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Integrated estimation model of clean coal ash content for froth flotation based on model updating and multiple LS-SVMs

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Clean coal ash content, a prominent product index describing coal froth flotation, is difficult to be measured online. This constraint leads to a lack of timely guidance during operation and impedes the optimal operation of the coal flotation process. To solve this problem, considering the fluctuation of working conditions, the heterogeneity of raw coal and the variation of feed coal classes, an integrated estimation model of clean coal ash content for coal flotation based on model updating and multiple least squares support vector machines (LS-SVMs) is proposed. First, a single estimation model for a single class of coal based on LS-SVM is built, and the internal parameters are optimized by gravitational search algorithm (GSA). Second, the model updating strategy is designed to solve the problem of the decline in single model accuracy. Furthermore, a multiple LS-SVMs model formed by several single models for different classes of coal is studied along with the model switching mechanism to address the problem of model mismatch. Finally, an industrial experiment and evaluation are conducted. The mean relative error between the estimated and actual values is 3.32%, and the correlation coefficient is 0.9331. The estimation accuracy and adaptability of the integrated model can meet the industrial requirements.
Rocznik
Strony
21--37
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
  • aiyuan University of Technology, College of Mining Engineering, Taiyuan, 030024, Shanxi, China
autor
  • aiyuan University of Technology, College of Mining Engineering, Taiyuan, 030024, Shanxi, China
autor
  • aiyuan University of Technology, College of Mining Engineering, Taiyuan, 030024, Shanxi, China
autor
  • aiyuan University of Technology, College of Mining Engineering, Taiyuan, 030024, Shanxi, China
autor
  • Ji Zhong Energy Group, Xingtai Coal Preparation Plant, Xingtai, 054026, Hebei, China
Bibliografia
  • ABBOTT J., 1994. Advanced coal preparation monograph series volume VII part 16 control concepts: C.J. Clarkson Australian Coal Preparation Society, Carrington, Australia. Minerals Engineering, 7, 428-429.
  • AHMAD, Z., ZHANG, J., 2005. Combination of multiple neural networks using data fusion techniques for enhanced nonlinear process modelling. Computers & Chemical Engineering, 30(2), 295-308.
  • BU, X.N., XIE, G.Y, PENG, Y.L., CHEN, Y.R., 2016. Kinetic modeling and optimization of flotation process in a cyclonic microbubble flotation column using composite central design methodology. International journal of mineral processing, 157, 175-183.
  • DING, J. L., CHAI, T. Y., WANG, H., 2011. Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization. IEEE Transactions on Neural Networks, 22(3), 408- 419.
  • DING, J.L., CHAI, T.Y., CHENG, W.J., ZHENG, X.P., 2015. Data-based multiple-model prediction of the production rate for hematite ore beneficiation process. Control Engineering Practice, 45, 219-229.
  • DOMLAN, E., HUANG, B., XU, F., ESPEJO, A., 2011. A decoupled multiple model approach for soft sensors design. Control Engineering Practice, 19(2), 126-134.
  • DONG, S.J., LUO, T.H., 2013. Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement, 46, 3143-3152.
  • GAO, Y., WANG, X., WANG, Z.L., 2015. Fault detection for a class of industrial processes based on recursive multiple models. Neurocomputing, 169, 430-438.
  • Ge, X.H., 2013. Influence of operation parameters on separation performance of flotation column. Clean Coal Technology, 19(3), 6-9,13.
  • GONZÁLEZ, G.D., ORCHARD, M., CERDA, J. L., CASALI, A., VALLEBUONA, G., 2003. Local models for soft sensors in a rougher flotation ban. Minerals Engineering, 16(5), 441-453.
  • HOSSEINI, S. M., FATEHI, A., JOHANSEN, T. A., SEDIGH, A. K., 2012. Multiple model bank selection based on nonlinearity measure and h-gap metric. Journal of Process Control, 22(9), 1732–1742.
  • JONSSON K, KITTLER J, LI YP, MATAS J., 2002. Support vector machines for face authentication. Image and Vision Computing, 20, 369-375.
  • JORJANI, E., POORALI, H.A., SAM, A., CHEHREH CHELGANI, S. C., MESROGHLI, S., SHAYESTEHFAR, M. R., 2009. Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network. Minerals Engineering, 22(11), 970-976.
  • KOH, P.T.L., SCHWARZ, M. P., 2003. Cfd Modelling of Bubble–Particle Collision Rates and Efficiencies in a Flotation Cell. Minerals Engineering, 16(11), 1055-1059.
  • LANGONE, R., ALZATE, C., KETELAERE, B. D., VLASSELAER, J., MEERT, W., SUYKENS, J. A. K., 2015. LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. Engineering Applications of Artificial Intelligence, 37, 268-278.
  • LENG, X.Z., WANG J.H., JI, H.B., WANG, Q.G., LI, H.M., QIAN, X., LI, F.Y., YANG, M., 2017. Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses. Chemosphere, 180, 513-522.
  • LIU, J. T., 2000. Cyclonic-static micro-bubble floatation apparatus and method. US, US Patent 6073775 A.
  • LI, H.B., CHAI, T.Y., YUE, H., 2012. Soft sensor of technical indices based on KPCA-ELM and application for flotation process. Journal of Chemical Industry and Engineering, 63(9), 2892-2898.
  • NAKHAEI, F., MOSAVI, M. R., SAM, A., VAGHEI, Y., 2012. Recovery and grade accurate prediction of pilot plant flotation column concentrate: Neural network and statistical techniques. International Journal of Mineral Processing, 110–111(8), 140-154.
  • NAKHAEI, F., IRANNAJAD, M., 2013. Comparison between neural networks and multiple regression methods in metallurgical performance modeling of flotation column. Physicochemical Problems of Mineral Process, 49 (1), 255–266.
  • RASHEDI, E., NEZAMABADI-POUR, H., SARYAZDI., S., 2009. GSA: A Gravitational Search Algorithm. Information Sciences, 179, 2232-2248.
  • REN,C.C., YANG, J.G., LIANG, C., 2015. Estimation of copper concentrate grade based on color features and Least-squares support vector regression. Physicochemical problems of mineral processing, 51(1), 163-172.
  • SARAFRAZI, S., NEZAMABADI-POUR, H., 2013. Facing the classification of binary problems with a gsa-svm hybrid system. Mathematical and Computer Modelling, 57, 270-278.
  • SARMADI, H., KARAMODIN, A., ENTEZAMI, A., 2016. A new iterative model updating technique based on least squares minimal residual method using measured modal data. Applied mathematical modelling, 40(23), 10323-10341.
  • SHARIFI, N., OZGOLI, S., RAMEZANI, A., 2017. Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours. Computer Methods and Programs in Biomedicine, 144, 13-19.
  • SUBASI, A., 2013. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Computers in Biology & Medicine, 43(5), 576-586.
  • SUYKENS J.A.K., VANDEWALLE, J., 1999. Least squares support vector machine classifiers. Neural Processing Letters, 9: 293-300.
  • TAO, Y.J., LIU, W.L., LU, M.X., 1999. Study on mathematical model of fine coal flotation. Journal of China University of Mining & Technology, 28(5), 425-428.
  • VANNY, M., KO, K.E., PARK, S.M., SIM, K.B., 2013. Physiological responses-based emotion recognition using multi-class SVM with RBF Kernel. Journal of Institute of Control, Robotics and Systems, 19(4), 364–371.
  • WANG, D.H., 2010. Multivariate Statistical Analysis and SPSS Application. Shanghai: East China University of Science and Technology Press, 193-194.
  • XIONG, W., ZHANG, W., XU, B., HUANG, B., 2016. JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy. Computers & Chemical Engineering, 90, 260-267.
  • YANG, X.P., XU, D.P., WU, C.P., FENG, S.G., WANG, R.Z., 2000. Experimental study on the measurement of coal slurry ash content. Coal Science and Technology, (7), 19-21.
  • YANG, X.P., FENG, S.G., XU, D.P., WU, C.P., WANG, R. S., LI, Y., LI, J.Z., 2001. Study on detection and control of flotation process in coal preparation plant. Coal Science and Technology, (8), 1-4.
  • YANG, T., HUO, X.L., YU, H.S., 2008. The analysis of factors affecting cyclonic micro-bubble column flotation. Clean Coal Technology, 14(1), 12-14.
  • ZHANG, H.J., LIU, J.T., CAO, Y.J., WANG, Y.T., 2013. Effects of particle size on lignite reverse flotation kinetics in the presence of sodium chloride. Powder technology, 246, 658-663.
  • ZHANG, Y., QIN, Y., XING, Z. Y., JIA, L. M., CHENG, X. Q., 2013. Roller bearing safety region estimation and state identification based on LMD–PCA–LSSVM. Measurement, 46(3), 1315-1324.
  • ZHANG, Z.L., YANG, J.G., WANG, Y.L., DOU, D.Y., XIA, W.C., 2014. Ash content prediction of coarse coal by image analysis and GA-SVM. Powder Technology, 268, 429-435.
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-4940133d-22b8-4f14-8b17-3819e5787af6
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