机构地区:[1]北京科技大学土木与资源工程学院,北京100083 [2]矿物加工科学与技术国家重点实验室,北京102628 [3]北京科技大学金属矿山高效开采与安全教育部重点实验室,北京100083
出 处:《工程科学学报》2022年第11期1897-1908,共12页Chinese Journal of Engineering
基 金:山东省重大科技创新工程资助项目(2019SDZY05);中央高校基本科研业务费专项资金资助项目(FRF-TP-20-039A1);矿物加工科学与技术国家重点实验室开放基金资助项目(BGRIMM-KJSKL-2021-18);中国博士后科学基金资助项目(2021M690363)。
摘 要:针对某露天转地下矿山充填成本高的问题,充分利用矿山周边的工业废弃物开发满足嗣后充填采矿法所要求的充填胶凝材料,并对充填料浆的配比进行了优化.首先,分析了材料的物化特性,采用不同的激发配方进行了室内试验,构建了用于钢渣基胶凝材料配方预测的GA-SVM模型,确定了钢渣基胶凝材料的最佳配方(质量分数)为:钢渣30%、脱硫石膏4%、水泥熟料12%、芒硝1%;其次采用XRD和SEM分析了钢渣基胶凝材料的水化机理;最后基于灰靶多目标决策模型对料浆配比进行优化实验,以强度(7 d和28 d)、工作特性(坍落度、泌水率)、成本为指标优化料浆配比.结果表明,采用新型钢渣基胶凝材料,充填料浆的最佳配比参数为:灰砂比1∶4,固相质量分数为72%.并进行了验证实验,得到相应强度参数和工作特性参数分别为1.74 MPa、3.61 MPa、24.2 cm和5.91%,均满足嗣后充填的要求,此配比条件下的充填成本为每立方米113元,较水泥充填成本降低了38.92%.To address the problem of high filling cost in an open pit to an underground mine,based on the machine learning method,the filling cementitious material needed for subsequent backfill mining method was developed using the available industrial wastes around the mine,and the ratio of filling slurry was optimized.First,the physical and chemical properties of the materials were analyzed.Unconfined compressive strength tests were conducted with different activator formulations to analyze the influence of each component on the strength of the backfill body.A genetic algorithm and support vector machine(GA-SVM) model was established to predict the steel-slag-based cementitious material formula using the experimental data,and the optimal ratio was determined based on the model prediction results.X-ray diffraction(XRD) and scanning electron microscope(SEM) were used to analyze the hydration products and microstructure characteristics of steel-slag-based cementitious materials at different curing ages and slag dosage conditions and determine the hydration mechanism of steel-slag-based cementitious materials.Finally,the slurry proportion was optimized by strength (i.e.,7 and 28 days) and working characteristics (i.e.,slump and bleeding rate) based on the principle of gray target decision.Results revealed that the relative errors of the GA-SVM model for predicting the steel-slag-based cementitious materials strength at 7 and 28 days are 3.6%–12.62% and 6.9%–10.19%,respectively,thereby indicating high prediction accuracy.The optimal proportion of steelslag-based cementitious materials determined by prediction analysis is steel slag content of 30%,desulfurized gypsum content of 4%,cement clinker content of 12%,and mirabilite content of 1%.The main hydration products of steel-slag-based cementitious materials are amorphous C-S-H gel,ettringite,tricalcium aluminate hydrate,Ca(OH),and CaCO.The calcium hydroxide content increases with the steel slag content,which generates a large number of pores and deteriorates the structure
关 键 词:膏体充填 GA-SVM 钢渣基胶凝材料 灰靶决策模型 配比优化
分 类 号:TG862.2[金属学及工艺—公差测量技术]
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