Coal Washing Branch of CHN Energy Baotou Energy Co. , Ltd.
China Coal Technology and Engineering Group Beijing Huayu Engineering Co. , Ltd.
Ordos Energy Bureau

由于煤质的波动,配煤是一个在不确定条件下的优化问题。针对目前铁路快速定量装车系统给煤模式单一,配煤精度低,配煤速度慢的问题,提出了一种基于BP神经网络的快速定量配煤方法,通过构建BP神经网络结构、选择合适的激活函数,得到优化的配煤模型。研究结果表明,该方法在加快配煤速度的同时提高了配煤精度,有效提高了矿山企业装快速定量装车系统的工作效率。

Due to fluctuations in coal quality, coal blending is an optimization problem under uncertain conditions. A fast quantitative coal blending method based on BP neural network is proposed to address the problems of single coal feeding mode, low coal blending accuracy, and slow coal blending speed in the current railway rapid quantitative loading system. By constructing a BP neural network structure and selecting appropriate activation functions, an optimized coal blending model is obtained. The research results indicate that this method not only accelerates the coal blending speed but also improves the coal blending accuracy, which effectively improves the work efficiency of the rapid quantitative loading system for mining enterprises.

李家壕煤矿智能选煤厂关键技术研究与示范工程(GJNY-20-237-1)

熊树宝, 李 季, 石建光, 等. 基于 BP 神经网络的智能配煤方法研究 [J]. 煤炭工程, 2023, 55(10): 162-166.