Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network
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.
Effect of coal blending on the ash fusion temperature and the prediction model for the ash fusion temperature
Due to the high ash fusion temperature of the coal from the Liu-qiao No.2 coal mine in Wan-bei (A), this type coal could not meet shell coal gasification for slag-tapping. Coal blending technology used to reduce ash fusion temperature of coal A was studied. This can significantly reduce the high fusibility of the coal A to meet the requirements of the shell gasifier slag-tapping process. The prediction model was established according to the relationship between coal ash fusion temperature and chemical composition of ashes by linear least square method. Showed that increasing the content of MgO and CaO in coal blending ash could significantly decrease the ash fusion temperature, the higher content of SiO2 has on condition that the total content of SiO2 and Al2O3 in the ash coal blending is constant can further decrease the ash fusion temperature. At the same time, the model could predict the coal blending ash fusion temperature by the three type of coal well.
An Empirical Study of the Relation of Chemical Properties to Ash Fusion Temperatures
Regression analysis is used to develop equations for calculating fusion temperatures of coal ash from chemical composition, based on 1250 analyses of ash from both eastern and western US coal. Standard errors for the equations are generally less than 27/sup 0/C, which is the analytical tolerance of the ash fusion temperature measurements. Equations are given for eastern, western and combined eastern and western coals. These equations: (1) provide a technique for calculating ash fusion temperatures from the chemical composition of the ash; (2) provide a method for calculating the ash fusion properties of coal blends; and (3) provide an improved understanding of the effect, significance and interactions of ash elements with respect to the thermal properties of coal ash.