论文部分内容阅读
为增加粉尘防护设施设计的针对性,提高粉尘防护设施评价的可靠性,以煤巷综掘工作面为例,分析了煤巷综掘工作面原始粉尘浓度的影响因素,采用LMBP神经网络对煤巷综掘工作面的粉尘浓度进行预测。结果表明:预测值误差在±10%以内,预测结果可靠,为其他作业场所原始粉尘浓度预测及其他职业病危害因素浓度(强度)预测提供了一种方法;依据原始粉尘浓度预测值,可对粉尘防护设施进行系统设计,采用神经网络对职业病危害浓度(强度)进行预测,可使职业病防护设施设计更具有针对性。
In order to increase the pertinence of the design of dust protection facilities and improve the reliability of the evaluation of dust protection facilities, taking the coal heading face fully mechanized mining face as an example, the influencing factors of the original dust concentration of coal heading face are analyzed. LMBP neural network Prediction of dust concentration in fully mechanized coal mining face. The results show that the prediction error is within ± 10% and the prediction result is reliable, which provides a method for forecasting the original dust concentration in other workplaces and predicting the concentration (intensity) of other occupational hazards. Based on the predicted value of the original dust concentration, Protection system design, using neural network to predict the concentration (intensity) of occupational hazards, can make occupational disease protection facilities design more targeted.