重庆大学学报2017，Vol.40Issue(1)：48-56,9.DOI:10.11835/j.issn.1000-582X.2017.01.006
磁化处理的全尾砂料浆沉降参数优化模型
Optimal prediction model on sedimentation parameters of pre-magnetized crude tailings slurry
摘要
Abstract
In order to improve the dewatering and concentrating effect of crude tailings slurry (CTR), magnetic treatment technique was introduced into dewatering and concentrating of CTR,and a GA-SVM (genetic algorithm-support velocity machine)model was established to optimize sedimentation parameters of CTR.The SVM model for predicting sedimentation parameters of pre-magnetized CTR was established and trained with sample data got from orthogonal experiments,taking magnetic induction,magnetized time,cycling velocity and mass concentration of CTR,unit consumption of flocculant as input factors,and sedimentation velocity as comprehensive output factor. Then a GA-SVM model for optimizing sedimentation parameters of pre-magnetized CTR could be obtained after parameters of SVM model optimized by the genetic algorithm.Furthermore,the GA-SVM model was adopted into an iron mine’s pre-magnetized CTR to optimize its sedimentation parameters,and the optimized sedimentation velocity was about 155 cm/h when magnetic induction,magnetized time,cycling velocity of CTR and unit consumption of PAC flocculant was 0.192 T,1.85 min,1.92 m/s and 28 g/t,respectively.The study results show that dewatering and concentrating effect of CTR could be improved and PAC could be saved by 40% under suitable condition of magnetic treatment.For optimizing sedimentation parameters of pre-magnetized CTR, the relative prediction error of GA-SVM model was less than 5%,which suggests that the GA-SVM model has higher prediction precision.The study also providing a new method to CTR ’s dewatering and concentration as well as its parameter optimization.关键词
全尾砂/磁处理/支持向量机/遗传算法Key words
crude tailings/magnetic treatment/support velocity machine/genetic algorithm分类
信息技术与安全科学引用本文复制引用
柯愈贤,王新民,张钦礼..磁化处理的全尾砂料浆沉降参数优化模型[J].重庆大学学报,2017,40(1):48-56,9.基金项目
国家“十一五”科技支撑计划项目（2008BAB32B03）。Supported by the 11th Five Year Key Programs for Science and Technology Development of China （2008BAB32B03）． （）
