Austenite can be retained at ambient temperature in steels by alloying and processing control. The transformation from austenite to martensite occurs under a certain conditions : thermal or deformation. Stress-strain ...Austenite can be retained at ambient temperature in steels by alloying and processing control. The transformation from austenite to martensite occurs under a certain conditions : thermal or deformation. Stress-strain induced martensitic transformation is important to improve the plasticity of steels which is called transformation induced plasticity (TRIP). Strength-ductility balance of the steels is greatly superior to that of other high strength steels due to the TRIP effect. A new type of steels-TRIP steel is developed展开更多
An orthogonal test was used to optimize the reaction conditions of roasting zinc oxide ore using（NH_4）_2SO_4. The optimized reaction conditions are defined as an（NH_4）_2SO_4/zinc molar ratio of 1.4：1, a roasting ...An orthogonal test was used to optimize the reaction conditions of roasting zinc oxide ore using（NH_4）_2SO_4. The optimized reaction conditions are defined as an（NH_4）_2SO_4/zinc molar ratio of 1.4：1, a roasting temperature of 440°C, and a thermostatic time of 60 min. The molar ratio of（NH_4）_2SO_4/zinc is the most predominant factor and the roasting temperature is the second significant factor that governs the zinc extraction. Thermogravimetric-differential thermal analysis was used for（NH_4）_2SO_4 and zinc mixed in a molar ratio of 1.4：1 at the heating rates of 5, 10, 15, and 20 K·min-1. Two strong endothermic peaks indicate that the complex chemical reactions occur at approximately 290°C and 400°C. XRD analysis was employed to examine the transformations of mineral phases during roasting process. Kinetic parameters, including reaction apparent activation energy, reaction order, and frequency factor, were calculated by the Doyle-Ozawa and Kissinger methods. Corresponding to the two endothermic peaks, the kinetic equations were obtained.展开更多
Dissolved gas analysis (DGA) occupies an extremely important position in transformer condition assessment, and many conventional methods have been proposed based on dissolved gas in oil analysis. In this paper, a comb...Dissolved gas analysis (DGA) occupies an extremely important position in transformer condition assessment, and many conventional methods have been proposed based on dissolved gas in oil analysis. In this paper, a combined deep neural network (CD NN) is proposed to combine the characteristics of dissolved gas in oil and conventional methods for transformer condition assessment. First, the sample data are normalized according to the characteristics of the conventional method parameters. Then, the normalized parameters are used as input parameters of the deep neural network. Multiple deep neural network models are built separately. Next, the prediction results of multiple deep neural network models are weighted according to the accuracy of different models. Finally, the one with the largest weight is selected as the final prediction result of the combined deep neural network. In this paper, we show the necessity of data normalization through data statistics and demonstrations. The comparison between setting up the normal state data and not considering the normal state data proves that the normal state is easy to misclassify with other states when predicting, which leads to a decrease in the prediction accuracy. By comparing with the composition method of this paper and the classification method commonly used in MATLAB software, it is confirmed that the method in this paper combines the advantages of other methods and corrects the prediction results, thus having higher accuracy.展开更多
文摘Austenite can be retained at ambient temperature in steels by alloying and processing control. The transformation from austenite to martensite occurs under a certain conditions : thermal or deformation. Stress-strain induced martensitic transformation is important to improve the plasticity of steels which is called transformation induced plasticity (TRIP). Strength-ductility balance of the steels is greatly superior to that of other high strength steels due to the TRIP effect. A new type of steels-TRIP steel is developed
基金financially supported by the National Natural Science Foundation of China(Nos.51204054 and 51574084)the Fundamental Research Funds for the Central Universities of China(No.N150204009)the National Basic Research Priorities Program of China(No.2014CB643405)
文摘An orthogonal test was used to optimize the reaction conditions of roasting zinc oxide ore using（NH_4）_2SO_4. The optimized reaction conditions are defined as an（NH_4）_2SO_4/zinc molar ratio of 1.4：1, a roasting temperature of 440°C, and a thermostatic time of 60 min. The molar ratio of（NH_4）_2SO_4/zinc is the most predominant factor and the roasting temperature is the second significant factor that governs the zinc extraction. Thermogravimetric-differential thermal analysis was used for（NH_4）_2SO_4 and zinc mixed in a molar ratio of 1.4：1 at the heating rates of 5, 10, 15, and 20 K·min-1. Two strong endothermic peaks indicate that the complex chemical reactions occur at approximately 290°C and 400°C. XRD analysis was employed to examine the transformations of mineral phases during roasting process. Kinetic parameters, including reaction apparent activation energy, reaction order, and frequency factor, were calculated by the Doyle-Ozawa and Kissinger methods. Corresponding to the two endothermic peaks, the kinetic equations were obtained.
基金supported by the Science and Technology Project from Headquarters of State Grid Corporation of China(No.52060020002P)。
文摘Dissolved gas analysis (DGA) occupies an extremely important position in transformer condition assessment, and many conventional methods have been proposed based on dissolved gas in oil analysis. In this paper, a combined deep neural network (CD NN) is proposed to combine the characteristics of dissolved gas in oil and conventional methods for transformer condition assessment. First, the sample data are normalized according to the characteristics of the conventional method parameters. Then, the normalized parameters are used as input parameters of the deep neural network. Multiple deep neural network models are built separately. Next, the prediction results of multiple deep neural network models are weighted according to the accuracy of different models. Finally, the one with the largest weight is selected as the final prediction result of the combined deep neural network. In this paper, we show the necessity of data normalization through data statistics and demonstrations. The comparison between setting up the normal state data and not considering the normal state data proves that the normal state is easy to misclassify with other states when predicting, which leads to a decrease in the prediction accuracy. By comparing with the composition method of this paper and the classification method commonly used in MATLAB software, it is confirmed that the method in this paper combines the advantages of other methods and corrects the prediction results, thus having higher accuracy.
