【摘要】 采用分形理论研究分级尾砂的级配。计算国内外大量矿山尾砂材料的分形参数,研究尾砂的分形级配与其胶结强度的关系。采用神经网络建立尾砂胶结强度与水泥质量分数、料浆浓度、尾砂颗粒分维数、孔隙分维数及分维数相关率的知识库模型。研究结果表明,尾砂胶结强度与其分形级配相关;随着尾砂颗粒间孔隙分维数减小,充填体强度增高;尾砂分维数相关率越大,充填体强度越大。用所建立的神经网络知识库模型,利用Logistic迭代方程的混沌遍历特性,采用混沌优化方法研究使充填体达到最佳强度的选矿尾砂最优分级。根据采场脱水工艺,应用该方法,获得安庆铜矿选矿尾砂的最佳级配参数。工程应用结果显示,该方法年增加尾砂用量2.87万t,胶结充填体强度提高8%,为矿山年节约充填成本55.8万元。更多还原

【Abstract】 Gradation of classifying tailings was researched with fractal theory. Fractal parameters of tailings material applied in a lot of domestic and overseas mines were calculated, and the relationship between the fractal gradation of classifying tailings and its cementing strength was studied. A knowledge bank embodying the relations between strength of cementing tailings and content of cement, concentration of slurry, fractal dimension of granule, fractal dimension of porosity, correlated coefficient of fractal dimension was established with neural network. The results show that the strength of cementing tailings is related to its fractal gradation. The strength of cementing tailings increases with the decrease of fractal dimension of porosity, and the higher the correlated coefficient of fractal dimension, the higher the strength of cementing tailings. According to the knowledge bank and chaotic travelling of Logistic iterative equation, the optimum gradation of mill tailings which can reach the optimum cementing strength was investigated using the method of chaotic optimization. According to dehydrating technology in stope, using the above method, the optimum gradation parameters of mill tailings in Anqing copper mine were obtained. The results of engineering application show that not only the amount of tailings is increased by (27800) per annum, but also the strength of cementing backfill is enhanced by 8%, and filling cost of (558000) yuan(RMB) per annum is saved.更多还原