基于神经网络与遗传算法的多目标充填料浆配比优化

doi:10.11872/j.issn.1005-2518.2019.04.581

摘要/Abstract

摘要：

随着充填法在地下矿山开采中的应用越来越广，在满足充填体强度要求的情况下，寻找生产成本最低的充填料浆配比对于矿山生产经营十分重要。基于人工神经网络和遗传算法提出了一种新的充填料浆配比优化方法。首先，以水泥质量分数、粉煤灰质量分数和尾砂质量分数3个充填料浆配比参数为优化参数，以充填体强度为优化目标，建立了3-9-1的BP神经网络，并基于遗传算法对BP神经网络进行优化，建立起预测精度更高的GA_BP神经网络。然后，将预测精度更高的GA_BP神经网络作为适应度函数，结合成本计算函数，通过遗传算法进行多目标优化以获取最优的充填料浆配比参数。结果表明：当充填体抗压强度为1.5 MPa时的成本最低，充填料浆配比组合为水泥质量分数为8%，粉煤灰质量分数为2.3%，尾砂质量分数为66.3%，最低成本为29.3元/t，优化结果与实际情况一致。

关键词: 充填体抗压强度, 充填法, 充填料浆配比优化, GA_BP神经网络, 遗传算法, 多目标优化

Abstract:

As the mining depth continues to increase, the pressure management of the stope and goaf is becoming more and more difficult.In order to maintain the stability of the stope,ensure the safety of the operation and prevent the collapse of the goaf,the filling method has become the preferred method for underground mining and has been widely applied.The filling method is to fill the filling body in the goaf to form a filling body with a certain compressive strength, and then carry out underground management by the supporting action of the filling body.Therefore, in the process of mining using the filling method, it is the key to efficient and safe mining of the mine to prepare the filling slurry with reasonable ratio and considerable economy to ensure that the compressive strength of the filling body can meet the needs of ground pressure management.However, there is no simple linear mapping between the filling ratio and the compressive strength of the filling body, and it is usually difficult to calculate by general mathematical methods.To this end, many researchers have conducted a lot of research on the ratio of filling slurry and the optimization of compressive strength of the filling body.Most of these studies only consider optimization under single-objective conditions, and there are few studies on multi-objective conditions.Therefore, other methods should be found to carry out multi-objective optimization research. In this paper, based on artificial neural network and genetic algorithm, a new optimization method of filling ratio is proposed.Firstly, the parameters of cement filling ratio, fly ash mass fraction and tailings mass fraction were optimized parameters, and the backfill strength was used as the optimization target to establish a BP neural network of 3-9-1.The BP neural network is optimized based on genetic algorithm, and the GA_BP neural network with higher prediction accuracy was established.Then,the GA_BP neural network with higher prediction accuracy is used as the fitness function, combined with the cost calculation function, and multi-objective optimization is performed by genetic algorithm to obtain the optimal filling slurry ratio parameter.The results show that when the compressive strength of the filling body is 1.5 MPa, the cement mass fraction is 8%, the fly ash mass fraction is 2.3%, the tailings mass fraction is 66.3%, the cost is the lowest, and the lowest cost is 29.3 yuan/t.The optimization results are consistent with the actual situation.

Key words: filling body compressive strength, filling method, filling slurry ratio optimization, GA_BP neural network, genetic algorithm, multi-objective optimization

中图分类号:

TD35

肖文丰,陈建宏,陈毅,王喜梅. 基于神经网络与遗传算法的多目标充填料浆配比优化[J]. 黄金科学技术, 2019, 27(4): 581-588.

Wenfeng XIAO,Jianhong CHEN,Yi CHEN,Ximei WANG. Optimization of Multi-objective Filling Slurry Ratio Based on Neural Network and Genetic Algorithm[J]. Gold Science and Technology, 2019, 27(4): 581-588.

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参数	序号
参数	1	2	3	4	5	6	7	8	9	10	11	12
ω₁	15.20	14.80	14.00	10.86	10.00	8.44	8.22	7.78	6.91	6.73	4.69	4.38
ω₂	0	0	0	0	0	0	0	0	0	0	0	0
ω₃	60.80	59.20	56.00	65.14	60.00	67.56	65.78	62.22	69.09	67.27	70.31	67.50
抗压强度/MPa	3.67	3.64	2.89	2.43	1.64	1.59	1.54	1.48	1.14	0.84	0.54	0.43
参数	序号
参数	13	14	15	16	17	18	19	20	21	22	23	24
ω₁	4.38	7.78	8.44	8.67	6.36	6.91	3.50	3.80	10.57	6.36	7.09	3.90
ω₂	0	15.56	16.89	17.73	12.73	13.82	14.00	15.20	0	0	14.18	15.60
ω₃	65.63	46.67	50.67	52.00	50.91	55.27	52.50	57.00	63.43	63.64	56.73	58.50
抗压强度/MPa	0.43	0.43	2.31	2.21	1.49	1.67	0.53	0.67	2.28	0.90	1.85	0.74

参数	序号
参数	1	2	3	4	5	6	7	8	9	10	11	12
ω₁	1	0.965	0.897	0.629	0.555	0.422	0.403	0.365	0.291	0.276	0.101	0.085
ω₂	0	0	0	0	0	0	0	0	0	0	0	0
ω₃	0.597	0.530	0.394	0.781	0.563	0.883	0.808	0.657	0.948	0.871	1	0.881
抗压强度/MPa	3.67	3.64	2.89	2.43	1.64	1.59	1.54	1.48	1.14	0.84	0.54	0.43
参数	序号
参数	13	14	15	16	17	18	19	20	21	22	23	24
ω₁	0.075	0.365	0.422	0.441	0.244	0.291	0	0.025	0.604	0.244	0.306	0.034
ω₂	0	0.897	0.974	1	0.734	0.797	0.807	0.877	0	0	0.818	0.900
ω₃	0.802	0	0.169	0.225	0.179	0.363	0.246	0.437	0.709	0.717	0.425	0.500
抗压强度/MPa	0.43	1.85	2.13	2.21	1.49	1.67	0.53	0.67	2.28	0.9	1.85	0.74

网络结构	误差	迭代次数	网络结构	误差	迭代次数
3-4-1	0.2838	159	3-9-1	0.1768	91
3-5-1	0.2298	87	3-10-1	0.1791	120
3-6-1	0.2238	79	3-11-1	0.1959	94
3-8-1	0.1961	123	3-12-1	0.2022	48

参数	数量/个
输入层与隐含层连接权值数量	27
隐含层阈值数量	9
隐含层与输出层连接权值数量	9
输出层阈值数量	1

试验序号	期望输出	GA_BP模型		BP模型
试验序号	期望输出	实际输出	相对误差/%	实际输出	相对误差/%
22	0.90	0.9005	0.05	0.9611	6.79
23	1.85	1.8492	0.08	1.7492	5.45
24	0.74	0.7397	0.04	0.7824	5.73