基于布谷鸟算法与BP神经网络的煤灰变形温度预测

以120种煤样为数据基础,采用布谷鸟算法(CS)优化BP(Back Propagation)神经网络,建立了CSBP模型对单煤、煤掺添加剂和配煤等3类样本的煤灰变形温度(DT)样本进行预测。模型以煤灰化学成分及其组合参数等13个变量作为输入量,以变形温度(DT)作为输出量。CSBP模型预测结果与BP神经网络模型预测结果进行对比发现,无论是单煤、煤掺添加剂还是配煤,CSBP模型较BP模型对煤灰变形温度(DT)的预测都更加精准,平均相对误差分别达到了3.11%、4.08%和4.22%。另外,对比3类样本预测结果发现,无论是CSBP模型还是BP模型,相比单煤预测而言,煤掺添加剂及配煤的预测误差都有明显的增加。

Predictio n of coal ash de formation temperature based on Cuckoo Search and BP Neural Netwo rk

On the basis of 120 coal ash samples, a CSBP model based on BP(Back Propagation) Neural Network optimized by Cuckoo Search (CS) was proposed for predicting the ash deformation temperature of single coals, coals mixed with addictives and mixed coals. The thirteen chemical composition parameters and combined parameters were employed as inputs, and the ash deformation temperature was used as output of the CSBP model. The results show that whether single coal, coal mixed with additives or mixed coals, CSBP model has a better performance compared with BP model and the average relative errors are reduced to 3.11%, 4.08% and 4.22%, respectively. In addition, comparing the prediction results of three kinds of samples, both the CSBP model and BP model have higher prediction errors for coals mixed with addictives and mixed coals more than that for single coals.