Study on performance optimization of gasification slag based paste filling materials in Ningdong mining area
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摘要: 宁东矿区作为黄河流域的9个亿吨煤基地之一,年产出煤基固废近2×108 t且气化渣堆存量大、规模化利用困难、简单填埋处理空间有限,充填开采能解决空间堆存难题,但成本高、性能亟待优化。根据响应面法设计气化渣在固体中的掺量(A)、气化渣与水泥质量比(B)、料浆含量(C)3因素3水平共17组中心组合实验,对气化渣基膏体充填材料的坍落度、扩展度、7和14 d单轴抗压强度等性能进行了对比优化研究。实验前使用X射线衍射仪(XRD)和扫描电镜(SEM)对原料的成分及微观形态进行观测分析,试块单轴压缩后通过SEM观测分析水化作用特点,揭示强度形成机制。综合强度和流动性得到最优配比及其性能特征为:A为48%,B为3,C为80%,脱硫石膏∶煤矸石∶炉底渣的质量按2∶1∶1配制,其7、14 d强度分别为1.15、2.41 MPa,坍落度为133 mm,扩展度为325.5 mm,坍落度与扩展度的比值为0.41。进一步基于响应面法分析得到7、14 d强度的单影响因素按显著性排序分别为:B>C=A、B>A>C;7、14 d强度的交互影响因素按显著性排序分别为:BC>AB>AC、AB> AC>BC;坍落度和扩展度的单影响因素按显著性排序分别为:C>B>A和C>A>B,进而为严控地表沉降、快充减少堵管、强度成本兼顾3种不同功能需求优选了对应配比方案及参数。研究成果为黄河流域的生态保护与煤炭低损伤开采提供了重要基础参数和优化方向。
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关键词:
- 气化渣基膏体充填材料 /
- 响应面法 /
- 影响因素 /
- 性能优化 /
- 水化机理
Abstract: As one of the nine coal bases at hundred million ton level in Yellow River Basin, Ningdong Mining Area has nearly 2×108 t of coal-based solid waste produced annually, and faces to the problems of large stock of gasification slag, difficulty in scaled utilization, and limited space for simple landfill. Filling mining could solve the problem of stockpiling, of which the cost is high and the performance needs to be optimized. According to the response surface method, 17 groups of central composite experiments were designed for the three factors at three levels: the mixing amount of gasification slag in solid (A), the mass ratio of gasification slag to cement (B), and the slurry concentration (C). Meanwhile, comparative optimization research was conducted for the slump, expansion, 7 d and 14 d uniaxial compressive strength and other properties of gasification slag based paste filling materials. Before the experiment, the composition and micromorphology of the raw materials were observed and analyzed by XRD and SEM. Besides, the test block was observed by SEM and had its hydration characteristics analyzed after uniaxial compression, thereby revealing the strength formation mechanism. By integrating the strength and fluidity, the following optimum mixing ratio and performance characteristics of the filling material were obtained: 48% for A, 3 for B and 80% for C. Meanwhile, the desulfurized gypsum, coal gangue and bottom slag should be mixed at the mass ratio of 2∶1∶1 to reach the 7 d and 14 d strengths of 1.15 MPa and 2.41 MPa respectively, the slump of 133 mm, the expansion of 325.5 mm, and the slump to expansion ratio of 0.41. According to the further analysis based on response surface method, the single influencing factors of 7 d and 14 d strength are B>C=A and B>A>C, the interaction influencing factors of 7 d and 14 d strength are BC>AB>AC and AB>AC>BC, and the single influencing factors of slump and expansion are C>B>A and C>A>B respectively according to the significance. On this basis, the proportioning scheme and parameters were optimized for the 3 different functional requirements, including the strict control of surface settlement, rapid filling to reduce pipe plugging, and control of strength and cost. Generally, the research results provide important basic parameters and optimization direction for the ecological protection and low damage coal mining in Yellow River Basin. -
表 1 预实验结果
Table 1 Prepare test results
实验水平分组 7 d强度/MPa 14 d强度/MPa 坍落度/mm 扩展度/mm 低 1.22 2.01 139 351 中 0.83 1.39 135 322 高 0.75 0.94 121 308 表 2 实验方案
Table 2 Experimental scheme
组序号 A/% B C/% 1 52 5 80 2 50 4 80 3 48 3 80 4 50 5 83 5 50 5 77 6 50 4 80 7 48 5 80 8 50 4 80 9 48 4 83 10 50 3 83 11 50 4 80 12 50 3 77 13 52 4 77 14 52 4 83 15 52 3 80 16 50 4 80 17 48 4 77 表 3 7 d抗压强度推荐模型
Table 3 Recommended 7 d compressive strength model and analysis of variance
模型 校正R2 预测R2 备注 Linear 0.0077 −0.4844 2FI −0.2373 −2.1286 Quadratic 0.9431 0.7970 建议采用 Cubic 0.9459 表 4 14 d抗压强度推荐模型
Table 4 Recommended 14 d compressive strength model and analysis of variance
模型 校正R2 预测R2 备注 Linear −0.0640 −0.5018 2FI −0.1734 −1.4885 Quadratic 0.9181 0.7409 建议采用 Cubic 0.9130 表 5 坍落度推荐模型
Table 5 Recommended slump model
模型 校正R2 预测R2 备注 Linear 0.9329 0.8974 建议采用 2FI 0.9350 0.8391 Quadratic 0.9416 0.7517 Cubic 0.9555 表 6 扩展度推荐模型
Table 6 Extension recommendation model
模型 校正R2 预测R2 备注 Linear 0.9440 0.9103 建议采用 2FI 0.9432 0.8410 Quadratic 0.9214 0.5270 Cubic 0.9785 -
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