Pelletization of synthesized magnetite concentrate obtained by magnetization roasting of Indian low-grade BHQ iron ore

Prediction of Sinter Properties Using a Hyper-Parameter-Tuned Artificial Neural Network

The present work aims at performing prediction validation for the physical properties of coke layered and nonlayered hybrid pelletized sinter (HPS) using artificial neural networks (ANNs). Physical property analyses were experimentally performed on the two HPS products. The ANN model was then trained to obtain the best prediction results with the grid-search hyper-parameter tuning method. The learning rate, momentum constant, and the number of neurons varied over specified ranges. The binary variable conversion was utilized to assess the two sintering processes. The nonlayered HPS product of 4 mm micropellets at basicity 1.75 and using 8% coke shows a good combination of physical properties, whereas HPS of 4 mm micropellets at 1.5 basicity using 4% coke as fuel and 2% coke as layering gives a radical improvement in physical properties. The yield of the HPS product is 96.07%, with the shatter index (SI), tumbler index (TI), and abrasion index (AI) values being 86.12, 79.60, and 5.74%, respectively. Hence, HPS can be preferred by implementing the layering of coke powder. The prediction analyses showed that the multilayer perceptron model (MLP) network with a 4-29-5 structure showed prediction accuracies of over 99.99% and a mean squared error (MSE) of 2.87 × 10^-4. It verifies the accuracy and prediction effectiveness of the hyper-parameter-tuned ANN model.

Effects of operation parameters on particle mixing performance in a horizontal high shear mixer

Particle mixing is an important unit operation in many industry processes. This work employed the discrete element method (DEM) to characterize the mixing performance of iron ore particles in a horizontal high shear mixer. The simulation results were first compared with the positron emission particle tracking (PEPT) data reported by Forrest et al. to validate the DEM model. Then the effects of key operation factors such as impeller rotation speed, number of impellers and filling ratio on the mixing efficiency were discussed. The Lacey mixing index was employed to evaluate the mixing performance. The results show that the mixing efficiency of particles increased with increasing the number of impellers and their rotation speeds. On the other hand, the filling ratio was negatively related to the particles velocity and the mixing efficiency. This was because the effective space left for free movement of particles decreased as the filling ratio increased, and the mixing of particles was therefore restricted. In addition, the mixing rate of particles in the axial direction of the high shear mixer was much slower than that in the radial direction.

Clean Utilization of Limonite Ore by Suspension Magnetization Roasting Technology

As a typical refractory iron ore, the utilization of limonite ore with conventional mineral processing methods has great limitations. In this study, suspension magnetization roasting technology was developed and utilized to recover limonite ore. The influences of roasting temperature, roasting time, and reducing gas concentration on the magnetization roasting process were investigated. The optimal roasting conditions were determined to be a roasting temperature of 480 ℃, a roasting time of 12.5 min, and a reducing gas concentration of 20%. Under optimal conditions, an iron concentrate grade of 60.12% and iron recovery of 91.96% was obtained. The phase transformation, magnetism variation, and microstructure evolution behavior were systematically analyzed by X-ray diffraction, vibrating sample magnetometer, and scanning electron microscope. The results indicated that hematite and goethite were eventually transformed into magnetite during the magnetization roasting process. Moreover, the magnetism of roasted products significantly improved due to the formation of ferrimagnetic magnetite in magnetization roasting. This study has implications for the utilization of limonite ore using suspension magnetization roasting technology.

Extraction of Manganese and Iron from a Refractory Coarse Manganese Concentrate

In this research, the coarse manganese concentrate was collected from a manganese ore concentrator in Tongren of China, and the contents of manganese and iron in coarse manganese concentrate were 28.63% and 18.65%, respectively. The majority of the minerals in coarse manganese concentrate occur in rhodochrosite, limonite, quartz, olivine, etc. Calcium chloride, calcium hypochlorite, coke, and coarse manganese concentrate were placed in a roasting furnace to conduct segregation roasting, which resulted in a partial chlorination reaction of iron to produce FeCl3, ferric chloride reduced to metallic iron and adsorbed onto the coke, and rhodochrosite broken down into manganese oxide. Iron was extracted from the roasted ore using low-intensity magnetic separation, and manganese was further extracted from the low-intensity magnetic separation tailings by high-intensity magnetic separation. The test results showed that iron concentrate with an iron grade of 78.63% and iron recovery of 83.60%, and manganese concentrate with a manganese grade of 54.04% and manganese recovery of 94.82% were obtained under the following optimal conditions: roasting temperature of 1273 K, roasting time of 60 min, calcium chloride dosage of 10%, calcium hypochlorite dosage of 5%, coke dosage of 10%, coke size of −1 mm, grinding fineness of −0.06 mm occupying 90%, low-intensity magnetic field intensity of 0.14 T, and high-intensity magnetic field intensity of 0.65 T. Most minerals in the iron concentrate were Fe, Fe3O4, and a small amount of SiO2 and CaSiO3; the main minerals in the manganese were MnO, and a small amount of Fe3O4, SiO2, and CaSiO3. The thermodynamic calculation results are in good agreement with the test results.