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Item Enhanced Recovery of Iron Values from Low-Grade Ores and Tailings through Reverse Cationic Flotation(World Researchers Associations, 2025) Varma, R.M.; Harsha, V.; Reddy, B.R.R.; Shanmugam, S.B.; Harish, H.India is well-known for its rich deposits of high-quality hematite ores, making it a vital player in the global market. As the availability of high-grade iron ores diminishes, the need to process low-grade ores, fines and slimes through beneficiation is becoming increasingly important to meet market requirements. The creation of fines and slimes leads to a mineral loss of about 20 to 25% of the overall mineral value during processing. This research investigates the beneficiation of iron ore tailings using reverse cationic flotation, with Sokem reagent acting as a collector and starch serving as a depressant. A series of comparative assessments involving magnetic separation and gravity separation were performed. An initial mineralogical examination showed that hematite and goethite were the main iron-bearing minerals, accompanied by quartz and kaolinite as significant gangue materials. The selective flocculation technique proved effective, enhancing the iron grade from 41.05% to 57.03% Fe, with a recovery rate of 47.35%. After desliming, the outcomes improved further, yielding 58.25% Fe and a recovery of 29.00%. These results underline the potential for successful beneficiation of iron ore tailings, offering valuable insights for enhancing the recovery of high-grade iron from low-grade ores and reducing mineral losses during processing. © 2025, World Researchers Associations. All rights reserved.Item Development of beneficiation circuit for low-grade laterite iron ores sourced from the Gujarat area(World Researchers Associations, 2025) Reddy, B.R.R.; Harsha, V.; Bhushan, A.S.; Harish, H.; Shanmugam, S.B.This study focuses on the maximum recovery of iron values from the low-grade laterite iron ore. The Fe analysis of laterite was carried out using wet method analysis. Subsequently, the characterization studies were carried out on laterite ore using Optical microscope for liberation studies, mineral phase analysis with XRD and elemental analysis using SEM-EDS. Further, the ore of feed particle size of-150 microns was subjected to physical separation techniques such as scrubbing, hydro cyclone, spiral concentrator and dual-stage HGMS and two beneficiation circuits. The results from the above physical separation beneficiation techniques showed a concentrate of 41.25% FeG and a recovery of 48.05% in beneficiation circuit 1 and a concentrate of 48.03 % FeG and a recovery of 62.11% in beneficiation circuit 2 which is not feasible for iron-making in the blast furnace. © 2025, World Researchers Associations. All rights reserved.Item Experimentation and Statistical Prediction of Dust Emission in Iron Ore Mines using Supervised Machine Learning (Regression) Modelling(World Researchers Associations, 2025) Rajib, P.; Harsha, V.; Senapati, A.; Sahas, S.V.In India, the mine area and the processing plant of materials such as iron ore and coal will cause dust emissions. The fugitive dust emission creates a hazardous working environment for the workers. Dust emissions will cause pulmonary-related diseases to the workers and also to the people living in nearby areas of the mine. Environmental effects such as air pollution occur due to the dispersion of particulate matter over the permissible limit in the processing area. This study evaluates dust emission levels and air quality control measures in an iron ore mine (A), Karnataka, India. Fugitive and workplace dust sampling was conducted following DGMS and MoEF and CC guidelines, with a specific focus on PM10 and PM2.5 particulate matter. Measurements revealed that dust concentrations in several mining areas exceeded the permissible limit of 1200 ?g/m³ as per the National Ambient Air Quality Standards (NAAQS, 2009). To analyze and predict these concentrations, supervised machine learning (regression) modeling including linear, polynomial (order 2) and polynomial (order 2) models, was applied. The results indicated that a third-order polynomial regression model provided the best fit for predicting dust concentrations, demonstrating lower error. The study emphasizes the necessity of more robust dust suppression measures including installing a dry fog dust suppression system, to guarantee safe working conditions and adherence to environmental regulations, even in the face of efforts to reduce dust exposure. © 2025, World Researchers Associations. All rights reserved.