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.
Prediction of ash fusion temperature based on grid search and support vector machine
Support vector machine(SVM) was used to model the mixed coals' ash softening temperature.The coal ash compositions were employed as the inputs of SVM,and the measured ash softening temperatures were used as the outputs.Parameters of the SVM model were optimized by grid search and the best parameters were obtained for different setting precisions.Single and mixed coals' ash fusion temperatures were predicted by the optimized SVM and the predicted values were compared with the actual measurement results.The relative error of testing single coal was the smallest when the setting precision was 0.01.The results showed that the maximum predicting error was 3.00%,and the average predicting error was 0.48%.Using the 0.01 precision for the mixed coals,the maximum relative error was 1.74%,and the average relative error was 0.62%.So the SVM model optimized by grid search has good predicting performance of ash fusion temperature.
Heavy metal adsorption from industrial wastewater by PAMAM/TiO2 nanohybrid: Preparation, characterization and adsorption studies
Abstract This study investigates the removal of heavy metals from industrial wastewater using TiO 2 nanoparticles modified with poly-amidoamine dendrimer (PAMAM, G 4 ). The metal ions Cu 2 + , Pb 2 + , and Cd 2 + were selected as model pollutants. FTIR, SEM, BET surface area and Zeta potential measurements, and Dispersion Observing methods have been employed for characterizing the synthetic nanohybrid and these techniques indicated that the PAMAM loaded on to TiO 2 have been favorably synthesized. The effects of several parameters including initial metal ion concentration, temperature, pH solution, the nanohybrid dosage and contact time were studied. The experimental data were analyzed using equilibrium isotherm and kinetics models. The adsorption process was studied using isotherm, kinetics, and thermodynamics models. The analysis shows that the Langmuir isotherm and pseudo-second-order kinetics are appropriate models for the adsorption process. The thermodynamic parameters illustrate that the absorption is endothermic and spontaneous. The findings suggest that the nanohybrid is a good super absorbent for the removal of heavy metals from industrial effluents.