Optimization Model and Pollution Treatment of Sintering Ore Distribution

sintering process plays an important role in iron and steel smelting process. The subsequent production of blast furnace ironmaking is directly affected by the quality of sinter. Among them, the proportion of raw materials and the advanced degree of sintering process are the two main factors affecting the quality of sinter. Because the control parameters of sintering process are too many and the physical and chemical process is too complex, it is difficult to establish and control the model accurately. Therefore, workers have long relied on experience to set temperature and other factors to engage in production, resulting in the quality of sinter is unstable, the cost is not easy to be controlled. Moreover, the flue gas produced in the sintering process will have different effects on the environment. Through the data analysis of the ore distribution scheme and the results of the physicochemical analysis of sinter in a steel plant, two aspects of the work are completed: one is to establish the optimal model of the cost of the sintering process, and the most suitable temperature for the sintering process. The second is the analysis of harmful components produced in sintering process.

DroneBorne Hyperspectral and Magnetic Data Integration: Otanmäki FeTiV Deposit in Finland

The technical evolution of unmanned aerial systems (UAS) for mineral exploration advances rapidly. Recent sensor developments and improved UAS performance open new fields for research and applications in geological and geophysical exploration among others. In this study, we introduce an integrated acquisition and processing strategy for droneborne multisensor surveys combining optical remote sensing and magnetic data. We deploy both fixedwing and multicopter UAS to characterize an outcrop of the Otanmäki FeTiV deposit in central Finland. The lithology consists mainly of gabbro intrusions hosting ore bodies of magnetiteilmenite. Large areas of the outcrop are covered by lichen and low vegetation. We use two droneborne multi and hyperspectral cameras operating in the visible to nearinfrared parts of the electromagnetic spectrum to identify dominant geological features and the extents of ore bodies via ironindicating proxy minerals. We apply band ratios and unsupervised and supervised image classifications on the spectral data, from which we can map surficial ironbearing zones. We use two setups with threeaxis fluxgate magnetometers deployed both by a fixedwing and a multicopter UAS to measure the magnetic field at various flight altitudes (15 m, 40 m, 65 m). The total magnetic intensity (TMI) computed from the individual components is used for further interpretation of ore distribution. We compare to traditional magnetic groundbased survey data to evaluate the UASbased results. The measured anomalies and spectral data are validated and assigned to the outcropping geology and ore mineralization by performing surface spectroscopy, portable Xray fluorescence (pXRF), magnetic susceptibility, and traditional geologic mapping. Locations of mineral zones and magnetic anomalies correlate with the established geologic map. The integrated survey strategy allowed a straightforward mapping of ore occurrences. We highlight the efficiency, spatial resolution, and reliability of UAS surveys. Acquisition time of magnetic UAS surveying surpassed ground surveying by a factor of 20 with a comparable resolution. The proposed workflow possibly facilitates surveying, particularly in areas with complicated terrain and of limited accessibility, but highlights the remaining challenges in UAS mapping.

Aeromagnetic and spectral expressions of rare earth element deposits in Gallinas Mountains area, Central New Mexico, USA

The Gallinas Mountains, located at the junction of Lincoln and Torrance Counties, New Mexico, USA, are a series of alkaline volcanic rocks intruded into Permian sedimentary rocks. The Gallinas Mountains area hosts fluorite and copper as veins containing bastnäsite, whereas deposits of iron skarns and iron replacement are in the area as well. These deposits produce iron. In this study, the multispectral band-ratio method is used for surface mineral recognition, whereas 2D subsurface structure inversion modeling was applied to explore the depth extent of the magnetic ore distribution from aeromagnetic data. Bastnäsite has higher magnetic susceptibility (0.009 SI) than the host rocks and surrounding sedimentary rock. The bastnäsite and iron oxides (magnetite + hematite) can contribute to a positive aeromagnetic anomaly. Results indicate that (1) the positive magnetic anomaly observed at Gallinas Mountains area can be accounted for by a mixture of bastnäsite and iron oxides at a depth of approximately 400 m and a thickness of approximately 13–15 m. The surface of this area is dominated by the hydrothermal alteration associated with iron oxides over the trachyte intrusions as detected by Landsat 8 band-ratio imaging.

Numerical estimation of fracture network permeability based on GEOFRAC model for groundwater modeling in a tin mine

Numerous geological research studies and mining operations have proved that fracture is one of the important factors controlling groundwater flow, mineralization, and ore distribution in metallic deposits. Most current approaches to groundwater flow simulation of naturally fractured media rely on the calculation of equivalent permeability tensors from a discrete fracture network (DFN). This study is aimed at developing a rational two-dimensional DFN by GEOFRAC, a geostatistical method of fracture direction and locations of sample data from a tin mine in the Gaosong area, Gejiu city, southwest China, and utilizing 3,724 outcrop fractures sampled on the ground of mountain Gaosong. Principal inputs of the DFN are density, direction, and continuity of disks that constitute a fracture plane. Fractures simulated by GEOFRAC were validated in that their directions corresponded well with those of the sample fractures. The permeability tensor of each modeling grid was then calculated based on the fracture network constructed. The results showed that GEOFRAC is valuable for two-dimensional DFN modeling in mines and other fracture-controlled geological phenomena, such as groundwater flow and slope failure.

MODELO MATEMÁTICO APLICADO A MOLIENDA DISCONTINUA BATCH

El modelo matemático aplicado a molienda batch (a nivel laboratorio), es una herramienta muy importante para llegar a simular y luego predecir el producto granulométrico de cierto mineral tratado; esto es por cada cierto intervalo de tiempo transcurrido de molienda, obtener su respectivo análisis granulométrico reflejado a nivel laboratorio, que luego será corroborado con datos reales y además poder verificar su buena aproximación. El modelo involucra a las funciones selección y fractura quienes son constituidos por los tamaños granulométricos de cada malla en la referida distribución mineral. Consiguiendo de esta manera predecir en forma efectiva y confiable, evitando costo y sobre todo tiempo, sin la necesidad de realizar el proceso de molienda para cada predicción de molienda batch. Palabras clave.- molienda, batch, simular, análisis granulométrico, fractura, mineral. ABSTRACT Mathematical modelling applied to batch milling (at experimental level), is a very important tool to simulate and predict the granulometric yield of a given treated ore; this means, for a given duration of milling operation, to obtain an estimate of the corresponding granulometric analysis, which can then be tested with real data later on, to verify the estimates. The model involves the selection and fracture functions depending on each mesh size in the ore distribution. This will allow the prediction of effective and reliable results, saving costs and more importantly, time, without the need to perform the milling process for each prediction of batch milling. Keywords.- milling, batch, simulation, granulometric, analysis, fracture, ore.