摘要
充填材料性能是充填开采控制地表沉陷的一个重要因素,为克服传统充填材料性能优化试验耗时长、成本高、人为误差较大的缺点,探索将传统正交试验与模糊决策相结合进行充填材料性能优化。基于L9(33)正交试验,确定了S1~S55组充填材料性能优选方案。在综合分析充填材料性能影响因素的基础上,建立充填材料性能优化指标体系,构建AHP-FUZZY模糊综合优选模型对方案进行比选,并分析了正交试验中各因素水平变化对最优方案的敏感性。结果表明:膏体充填材料性能最优方案为"S4"即水泥含量15%,灰矸比1∶2,质量浓度75%,决策结果与试验结果相符,且"灰矸比"对充填材料性能优化的敏感性程度最高,在实际膏体充填开采中要严格控制粉煤灰和煤矸石掺量。
The performance of filling material is an important factor for controlling the surface subsidence by backfill mining.In order to overcome the disadvantages of long time-consuming,high cost and greater human error in the traditional performance optimization experiments of filling material,it was explored to carry out the performance optimization of filling material by combining the e traditional orthogonal experiment with the fuzzy decision-making. Firstly,five sets of filling material performance optimization scheme that remembered as S1 to S5 were determined through L9（ 33） orthogonal test. Then,the optimization index system and AHP-FUZZY comprehensive optimization model of the performance of filling materials were established based on the comprehensive analysis of the factors affecting the performance of filling materials. In addition,the sensitivity of the change of each factor in the orthogonal experiment to the optimal scheme were analyzed. The results showed that the optimal scheme of the performance of filling materials was ＂ S4＂,that was,the cement content was 15%,the fly ash-gangue ratio was 1∶2,and the mass concentration was 75%. The results from fuzzy decision-making method was consistent with the result from orthogonal experiment. Moreover,the fly ash-gangue ratio was the most sensitive to the performance optimization of filling material,so the mixing amount of fly ash and coal gangue must be strictly controlled in the actual backfill mining.
出处
《中国安全生产科学技术》
CAS
CSCD
北大核心
2017年第10期143-149,共7页
Journal of Safety Science and Technology
基金
国家自然科学基金项目(41601593)
关键词
充填开采
充填材料
正交试验
AHP
模糊决策
性能优化
backfill mining
filling material
orthogonal experiment
analytic hierarchy process(AHP)
fuzzy decision-making
performance optimization
