The orthogonal test was used to optimize the reaction conditions of roasting zinc oxide ore with NaOH aiming to comprehensively utilize zinc oxide ore.The optimized reaction conditions were molar ratio of NaOH to zinc...The orthogonal test was used to optimize the reaction conditions of roasting zinc oxide ore with NaOH aiming to comprehensively utilize zinc oxide ore.The optimized reaction conditions were molar ratio of NaOH to zinc oxide ore 6:1,roasting temperature 450°C,holding time 150 min.The molar ratio of NaOH to zinc oxide ore was the most predominant factor affecting the extraction ratios of zinc oxide and silica.The mineral phase transformations were investigated by testing the phases of specimens obtained at different temperatures.The process was that silica reacted with molten NaOH to form Na_2SiO_3 at first,then transformed into Na_4SiO_4 with temperature rising.ZnCO_3 and its decomposing product ZnO reacted with NaOH to form Na_2ZnO_2.Na_2ZnSiO_4was also obtained.The reaction rate was investigated using unreacted shrinking core model.Two models used were chemical reaction at the particle surface and diffusion through the product layer.The results indicated that the reaction rate was combine-controlled by two models.The activation energy and frequency factor were obtained as 24.12 k J/mol and 0.0682,respectively.展开更多
Online gradient method has been widely used as a learning algorithm for training feedforward neural networks. Penalty is often introduced into the training procedure to improve the generalization performance and to de...Online gradient method has been widely used as a learning algorithm for training feedforward neural networks. Penalty is often introduced into the training procedure to improve the generalization performance and to decrease the magnitude of network weights. In this paper, some weight boundedness and deterministic con- vergence theorems are proved for the online gradient method with penalty for BP neural network with a hidden layer, assuming that the training samples are supplied with the network in a fixed order within each epoch. The monotonicity of the error function with penalty is also guaranteed in the training iteration. Simulation results for a 3-bits parity problem are presented to support our theoretical results.展开更多
基金Projects(51774070,51204054)supported by the National Natural Science Foundation of ChinaProject(N150204009)supported by the Ministry of Education Basic Scientific Research Business Expenses,ChinaProject(2007CB613603)supported by the National Basic Research Program of China
文摘The orthogonal test was used to optimize the reaction conditions of roasting zinc oxide ore with NaOH aiming to comprehensively utilize zinc oxide ore.The optimized reaction conditions were molar ratio of NaOH to zinc oxide ore 6:1,roasting temperature 450°C,holding time 150 min.The molar ratio of NaOH to zinc oxide ore was the most predominant factor affecting the extraction ratios of zinc oxide and silica.The mineral phase transformations were investigated by testing the phases of specimens obtained at different temperatures.The process was that silica reacted with molten NaOH to form Na_2SiO_3 at first,then transformed into Na_4SiO_4 with temperature rising.ZnCO_3 and its decomposing product ZnO reacted with NaOH to form Na_2ZnO_2.Na_2ZnSiO_4was also obtained.The reaction rate was investigated using unreacted shrinking core model.Two models used were chemical reaction at the particle surface and diffusion through the product layer.The results indicated that the reaction rate was combine-controlled by two models.The activation energy and frequency factor were obtained as 24.12 k J/mol and 0.0682,respectively.
基金The NSF (10871220) of Chinathe Doctoral Foundation (Y080820) of China University of Petroleum
文摘Online gradient method has been widely used as a learning algorithm for training feedforward neural networks. Penalty is often introduced into the training procedure to improve the generalization performance and to decrease the magnitude of network weights. In this paper, some weight boundedness and deterministic con- vergence theorems are proved for the online gradient method with penalty for BP neural network with a hidden layer, assuming that the training samples are supplied with the network in a fixed order within each epoch. The monotonicity of the error function with penalty is also guaranteed in the training iteration. Simulation results for a 3-bits parity problem are presented to support our theoretical results.
