尾砂胶结充填体强度影响因素分析与研究

【作者】 李鹏；

【导师】 黄德镛； 翁春林；

【作者基本信息】 昆明理工大学 ， 采矿工程， 2011， 硕士

【摘要】 资源问题和环境问题是当今人类社会面临的两大难题,矿产资源的开发利用,在资源减少的同时也将对环境造成一定的破坏。如何合理解决矿山开采后形成的采空区和选矿后大量的残留尾砂已经成为当今世界许多国家的重要研究课题。采空区的存在会引起一系列的安全隐患和经济问题：比如井下作业的安全、地表的下沉和塌陷、长期对采空区的监测和维护等；大量的残留尾砂污染地表环境,破坏土壤并且占用大量的地表用地。尾砂充填利用矿山大量的尾砂为原材料,与胶凝剂,水混合搅拌后冲入采空区承受地压压力,为井下生产产创造安全的作业环境,同时也将占用地表的尾砂送入地下,极大降低了尾砂对环境的破坏。本文针对国内某矿山的矿柱尾砂胶结充填,为矿山在充填工作中提供数据和理论上的依据。在室内进行尾砂胶结充填体的抗压强度试验,在7cm×7cm×7cm的模具中浇注尾砂胶结充填体。具体的方案为：在料浆浓度(70%、72%)不同和尾砂粒级(全尾砂、分级尾砂)不同的条件下进行灰砂比为1：3、1：4、1：5、1：7、1：10、1：12、1：15、1：20的尾砂胶结充填体抗压强度试验。每种配比共浇注9个模块,分为3组,每组的模块数为3个,养护的时间为3d、7d、28d。本次试验共浇注模块288块。得到尾砂胶结充填体抗压强度试验数据后,首先对数据进行直观的分析,找出尾砂胶结充填体与灰砂比、料浆浓度、尾砂粒级、养护时间的关系。其次对养护时间为28d的尾砂胶结充填体抗压强度进行极差和方差的分析,找出灰砂比、料浆浓度、尾砂粒级哪个是主因素,哪个是次要因素。找出最优组合和因素的显著性。最后使用matlab7.1软件建立尾砂胶结充填体抗压强度的BP神经网络模型和灰砂比的BP神经网络模型。比较室内试验数据和工程实际数据。计算两者的误差以及通过尾砂胶结充填体抗压强度的BP神经网络模型的预测数据和工程实际数据的比较。室内试验是否能为工程实际提供一定的参考和依据。更多还原

【Abstract】 Resources and environmental problems are two major problems in our human society nowadays. With deduce of mine production, the development and utilization of mineral resources have the great impact to our surroundings and cause some damages indeed. How reasonable solutions should be formed to solve the gob problem after mining and the existence of large amount tailings have become the world’s important research topics in many countries. The existence of gob areas will cause a security risk and economic issues:such as the safety of underground operations, surface subsidence and collapse, long-term monitoring of mined areas and maintenance; a lot of residue tailings pollution to surface environment, destroying soil structure and occupying a large number of surface sites. A large amount of tailings were made up as the raw materials, companied with the gelling agent as well as water, and then poured them into the mined area to hold the underground pressure. The purpose is to operate a safe underground working environment and also occupy the tailings into the ground to reduce the great damage to the environment.In this paper,54-58 regarded as the research target to provide the exact data and theoretical basis. The compressive strength of cement tailing backfill tested indoors. The cement tailing backfill was poured into the module,7cm×7cm×7cm casting mold. The Specific programs as follows:8 classified gray sand rations (1:3,1:4,1:5,1:7,1:10,1:12,1:15,1:20) which under different condition of slurry concentration(70%,72%) and tail sand level (full tailings, classified tailings) were took into the cement tailing compressive test. Each ratio gets 9 modules which divided into 3 groups and each group has 3 modules. Curing time relates to 3days,7days, and 28 days. The experiments were pouring 288modules.After the collection of compressive data from the cement tailing compressive strength test, the data analyzed directly to identify the relationship between cemented tailings backfill, gray sand ratio, slurry concentration, the sand level as well as the curing time.Secondly, according to the analyze data of twisting pillar cement tailings in the curing time about 28 days by using range and variance analysis method to identify the gray sand ratio, slurry concentration and the level sand, which aims to make sure the main factor and the secondary factor. Find out the significance of optimum combination and factors. Finally, matlab7.1 software is used to establish the compressive strength of cemented tailings backfill of the BP neural network model and Than suggested BP neural network model Comparative indoor test data and actual data. The error and calculated both by tailings cemented filling body of compressive strength of BP neural network models predict data and actual data comparison. For engineering laboratory test whether provide certain reference and basis.更多还原