore, Pakistan and Iran, Energy 36(12), 6981–6992.
  • CLARKE, G.M., KEMPSON, R.E., 1997. Introduction to the Design and Analysis of Experiments. Arnold, London.
  • CORNELL, J.A., 1990. How to Apply Response Surface Methodology. 2nd ed., American Society for Quality Control, Wisconsin.
  • DEAN, A., LEWIS, S., 2006. Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics. Springer Science & Business Media, New York.
  • HAMETNER, C., JAKUBEK, S., 2013. State of charge estimation for Lithium Ion cells: Design of experiments, nonlinear identification and fuzzy observer design, J. Power Sour. 238, 413–421.
  • GUI, X.H., LIU, J.T., LI, Y.F., CAO, Y.J., LIU, C., LI, G.S., 2010. Study on basic theory of the grain settlement in fluidised bed, XXVth International Mineral Processing Congress (IMPC 2010), Brisbane, Australia.
  • GUPTA, A., DENIS, Y., YAN, D.S., 2006. Mineral Processing Design and Operation: An Introduction. Elsevier, The Netherlands.
  • HARRIS, J., 2000. An Introduction to Fuzzy Logic Applications. Kluwer Academic Publishers, The Netherlands.
  • KHOSHDAST, H., 2014. A parametric model for predicting cut point of hydraulic classifiers, J. Min. Environ. 5(1), 47–54.
  • KHOSHDAST, H., KHOSHDAST, H., SHOJAEI, V., 2014. Effect of baffle design parameters on fluid dynamic response of a coal classifier, J. Min. World Exp. 3(1), 15–23.
  • KHOSHDAST, H., SAM, A., HASANI, F., KHOSHNAM, F., 2012. Efficiency evaluation of Masliyah model for the prediction of cut size of vertical current hydraulic classifiers, J. Anal. Num. Method Min. Eng. 1(2), 32–39.
  • LEEKWIJCK, W.V., KERRE, E.E., 1999. Defuzzification: criteria and classification, Fuzzy Set. Syst. 108(2), 159–178.
  • LIU, H.L., CHIOU, Y.R., 2005. Optimal decolorization efficiency of Reactive Red 239 by UV/TiO2 photocatalytic process coupled with response surface methodology, Chem. Eng. J. 112, 173–179.
  • MAMDANI, E.H., ASSILIAN, S., 1999. An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Hum-Comput. St. 51(2), 135–147.
  • MONTGOMERY, D.C., 2013. Design and Analysis of Experiments. 8th ed., John Wiley & Sons, Inc., New York.
  • NGUYEN, H.T., WALKER, E.A., 2006. A First Course in Fuzzy Logic. 3rd ed., Taylor & Francis Group, New York.
  • OLIVERO, R.A., SESHADRI, S., DEMING, S.N., 1993. Development of an expert system for selection of experimental designs, Anal. Chimica Acta 277(2), 441–453.
  • RAHMANIAN, B., PAKIZEH, M., ESFANDYARI, M., HESHMATNEZHAD, F., MASKOOKI, A., 2011. Fuzzy modeling and simulation for lead removal using micellar-enhanced ultrafiltration (MEUF), J. Hazard. Mater. 192, 585– 592.
  • RAZAVI PARIZI, S.E., 2010. Introduction to Linear Regression Analysis. Shahid Bahonar University Press, Kerman.
  • SIVANANDAM, S.N., SUMATHI, S., DEEPA, S.N., 2007. Introduction to Fuzzy Logic Using MATLAB. Springer Berlin, Heidelberg New York.
  • TRIPATHY, S.K., BHOJA, S.K., KUMAR, C.R., SURESH, N., 2015. A short review on hydraulic classification and its development in mineral industry, Powder Technol. 270, 205–220.
  • TSENG, T.L., KONADA, U., KWON, Y., 2016. A novel approach to predict surface roughness in machining operations using fuzzy set theory, J. Computat. Des. Eng. 3(1), 1–13.
  • WILLS, B.A., FINCH, J.A., 2015. Wills' Mineral Processing Technology. 8th ed., Elsevier, The Netherlands.
  • YAGER, R.R., ZADEH, L.A., 1992. An Introduction to Fuzzy Logic Applications in Intelligent Systems. Springer Science, New York.
  • YANG, F., WU, S., PFEIFER, T., HENSE, K., 2006. Optimization of multi-criteria experiments with fuzzy results, Tsinghua Sci. Technol. 11(6), 686–692
  • YETILMEZSOY, K., DEMIREL, S., VANDERBEI, R.J., 2009. Response surface modeling of Pb(II) removal from aqueous solution by Pistacia vera L.: Box–Behnken experimental design, J. Hazard. Mater. 171, 551–562.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b9d7ffb7-ffbd-46e4-9a3d-ec138177b4c4
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