Optimization of soft-sensing model for ash content prediction of flotation tailings by image features tailings based on GA-SVMR

• Autorzy: Wang Guanghui , He Ting , Kuang Yali , Lin Zhe

Identyfikatory

DOI

10.37190/ppmp/122711

• Języki publikacji: EN

Abstrakty

EN

Ash content is one of the most important properties of coal quality and the ash prediction of coal slurry in floatation is urgent and important for automation of the floatation process. The aim of this paper is to propose a method of ash content prediction for flotation tailings by the use of image analysis. The mean gray value, energy, skewness and coal slurry concentration are highly correlated with coal slurry ash content by correlation analysis based on experiments while the particles’ size has little effect on the ash. Single variable linear prediction model between coal ash content and mean gray value was developed by the LS and its prediction errors were below 7%. For improving the prediction results, an ash prediction model based on GA-SVMR was established with additional three input parameters: energy, skewness, coal slurry concentration. This model has a higher accuracy with predictive errors all below 5% and 80% of them less than 3%. Results indicate that GA-SVMR model has a higher precision compared with LS model and PSO-SVMR model and soft-sensing model based on image features of the slurry can be used as a new method for ash detection of floatation tailings in automatic control process of coal flotation.

Słowa kluczowe

EN

GA-SVMR; image features; flotation; ash content

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2020
• Tom: Vol. 56, iss. 4
• Strony: 590--598
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Wang Guanghui

• Key Laboratory of Coal Processing and Efficient Utilization of Ministry of Education, School of Chemical Engineering and Technology, China University of Mining & Technology, Xuzhou, 221116, Jiangsu, China

autor

He Ting

• Key Laboratory of Coal Processing and Efficient Utilization of Ministry of Education, School of Chemical Engineering and Technology, China University of Mining & Technology, Xuzhou, 221116, Jiangsu, China

autor

Kuang Yali

• Key Laboratory of Coal Processing and Efficient Utilization of Ministry of Education, School of Chemical Engineering and Technology, China University of Mining & Technology, Xuzhou, 221116, Jiangsu, China

autor

Lin Zhe

• Key Laboratory of Coal Processing and Efficient Utilization of Ministry of Education, School of Chemical Engineering and Technology, China University of Mining & Technology, Xuzhou, 221116, Jiangsu, China

Bibliografia

• ALLEN, T. 1969. Determination of the size distribution and specific surface of fine powders by photoextinction methods I: Theoretical estimate of variation in extinction coefficient with particle size using a white light source. Powder Technology 2(3): 133–140.
• CHEN C. Y., YEH K. L., AISYAH R., LEE D. J., CHANG J. S., 2011. Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: a critical review. Bioresource technology, 102(1), 71-81.
• CITIR C., AKTAS Z., BERBER R., 2004. Off-line image analysis for froth flotation of coal. Computers & chemical engineering, 28(5), 625-632.
• CLAUDIO A.P., PABLO A.E., PABLO A.V., LUIS E. C., ARAVENA C.M., DANIEL A. S., LEONEL E. M., 2011. Ore grade estimation by feature selection and voting using boundary detection in digital image analysis. International Journal Processing, 101(1), 28-36.
• CTVRTNICKOVA T., MATEO M.P., YAÑEZ A., NICOLAS G., 2010. Laser Induced Breakdown Spectroscopy application for ash characterisation for a coal fired power plant. Spectrochimica Acta Part B: Atomic Spectroscopy, 65(8), 734-737.
• DIAMBOMBA H. TUNGADIO, JACOBUS A. JORDAAN, MUKWANGA W. SITI, 2016. Power system state estimation solution using modified models of PSO algorithm: Comparative study. Measurement, 92, 508-523.
• G.T. ADEL AND G.H. LUTTRELL, 1996. Development of a video-based slurry sensor for on-line ash analysis. Technical Progress Report.
• GÜLHAN ÖZBAYOĞLU, A. MURAT ÖZBAYOĞLU, M. EVREN ÖZBAYOĞLU, 2008. Estimation of Hardgrove grindability index of Turkish coals by neural networks. International Journal of Mineral Processing, 85(4), 93-100.
• HARGRAVE J. M., MILES N. J., HALL S. T., 1996. The use of grey level measurement in predicting coal flotation performance. Minerals Engineering, 9(6), 667-674.
• HOLTHAM P. N., NGUYEN K. K., 2002. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam. International Journal of Mineral Processing, 64(2), 163-180.
• JORJANI E., POORALI H. A., SAM A., CHELGANI S. C., MESROGHLI SH., 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.
• PATIL S. G., MANDAL S., HEGDE A. V., 2012. Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater. Advances in Engineering Software, 45(1), 203-212.
• SADR-KAZEMI N., CILLIERS J. J., 1997. An image processing algorithm for measurement of flotation froth bubble size and shape distributions. Minerals Engineering, 10(10), 1075-1083.
• SHEAN B. J., CILLIERS J. J., 2011. A review of froth flotation control. International Journal of Mineral Processing, 100(3), 57-71.
• WANG L., LI Y., FAN R., FAN R., 2019. Influencing mechanisms of sodium hexametaphosphate on molybdenite flotation using sea water. Physicochemical Problems of Mineral Processing, 55(5), 1091-1098.
• WANG W., CHEN L., 2015. Flotation Bubble Delineation Based on Harris Corner Detection and Local Gray Value Minima. Minerals, 5(2), 142-163.
• ZHANG H.Y., KUANG Y.L., WANG G.H., LI J., 2014. Soft Sensor Model for Coal Slurry Ash Content Based on Image Gray Characteristics. International Journal of Coal Preparation & Utilization, 34(1), 24-37.
• 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.