采煤机自适应截割控制方案的设计与应用

Design and Application of Adaptive Cutting Control Scheme for Shearer

本文以采煤机对夹矸煤层开采时的自适应调整控制为研究对象,根据夹矸参数的两种情形实现自适应截割优化控制,控制时以滚筒转速和牵引速度为优化对象;利用传感器采集采煤机截割滚筒的转矩信号,并利用Elman神经网络模型预测夹矸参数,通过实践测试发现,该模型具有较高的预测精度,预测误差可控制在10%范围内。将截割控制方案应用到7LS-06(LWS579)型采煤机工程实践中,经现场调试应用发现达到了预期效果,并且采煤机的采煤效率和故障率分别提升了5%和降低了24%。

This paper taking the adaptive adjustment control of the shearer to the mining of the gangue seam as the research object,the adaptive cutting optimization control is realized in two cases according to the parameters of the gangue seam,and the drum speed and traction speed are the optimization objects during the control;The sensor is used to collect the torque signal of the shearer′s cutting drum,and the Elman neural network model is used to predict the gangue parameters.Through practical tests,it is found that the model has high prediction accuracy,and the prediction error can be controlled within 10%.The cutting control scheme was applied to the engineering practice of 7LS-06(LWS579)shearer,and it was found that the expected effect was achieved through field debugging and application,and the mining efficiency and failure rate of the shearer increased by 5%and decreased by 24%respectively.