Predictio n of coal ash de formation temperature based on Cuckoo Search and BP Neural Netwo rk
On the basi of 120 coal ash samples, a CSBP model basedo n BP ( Back Propaga tion) Neural Ne twork optimized by Cuckoo Search ( CS ) was proposde for predicting the ash deformation temperature of single coals, coals mixed witha ddictives 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 additivesor mixed cola s, CSBP modleahsa bettre perfor mance 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 haveh igher prediction errors for coals mixed with addicit ves and mixed coals more ht an that for single coals.
国家科技期刊平台
NSTL
万方数据
维普
