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Author: search.filters.author.Harsha, V.Subject: search.filters.subject.experimental study

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    Experimental and statistical analysis on rate of penetration under the influence of rotational speed for drilling limestone in the open cast mine area
    (World Researchers Associations, 2025) Subrahmanyam, S.K.V.; Harsha, V.; Reddy, B.R.R.; Shanmugam, S.B.; Harish, H.
    In this study, an experimental investigation was carried out to study the rate of penetration for drilling limestone in an open-cast mine. The investigation was also carried out to study the influence of rotational speed. Drilling experiments were carried out with a constant drilling depth of 10m and varying speeds of 40rpm, 45rpm and 50rpm. As the drilling was carried out, the fresh drill bit caused an increase in the rate of drilling penetration. Further, as it reached the optimal level, there was a decrease in the rate of penetration due to the wearing out of the drill bit. Further, the prediction of experimental results was carried out using the regression analysis using linear and polynomial models. The results show that the polynomial model was found to be in close relation with experimental results. © 2025, World Researchers Associations. All rights reserved.
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    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.