Reduction roasting-acid leaching process was utilized to process high-iron-content manganese oxide ore using black charcoal as reductant. The results indicate that, compared with the traditional reductant of anthracit...Reduction roasting-acid leaching process was utilized to process high-iron-content manganese oxide ore using black charcoal as reductant. The results indicate that, compared with the traditional reductant of anthracite, higher manganese extraction efficiency is achieved at lower roasting temperature and shorter residence time. The effects of roasting parameters on the leaching efficiency of Mn and Fe were studied, and the optimal parameters are determined as follows: roasting temperature is 650 °C, residence time is 40 min, and black charcoal dosage is 10%(mass fraction). Under these conditions, the leaching efficiency of Mn reaches 82.37% while that of Fe is controlled below 7%. XRD results show that a majority of MnO2 and Fe2O3 in the raw ore are reduced to MnO and Fe3O4, respectively.展开更多
We investigate the thermoelectric energy conversion efficiency of Si and Ge nanowires, and in particular, that of Si/Ge core-shell nanowires. We show how the presence of a thin Ge shell on a Si core nanowire increases...We investigate the thermoelectric energy conversion efficiency of Si and Ge nanowires, and in particular, that of Si/Ge core-shell nanowires. We show how the presence of a thin Ge shell on a Si core nanowire increases the overall figure of merit. We find the optimal thickness of the Ge shell to provide the largest figure of merit for the devices. We also consider Ge core/Si shell nanowires, and show that an optimal thickness of the Si shell does not exist, since the figure of merit is a monotonically decreasing function of the radius of the nanowire. Finally, we verify the empirical law relating the electron energy gap to the optimal working temperature that maximizes the efficiency of the device.展开更多
The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However...The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.展开更多
基金Project(2013JSJJ028)supported by the Teachers’Research Fund of Central South University,ChinaProject supported by Co-Innovation Center for Clean and Efficient Utilization of Strategic Mineral Resources,China
文摘Reduction roasting-acid leaching process was utilized to process high-iron-content manganese oxide ore using black charcoal as reductant. The results indicate that, compared with the traditional reductant of anthracite, higher manganese extraction efficiency is achieved at lower roasting temperature and shorter residence time. The effects of roasting parameters on the leaching efficiency of Mn and Fe were studied, and the optimal parameters are determined as follows: roasting temperature is 650 °C, residence time is 40 min, and black charcoal dosage is 10%(mass fraction). Under these conditions, the leaching efficiency of Mn reaches 82.37% while that of Fe is controlled below 7%. XRD results show that a majority of MnO2 and Fe2O3 in the raw ore are reduced to MnO and Fe3O4, respectively.
文摘We investigate the thermoelectric energy conversion efficiency of Si and Ge nanowires, and in particular, that of Si/Ge core-shell nanowires. We show how the presence of a thin Ge shell on a Si core nanowire increases the overall figure of merit. We find the optimal thickness of the Ge shell to provide the largest figure of merit for the devices. We also consider Ge core/Si shell nanowires, and show that an optimal thickness of the Si shell does not exist, since the figure of merit is a monotonically decreasing function of the radius of the nanowire. Finally, we verify the empirical law relating the electron energy gap to the optimal working temperature that maximizes the efficiency of the device.
基金supported by the National Natural Science Foundation of China(Grant No.51176030)Jiangsu Science and Technology Department(Grant No.BY2015070-17)
文摘The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.
