Technology intensified with surface wettability was introduced to leach vanadium and chromium from converter vanadium slag without roasting. Parameters affecting the leaching efficiency of vanadium and chromium were i...Technology intensified with surface wettability was introduced to leach vanadium and chromium from converter vanadium slag without roasting. Parameters affecting the leaching efficiency of vanadium and chromium were investigated： sulfuric acid concentration, MnOz-to-slag mass ratio, liquid-to-solid ratio, leaching time, leaching temperature, and sodium dodecyl sulfate （SDS）-to-slag mass ratio. The leaching efficiencies of vanadium and chromium were 33.46 % and 20.02 % higher in the presence of MnO2 and SDS, respectively, compared to the control. The leaching efficiencies of vanadium and chromium were 68.93 % and 30.74 %, respectively, under the optimum conditions： sulfuric acid concentration 40 wt%, MnOz-to-slag mass ratio 10.0 wt%, liquid-to-solid ratio 5：1 mL/g; 12 h; 90 ~C; and SDS-to-slag mass ratio 0.25 wt%. The analysis of the reaction mechanism in the leaching process indicates that MnO2 combined with protons （H＋） could oxidize low-valent vanadium and chromium; SDS could change the chemical behavior and decrease the surface tension of the aqueous solution to favor MnO2 oxidization.展开更多
In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of ind...In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.展开更多
基金Project(2015BAB17B00)supported by the National Key Technology R&D Program of ChinaProject(CYB15045)supported by the Program for Chongqing University Postgraduates’ Innovation Project,China
文摘Technology intensified with surface wettability was introduced to leach vanadium and chromium from converter vanadium slag without roasting. Parameters affecting the leaching efficiency of vanadium and chromium were investigated： sulfuric acid concentration, MnOz-to-slag mass ratio, liquid-to-solid ratio, leaching time, leaching temperature, and sodium dodecyl sulfate （SDS）-to-slag mass ratio. The leaching efficiencies of vanadium and chromium were 33.46 % and 20.02 % higher in the presence of MnO2 and SDS, respectively, compared to the control. The leaching efficiencies of vanadium and chromium were 68.93 % and 30.74 %, respectively, under the optimum conditions： sulfuric acid concentration 40 wt%, MnOz-to-slag mass ratio 10.0 wt%, liquid-to-solid ratio 5：1 mL/g; 12 h; 90 ~C; and SDS-to-slag mass ratio 0.25 wt%. The analysis of the reaction mechanism in the leaching process indicates that MnO2 combined with protons （H＋） could oxidize low-valent vanadium and chromium; SDS could change the chemical behavior and decrease the surface tension of the aqueous solution to favor MnO2 oxidization.
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
