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《Journal of Safety Science and Technology》 2014-05
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Study on GA_SVM optimal prediction model on flocculating sedimentation parameter of unclassified tailings

ZHANG Qin-li;CHEN Qiu-song;WANG Xin-ming;XIAO Chong-chun;School of Resources and Safety Engineering,Central South University;  
A GA_SVM model was established to optimize the flocculating sedimentation parameters. The tailings concentration,flocculant consumption and flocculant concentration were used as the input parameters and the sedimentation speed was confirmed to be the synthesized output parameter. Some training and validating samples were established through indoor experiment. Then,for predicting flocculating sedimentation parameters,a support vector machine(SVM) regression model was established. The mean square error of the value was made as a fitness function. Then,the model parameters were optimized through the genetic algorithm(GA). GA_SVM model was used in some mine,and the results showed that the best tailings concentration,flocculant consumption and flocculant concentration are 20% ~ 25%,10g / t and 0. 09%. Comparing with the experiment results,the relative error of prediction result can be controlled at about 5%. The application indicates that this mode makes good effect,and it provides a new method to optimize the flocculating sedimentation parameters.
【Fund】: 国家“十二五”科技支撑计划项目(2012BAC09B02)
【CateGory Index】: X751
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