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.
Employing support vector machine to predict the ash fusion temperature of coal blends
In order to improve the accuracy of the ash fusion temperature prediction,support vector machine and genetic algorithms were combined to model the ash fusion temperature of coal blends.The chemical compositions of the coal ash were employed as inputs and the ash fusion temperature as output.The results show that the maximum and average relative predicting error are 7.4 % and 0.678 %,respectively.Support vector machine can find its application in the small sample training,its calculating speed is fast which can improve the real-time processing performance.
Numerical Simulation of Induction Heating Process of Continuous Casting Slab
Compared with traditional blazing furnace, the Continuous Casting-Direct Rolling is an advanced manufacturing steel technology, which can reduce energy waste, decrease pollution and enhance efficiency. The characteristics of steels during induction heating are complex, the change of material properties with temperature makes exact analysis methods very difficult to implement. Therefore, a powerful computer aided numerical tool (i.e., finite difference analysis) is selected to numerically model the induction heating process in this paper. The mathematic model coupling with electromagnetic field and thermal field was established, and it was solved by finite difference method (FDM), thus the slab temperature distribution and its variation with time were obtained, and the characteristics in whole induction heating process were studied. To validate the program feasible, the results were evaluated and compared with experiment results, which showed that the simulation results are reliable and effective. The skin effect in heating process from the two results was studied and demonstrated, the temperature change caused by different parameters such as the induced power intensity and the corner radian were also presented, which indicate that the slab temperature can be heated uniformly through adjusting these parameters, thus the continuous casting slab can meet the rolling requirement.