To improve the working performance of filling slurry, increasing strength and density of backfill body is a research direction of mine filling method. Filling ratio experiments showed that adding appropriate amount of water reducers during the preparation of the filling material could increase the compressive strength of the filling body. In order to obtain economic and efficient water reducers and parameters, four kinds of water reducers, i.e., naphthalene, amino, wood, and calcium binding aliphatic were used for new filling materials. A match experiment with certain mine backfilling materials was carried out, and a GA-SVM prediction model was established to optimize the selection. The four kinds of water reducers were used as the input data and the 28 days compressive strengths of filling body were confirmed to be the synthesized output data. Some training and validating samples were established through indoor experiment; a support vector machine (SVM) regression model was established. Then, the model parameters were optimized through the genetic algorithm (GA). The results show that the best tailing concentrations of the four kinds of water reducers were 0, 0.35%, 0.30%, and 0.60%, and that the compressive strength of filling body could be 4.20 MPa. Compared with the experiment results, the relative error of the prediction result can be controlled within about 1%. This model provides a new method to optimize sedimentation parameters.
ZHANG Qinli
,
LI Hao
,
LIU Jixiang
,
LIU Qunwu
,
CHEN Qiusong
. Prediction of water-reducers influence on strength of backfill body using GA-SVM model[J]. Science & Technology Review, 2015
, 33(11)
: 44
-48
.
DOI: 10.3981/j.issn.1000-7857.2015.11.007
[1] 王新民, 古德生, 张钦礼. 深井矿山充填理论与管道输送技术[M]. 长沙: 中南大学出版社, 2010. Wang Xinmin, Gu Desheng, Zhang Qinlin. Theory and technology of deep mine backfilling[M]. Changsha: Central South University Press, 2010.
[2] 李作章, 徐日升. 尾矿库安全技术[M]. 北京: 航空工业出版社, 1996. Li Zuozhang, Xu Risheng. Safety Technical for tailing pond[M]. Beijing: Aviation Industry Press, 1996.
[3] 彭亮茗, 舒杰. 浅析混凝土外加剂使用及其发展[J]. 上海建材, 2010 (1): 7-8. Peng Liangming, Shu Jie. Analysed the using and development of concrete admixture[J]. Shanghai Building Materials, 2010(1): 7-8.
[4] Wang X M, Zhao B, Zhang Q L. Cemented backfilling technology with unclassified tailings based on vertical sand silo[J]. Journal of Central South University of Technology, 2008, 15(6): 800-807.
[5] 张钦礼, 李谢平, 杨伟, 等. 基于BP网络的某矿山充填料浆配比优化[J]. 中南大学学报: 自然科学版, 2013, 44(7): 2868-2874. Zhang Qinli, Li Xieping, Yang Wei, et al. Optimization of filling slurry ratio in a mine based on back-propagation neural network[J]. Journal of Central South University: Science and Technology Edition, 2013, 44(7): 2868-2874.
[6] 韩斌, 王贤来, 肖卫国, 等. 基于多元非线性回归的井下采场充填体强度预测及评价[J]. 采矿与安全工程学报, 2012, 29(5): 714-718. Han Bin, Wang Xianlai, Xiao Weiguo, et al. Estimation and evaluation of backfill strength in underground stope based on multivariate nonlinear regression analysis[J]. Journal of Mining & Safety Engineering, 2012, 29(5): 714-718.
[7] 吴景龙, 杨淑霞, 刘承水. 基于遗传算法优化参数的支持向量机短期负荷预测方法[J]. 中南大学学报: 自然科学版, 2009, 40(1): 180-184. Wu Jinglong, Yang Shuxia, Liu Chengshui. Parameter selection for support vector machines based on genetic algorithms to short- term power load forecasting[J]. Journal of Central South University of Technology: Natural Science Edition, 2009, 40(1): 180-184.
[8] 李伟超, 宋大猛, 陈斌. 基于遗传算法的人工神经网络[J]. 计算机工程与设计, 2006, 27(2): 316-318. Li Weichao, Song Dameng, Cheng Bin. Artificial neural network based on genetic algorithm[J]. Compute Engineering and Design, 2006, 27(2): 316-318.
[9] Jae H M, Lee Y C. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters[J]. Expert Systems with Application, 2005, 28: 603-614.
[10] 张钦礼, 陈秋松, 胡威, 等. 充填钻孔寿命SVM优化预测模型研究[J]. 中南大学学报: 自然科学版, 2014, 45(1): 2868-2874. Zhang Qinli, Chen Qiusong, Hu Wei, et al. SVM optimal prediction model of the backfill drill-hole life[J]. Journal of Central South University: Science and Technology Edition, 2013, 44(7): 2868-2874.
[11] 李蓓智, 李立强, 杨建国, 等. 基于GA-SVM的质量预测系统设计和实现[J]. 计算机工程, 2011, 37(1): 167-169. Li Beizhi, Li Liqiang, Yang Jianguo, et al. Design and implementation of quality prediction system based on GA- SVM[J]. Computer Engineering, 2011, 37(1): 167-169.
[12] 郑春红, 焦李成, 郑贵文. 基于GA的遥感图像目标SVM自动识别[J]. 控制与决策, 2005, 20(11) : 1212-1215. Zheng Chunhong, Jiao Licheng, Zheng Guiwen. Genetic algorithm based SVM for automatic target classification of remote sensing images[J]. Control and Decision, 2005, 20(11): 1212-1215.
[13] 范作鹏, 任少峰, 张忠辉, 等. 高效减水剂对全尾砂充填料性能的影响[J]. 金属矿山, 2014, 455(5): 40-44. Fan Zuopeng, Ren Shaofeng, Zhang Zhonghui, et al. Effects of superplasticizers on properties of whole tailing backfilling materials[J]. Metal Mine, 2014, 455(5): 40-44.
[14] 曾照凯, 张义平, 吴刚. 基于正交优化的胶结充填体强度试验研究[J]. 有色金属, 2011, 62(3): 6-8. Zeng Zhaokai, Zhang Yiping, Wu Gang. Cementing filling body strength test research based on orthogonal optimization[J]. Nonferrous Metal, 2011, 62(3): 6-8.
[15] 魏微, 高谦. 减水剂对全尾砂新型胶凝材料强度的影响[J]. 混凝土, 2013, 5: 80-82. Wei Wei, Gao Qian. Effects of water' reducers on strength of neotype whole-tailings cementing materials[J]. Concrete, 2013, 5: 80-82.
