CAJ | 학술논문

根据义马中部井田瓦斯地质规律，选取埋深、开采强度、开采顺序和煤层厚度作为自变量，瓦斯涌出量为目标量，构建自变量矩阵和参考序列，进行瓦斯涌出量影响因素灰色关联度分析；由于各瓦斯地质影响因素与瓦斯涌出量的高度非线性关系，将各影响因素进行归一化处理，建立优化神经网络预测瓦斯涌出量数值模型，样本训练收敛速度快，误差在0.12%以内，并用此模型对耿村井田深部煤层瓦斯涌出量进行了预测。
근거의마중부정전와사지질규률，선취매심、개채강도、개채순서화매층후도작위자변량，와사용출량위목표량，구건자변량구진화삼고서렬，진행와사용출량영향인소회색관련도분석；유우각와사지질영향인소여와사용출량적고도비선성관계，장각영향인소진행귀일화처리，건립우화신경망락예측와사용출량수치모형，양본훈련수렴속도쾌，오차재0.12%이내，병용차모형대경촌정전심부매층와사용출량진행료예측。
An independent matrix and reference sequence were constituted according to the regularity of coal-bed gas geology in the middle of Yima coal field and by selecting the buried depth of coal seams, the mining intensity, mining sequence and the thickness of coal seam as the independent valuables and the gas emission as the objective variable, the grey correlation analysis on the influence factors of the gas emission was made. Since each influence factor of the coal-bed gas geology has a high nonlinear relation with gas emission, normalized treatment for the influence of each factor was made, a mathematical model based on the optimized neural network was set up for gas emission prediction, the convergence speed of the sample training of the model is fast, with the error less than 0. 12%,and this model was used for the gas emission prediction in deep coal seams in Gengcun coal field.