Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network

Department of Chemical and Biochemical Engineering, National Engineering Laboratory for Green Chemical Productions of Alcohols−Ethers−Esters, College of Chemistry & Chemical Engineering, Xiamen University, Xiamen, China

Abstract

Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT) of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST) in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO) were used as inputs and ST was used as output of the model.