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基于RSM-BBD的拜耳法赤泥胶结充填配比优化
引用本文:李帅,于正,冯如,潘纪龙. 基于RSM-BBD的拜耳法赤泥胶结充填配比优化[J]. 黄金, 2022, 43(1): 34-41. DOI: 10.11792/hj20220106
作者姓名:李帅  于正  冯如  潘纪龙
作者单位:中南大学资源与安全工程学院
基金项目:湖南省自然科学基金项目(2021JJ40745);国家自然科学基金青年基金项目(51804337)。
摘    要:针对拜耳法赤泥难以规模化利用的问题,开发拜耳法赤泥作为矿山胶结充填骨料.基于配方探索试验结果,运用BBD方法设计9组试验,以响应面优化法探究料浆浓度和拜耳法赤泥C料质量比与充填体抗压强度、坍落度和C料单耗3个响应量的相关关系及最佳配比.结果表明:在拜耳法赤泥C料质量比为6.88、料浆浓度为58.69%时满意度值达到最大...

关 键 词:拜耳法赤泥  BBD试验设计  胶结充填  配方探索  响应面优化  配比优化

Proportioning optimization of cemented filling of Bayer red mud based on RSM-BBD
Li Shuai,Yu Zheng,Feng Ru,Pan Jilong. Proportioning optimization of cemented filling of Bayer red mud based on RSM-BBD[J]. Gold, 2022, 43(1): 34-41. DOI: 10.11792/hj20220106
Authors:Li Shuai  Yu Zheng  Feng Ru  Pan Jilong
Affiliation:(School of Resources and Safety Engineering,Central South University)
Abstract:Bayer red mud is difficult to utilize in large scale,in light of which a cemented filling aggregate made of Bayer red mud is developed.Based on the proportioning exploration test results,BBD method is used to design 9 groups of tests,and the response surface method is used to explore the relation between slurry concentration,mass ratio of Bayer red mud and C material,and the 3 response values of filling body compressive strength,slump and single consumption of C material,as well as the optimal proportion.The results show that the degree of satisfaction is 0.729,reaching the highest level,when the mass ratio of Bayer red mud and C material is 6.88 and the slurry concentration is 58.69%.At the time,the compressive strength of filling body after 28 d is 1.56 MPa,the slump is 29.5 cm,the single consumption of C material is 74.716 kg/t,meeting the actual requirement in mine production.The application of Bayer red mud to filling in goafs of mines not only eliminates the safety risks in goafs and ensures the actual requirement in mine production,but also realizes scaled-up utilization of Bayer red mud and protects surface environment.
Keywords:Bayer red mud  BBD test design  cemented filling  formula exploration  response surface optimization  proportioning optimization
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