Faculty Publications
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Publications by NITK Faculty
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Item Performance Testing of Diesel Engine using Cardanol-Kerosene oil blend(EDP Sciences edps@edpsciences.com, 2018) Ravindra, n.; Mangalpady, M.; Harsha, V.Awareness of environmental pollution and fossil fuel depletion has necessitated the use of biofuels in engines which have a relatively cleaner emissions. Cardanol is a biofuel, abundantly available in India, which is a by-product of cashew processing industries. In this study performance of raw Cardanol blended with kerosene has been tested in diesel engine. Volumetric blend BK30 (30% kerosene and 70% Cardanol) has been used for the test. The properties like flash point, viscosity and calorific value of the blend have been determined. The test was carried out in four stroke diesel engine connected with an eddy current dynamometer. Performance of the engine has been analysed by finding the brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The results showed that the brake thermal efficiency of the blend is 29.87%, with less CO and smoke emission compared to diesel. The results were also compared with the performance of Cardanol diesel blend and Cardanol camphor oil blend, which were already tested in diesel engines by other researchers. Earlier research work reveals that the blend of 30% camphor oil and 70% Cardanol performs very closer to diesel fuel with a thermal efficiency of 29.1%. Similarly, higher brake thermal efficiency was obtained for 20% Cardanol and 80% diesel blend. © The Authors, published by EDP Sciences, 2018.Item Adaptive Workload Management for Enhanced Function Performance in Serverless Computing(Association for Computing Machinery, Inc, 2025) Birajdar, P.A.; Harsha, V.; Satpathy, A.; Addya, S.K.Serverless computing streamlines application deployment by removing the need for infrastructure management, but fluctuating workloads make resource allocation challenging. To solve this, we propose an adaptive workload manager that intelligently balances workloads, optimizes resource use, and adapts to changes with auto-scaling, ensuring efficient and reliable serverless performance. Preliminary experiments demonstrate an ≈ 0.6X% and 2X% improvement in execution time and resource utilization compared to the First-Come-First-Serve (FCFS) scheduling algorithm. © 2025 Copyright held by the owner/author(s).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 Investigation of Dust Emission in Limestone Mines and its Statistical Prediction using Supervised Machine Learning (Regression) Modelling(World Researchers Associations, 2025) Rajib, P.; Harsha, V.; Shanmugam, S.B.; Harish, H.; Amrites, S.In India, the fugitive dust emissions in the processing plant and mining area of limestone mines are very high. The dust emission of (particulate matter) PM10 and PM2.5 forms an unsafe working environment for workers in processing plant areas and mining areas. The excessive emission of PM10 and PM2.5 will cause lung-related diseases to the workers and the people existing in the adjacent areas of the mine. The dust emission majorly causes air pollution to occur due to the distribution of particulate matter in the work area. This study majorly investigates the dust emission levels of PM10 and PM2.5 in the limestone mine of Kadapa, Andra Prasad, India. The investigation on the dust emission of PM10 and PM2.5 was carried out as per the guidelines of DGMS and MoEF and CC guidelines, with a specific focus on PM10 and PM2.5 particulate matter. From the study, it was clear that the dust emission levels of PM10 and PM2.5 in the mine area and some parts of the processing area were below the permissible limit of 1200 ?g/m³ as per the National Ambient Air Quality Standards (NAAQS, 2009). It was also found that the dust emission levels of PM10 and PM2.5 in the crushing and screening area of the processing plant were above the permissible limit of 1200 ?g/m³. Further the statistical prediction model was developed using linear, quadratic and cubic supervised machine learning (regression) modelling. The results indicated that the cubic regression model will provide the accurate prediction of fugitive dust emission with lower error and standard deviation. © 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 Development of an equation to predict blast induced ground vibrations of open cast lime stone mine by using Multiple Linear Regression (MLR)(World Researchers Associations, 2025) Appani, R.; Harsha, V.; Subrahmanyam, S.K.V.This study focuses on predicting ground vibrations generated by blasting activities in open cast limestone mining by integrating blast design parameters with conventional variables. Blasting is a critical operation for the effective removal of overburden and mineral extraction, but it can lead to significant adverse effects, particularly ground vibrations, which pose challenges for both mining and environmental engineers. Conventional methods for estimating these vibrations typically focus on the distance from the blast site and the maximum charge per delay as key independent variables. Recognizing the substantial impact of blast design on vibration levels, this research employs multiple linear regression analysis to incorporate additional factors such as blast design elements. By developing a more comprehensive predictive model, the study aims to enhance the accuracy of ground vibration forecasts, ultimately contributing to safer and more sustainable mining practices. © 2025, World Researchers Associations. All rights reserved.Item 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.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.