【摘要】 在选矿工艺中,碎磨流程是关键的物料准备阶段。针对传统碎磨流程数学模型建模方法中未考虑设备本征参数问题,在破碎阶段采用技术成熟的矩阵模型,同时利用神经网络的多特征融合能力,对磨矿流程中的设备结构特征和给料粒级分布进行处理,建立神经网络-矩阵混合模型(Back Propagation-Matrix Model,BP-MM)。以碎磨制样短流程为例,基于破碎实验数据构建颚式破碎机、对辊破碎机的矩阵模型,利用神经网络方法构建融合磨盘间隙参数的盘式碎磨机BP神经网络模型,搭建碎磨制样短流程的BP-MM混合模型。以平均绝对误差、均方根误差和决定系数为评价指标,将BP-MM混合模型的预测结果与JKSimmet仿真结果进行对比。结果表明,BP-MM混合模型预测误差控制在3%以内,当磨盘间隙为0.1 mm时,碎磨制样短流程磨矿产品粒度≤0.15 mm。BP-MM混合模型建模方法可处理多特征与多参数融合的输入数据,有效提高建模精度和预测性能,为碎磨流程控制优化提供新思路。更多还原

【Abstract】 In mineral beneficiation,the crushing and grinding process is the key material preparation stage. Aiming at the problem that the intrinsic parameters of the equipment are not considered in the mathematical model modeling method of the traditional crushing and grinding process,the mature matrix model is adopted in the crushing stage. Meanwhile,the multi-feature fusion ability of neural networks is utilized to process the structural characteristics of the equipment and the distribution of feed particle size in the grinding process,and the Back Propagation-Matrix Model( BP-MM) is established. Taking the short process of crushing and grinding sample preparation as an example,the matrix model of jaw crusher and roll crusher is constructed based on the crushing experimental data. The BP neural network model of disc mill with the gap parameters of grinding disc is constructed by neural network method,and the BP-MM hybrid model of short process of crushing and grinding sample preparation is constructed. Taking the mean absolute error,root mean square error and determination coefficient as evaluation indicators,the prediction results of the BP-MM hybrid model were compared with the simulation results of JKSimmet. The results demonstrated that the BP-MM hybrid model achieved prediction errors below 3%. When the gap of the grinding disc is 0. 1 mm,the particle size of the grinding product in the short process of crushing and grinding sample preparation is ≤0. 15 mm. The BP-MM hybrid model modeling method can handle the input of multi-feature and multi-parameter fusion,effectively improve the modeling accuracy and prediction performance,and provide new ideas for the control optimization of crushing and grinding process.更多还原