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[1]张睿冲,谢承煜.基于GPR-TOPSIS方法的充填配比多目标多属性优化[J].中国安全生产科学技术,2019,15(3):55-61.[doi:10.11731/j.issn.1673-193x.2019.03.009]
 ZHANG Ruichong,XIE Chengyu.Multiobjective and multiattribute optimization of filling proportion based on GPR-TOPSIS method[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(3):55-61.[doi:10.11731/j.issn.1673-193x.2019.03.009]
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基于GPR-TOPSIS方法的充填配比多目标多属性优化
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
15
期数:
2019年3期
页码:
55-61
栏目:
职业安全卫生管理与技术
出版日期:
2019-03-31

文章信息/Info

Title:
Multiobjective and multiattribute optimization of filling proportion based on GPR-TOPSIS method
文章编号:
1673-193X(2019)-03-0055-07
作者:
张睿冲12谢承煜3
(1.中南大学 资源与安全工程学院,湖南 长沙 410083;2.广西大学 资源环境与材料学院,广西 南宁 530004;3.湘潭大学 环境与资源学院,湖南 湘潭 411105)
Author(s):
ZHANG Ruichong12XIE Chengyu3
(1. School of Resources and Safety Engineering, Central South University, Changsha Hunan 410083, China;2. School of Resources, Environment and Materials, GuangXi University, Nanning Guangxi 530004, China;3. College of Environment and Resources, Xiangtan University, Xiangtan Hunan 411105, China)
关键词:
胶结充填高斯过程回归TOPSIS法多目标多属性优化
Keywords:
cemented filling Gaussian process regression technique for order preference by similarity to an ideal solution (TOPSIS) multiobjective and multiattribute optimization
分类号:
X936;TD853
DOI:
10.11731/j.issn.1673-193x.2019.03.009
文献标志码:
A
摘要:
为获得某金矿尾砂胶结充填材料最优配比,基于试验结果,以海水比例、灰砂比和料浆质量浓度为输入参数,以充填体强度、塌落度及泌水率为输出参数,建立了充填配比与其响应量的高斯过程回归模型,分析了不同因素对充填性能的影响程度;采用遗传算法对高斯过程回归模型进行多目标参数优化,获得了Pareto非劣解,在此基础上,引入多属性决策的TOPSIS法对Pareto非劣解进行方案优选,确定了充填最优配比。研究结果表明:高斯过程回归模型相对误差值均小于6%,可靠性高;灰砂比及料浆质量浓度对充填性能影响较为显著,采用海水作为充填水源将降低充填体的强度;经优化后的充填配比与试验结果相符。
Abstract:
To obtain the optimum proportion of the tailings cemented filling material of a gold mine, through taking the seawater ratio, the cementsand ratio and the mass concentration of slurry as the input parameters, and taking the strength of filling body, the slump and the bleeding rate as the output parameters, a Gaussian process regression model of filling proportion and its responses was established based on the test results, and the influence of different factors on the filling performance was analyzed. The multiobjective parameters optimization of the Gaussian process regression model was carried out by using the genetic algorithm, and the Pareto noninferior solutions were obtained. On this basis, the optimal selection of schemes on the Pareto noninferior solutions was conducted by introducing into the TOPSIS method of multiattribute decision making, and the optimum filling proportion was obtained. The results showed that all the relative errors of the Gaussian process regression model were less than 6%, with a high reliability. The cementsand ratio and the mass concentration of slurry had more significant influence on the filling performance, and the strength of filling body reduced when taking the seawater as the filling water source. The filling proportion after optimization was consistent with the test results.

参考文献/References:

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备注/Memo

备注/Memo:

收稿日期: 2018-10-09
基金项目: 湖南省自然科学基金青年基金项目(2018JJ3510)
作者简介: 张睿冲,博士研究生,讲师,主要研究方向为金属矿体开采岩层移动规律与控制技术。
更新日期/Last Update: 2019-04-15