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
Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images. We propose,a novel feature selection and classification method for hy...Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images. We propose,a novel feature selection and classification method for hyperspectral images by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM). Global optimal search performance of PSO is improved by using a chaotic optimization search technique. Granularity based grid search strategy is used to optimize the SVM model parameters. Parameter optimization and classification of the SVM are addressed using the training date corre-sponding to the feature subset. A false classification rate is adopted as a fitness function. Tests of feature selection and classification are carried out on a hyperspectral data set. Classification performances are also compared among different feature extraction methods commonly used today. Results indicate that this hybrid method has a higher classification accuracy and can effectively extract optimal bands. A feasible approach is provided for feature selection and classifica-tion of hyperspectral image data.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.
基金Project 40401038 supported by the National Natural Science Foundation of China
文摘Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images. We propose,a novel feature selection and classification method for hyperspectral images by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM). Global optimal search performance of PSO is improved by using a chaotic optimization search technique. Granularity based grid search strategy is used to optimize the SVM model parameters. Parameter optimization and classification of the SVM are addressed using the training date corre-sponding to the feature subset. A false classification rate is adopted as a fitness function. Tests of feature selection and classification are carried out on a hyperspectral data set. Classification performances are also compared among different feature extraction methods commonly used today. Results indicate that this hybrid method has a higher classification accuracy and can effectively extract optimal bands. A feasible approach is provided for feature selection and classifica-tion of hyperspectral image data.
