摘要
烧结脱硫灰渣是焦化厂生产炼钢焦煤排放的废弃物。由于脱硫灰渣含有游离氧化钙和亚硫酸钙等不稳定的矿物成分,导致烧结脱硫灰渣资源利用面临困难。针对鞍钢矿山全尾砂无废充填法开采,以矿渣微粉和烧结脱硫灰渣作为主要材料,熟料和硫酸盐为复合激发剂,对鞍钢矿山全尾砂充填胶凝材料进行开发。首先开展充填胶凝材料配比的正交试验;然后,在极差分析的基础上,建立神经网络模型和预测能力,揭示胶凝材料复合激发剂配比对充填体强度影响规律;最后,综合极差分析和预测分析结果,获得了利用烧结脱硫灰渣开发的全尾砂胶凝材料的最优配方。结果显示:以鞍钢全尾砂作为充填料,胶砂比为1∶6和浓度为70%的胶凝材料最优配比为:熟料4%,脱硫灰渣19%,芒硝0.5%,矿渣微粉76.5%,充填体28 d,强度最高达到3.883 MPa,是相同条件下水泥胶结充填体强度的1.7倍,但材料成本仅是水泥的40%~45%。
The sintering desulfurization ash is waste material discharged during the production of steelmaking coking coal in coking plant.The desulfurization ash consists some unstable mineral compositions,such as f-CaO and CaSO3,which makes it difficult to recycle.Due to the whole tailings filling method is used for mining in Anshan Steel Company,a new filling cementing material was developed with sintering desulfurization ash and water-quenched slag,which was activated by clinker and sulfate.Firstly,the orthogonal design was implemented.Then,the relationship between the cementitious filling body′s strength and the content of activator was revealed by a neural network model based on the range analysis.Finally,the optimal proportion was obtained according to range analysis and prediction models.The results indicated that using whole tailings as filling material,the mass ratio of the cementing material to tailings was16;the mass concentration of backfilling slurry was70%;and the optimal proportions of clinker,sintering desulfurization ash,mirabilite and slag were4%,19%,0.5%and76.5%,respectively.Under these conditions,the highest strength of filling body attained3.883MPa after28days,which was1.7times that of cementing pack body but costed only40%~45%of the cementing method.
作者
董培鑫
宋仁峰
高谦
张政
DONG Peixin;SONG Renfeng;GAO Qian;ZHANG Zheng(University of Science and Technology of Beijing, Beijing 100083, China;Mine Design Institute of Anshan Iron and Steel Group, Anshan 114002, China)
出处
《矿产保护与利用》
北大核心
2017年第5期24-28,共5页
Conservation and Utilization of Mineral Resources
基金
国家高技术研究发展计划(863计划)(SS2012AA062405)
关键词
烧结脱硫灰渣
新型胶凝材料
全尾砂
正交试验
人工神经网络
sintering desulfurization ash
new filling cementing material
whole tailings
orthogonal experiment
artificial neural network
