Prediction of coal ash deformation temperature based on Cuckoo Search and BP Neural Network

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.