摘要
为缩短充填实验周期,尽快找到最优充填原料配比,以正交试验和单因素试验结果为BP神经网络的训练样本,以不同原料的灰中占比、灰砂比为输入参数,以充填料的料浆扩展度、7 d和28 d抗压强度为输出结果,建立BP神经网络模型预测钢尾渣-矿渣基充填料强度;在钢渣配比16.0%条件下,采用建立的模型优化制备充填体。结果表明:在隐含层神经元为14个时,料浆扩展度、7 d和28 d抗压强度最大相对误差分别为0.36%,1.46%,2.23%,样本外充填料的7 d和28 d抗压强度平均相对误差为2.86%和1.36%,样本内外的误差均较小,模型预测精度较高、泛化能力较强;对于采用模型优化制备的充填体,其28 d抗压强度的预测值为2.16 MPa、实测值为2.13 MPa,两者相差较小,BP神经网络可用于预测充填料的抗压强度,对优化充填原料配比、减少充填实验工作量具有指导意义。
In order to shorten the cycle of filling experiment and find the optimal raw material ratio as soon as possible,the results of orthogonal experiment and single factor experiment were used as the training samples of BP neural network,and the percentage of ash in the different raw materials and the ash-sand ratio were used as input parameters,and the slurry expansion,7 d and 28 d backfill strength of the filler were used as output results,the BP neural network model was establised to predict the strength of steel slag tailings-slag based filler.The established model was used to optimize the preparation of filling body under the condition of 16.0% steel tailings ratio.The results show that at 14 neurons in the hidden layer,the maximum relative errors of the flow and backfill strength in 7,28 d are 0.36%,1.46%,2.23% respectively,and the average relative errors of backfill strength of fillers out of samples in 7,28 d are 2.86%,1.36% respectively.The errors inside and outside the sample are small,the prediction accuracy of the model is high and the generalization ability is strong.For the filler prepared by modle optimization,the predicted value of 28 d backfill strength is 2.16 MPa,and the measured value is 2.13 MPa,and the difference is small.The BP neural network can be used to predict the backfill strength,which has guiding significance for optimizing the ratio of filling raw materials and reducing the workload of filling experiment.
作者
昌欢
朱子康
顾宝澍
代梦博
袁邦兴
林振林
春铁军
CHANG Huan;ZHU Zikang;GU Baoshu;DAI Mengbo;YUAN Bangxing;LIN Zhenlin;CHUN Tiejun(School of Metallurgical Engineering,Anhui University of Technology,Maanshan 243032,China)
出处
《安徽工业大学学报（自然科学版）》
CAS
2022年第3期256-261,267,共7页
Journal of Anhui University of Technology（Natural Science）
基金
安徽省自然科学基金项目(2008085ME175)
大学生创新创业训练计划项目(S202010360155,202010360010)。
关键词
钢尾渣
矿山充填
抗压强度
BP神经网络
steel slag tailings
mine filling
backfill strength
BP neural network
