Faculty Publications

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    Investigation of iron ores based on the bond grindability test
    (American Institute of Physics Inc. subs@aip.org, 2020) Hanumanthappa, H.; Vardhan, H.; Raj, G.R.; Kaza, M.; Sah, R.; Sinha, A.; Shanmugam, B.K.
    Grinding is a process of reduction of lumps to powder depending on the requirement of particle size and particle shape. The present investigation involves the identify the physical properties of three different types of iron ores by using Bond ball mill. The result shows that the maximum Bond work index of 14 KWh/mt was obtained for 'A' type iron ore sample. The Bond work index for 'B' and 'C' type iron ore sample are of 11 and 10 KWh/mt. The variation of BWI of three iron ore sample may be varied during the geological formation of each iron ore sample. The output product of three iron ore sample composed of three different size fractions when ground in Bonds ball mill. Based on the BWI and output product size of three iron ore samples are classified as hard ore ('A' type iron ore sample), medium hard ore ('B' type iron ore sample) and soft ore ('C' type iron ore sample). The classification of different types of iron ore helps to run the plant scale ball mill by setting suitable operating parameters to the ball mill. © 2020 Author(s).
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    The screening efficiency of linear vibrating screen-An experimental investigation
    (American Institute of Physics Inc. subs@aip.org, 2020) Shanmugam, B.K.; Vardhan, H.; Raj, G.R.; Kaza, M.; Sah, R.; Hanumanthappa, H.
    Screening is a process of continuous physical separation of two or more powdered material depending on the particle size and particle shape. The present investigation involves the study of screening efficiency obtained in the widely used plant scale linear vibrating screen. The present investigation also involves the recommendations for improving screening efficiency. The result shows that the linear vibrating screen has the maximum screening efficiency of 65.92% for first 10 minutes of screening and reduced up to 59.34% for 60 minutes of screening. The reduction in screening efficiency with respect to screening time is due to the screen blinding. The screening of powdered materials faces the problem of screen mesh blinding which reduces the screening efficiency. The present authors recommend utilizing the screen configuration of circular vibrating screen which can provide higher screening efficiency with reduced screen blinding. The utilization of circular vibrating screen will reduce the screen blinding and prevents the labor requirement for removing screen blinding thereby increasing the efficiency and the production rate of screening. © 2020 Author(s).
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    A comparative study on a newly designed ball mill and the conventional ball mill performance with respect to the particle size distribution and recirculating load at the discharge end
    (Elsevier Ltd, 2020) Hanumanthappa, H.; Vardhan, H.; Raj, G.R.; Kaza, M.; Sah, R.; Shanmugam, B.K.
    The discharge end design of a ball mill plays an important role in discharging the desired particle sizes (?150 + 10 µm) and the percentage of recirculating load from the discharge end of the ball mill. In continuous wet ball mills, the composition of feed (hard ore or soft ore) to the mill varies continuously, leading to uncontrolled grinding in the mill. In view of this, a new design of the discharge mechanism has been implemented to remove the ground particles of desired particle size fraction with minimum recirculating load (+150 µm). The results from the discharge end with lifters (closed and open) show that the particle size fraction obtained from the discharge end has a maximum percentage of desired particle size fraction when the mill is operating at 60% critical speed. Discharge end without lifters has an uncontrolled particle size distribution in the discharge and the percentage of desired-size particles discharged was found to be very less. Also, the percentage of the recirculating load is minimum in the case of discharge end with lifter design compared with discharge end without a lifter. Hence, a new design of lifters in the discharge end leads to the discharge of the desired particle size fraction with minimum recirculating load. © 2019 Elsevier Ltd
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    Estimation of Grinding Time for Desired Particle Size Distribution and for Hematite Liberation Based on Ore Retention Time in the Mill
    (Springer, 2020) Hanumanthappa, H.; Vardhan, H.; Raj, G.R.; Kaza, M.; Sah, R.; Shanmugam, B.K.
    Iron ores obtained from different sources differ in their chemical and physical properties. These variations make the process of grinding a difficult task. The work carried out in this context focuses on three different samples of iron ore, viz., high silica high alumina, low silica high alumina, and low silica low alumina. The grinding process for all the three iron ores is carried out individually in Bond’s ball mill and the total retention time taken by each iron ore sample is calculated. The present investigation focuses on utilizing the calculated retention time of the iron ore as a standard grinding reference time to the laboratory ball mill for optimizing the grinding time of each ore. The desired P80 (150 ?m) with an acceptable range of hematite liberation (> 75%) was obtained in the laboratory ball mill after reducing 6 min from the total retention time taken in the Bond ball mill. © 2020, Society for Mining, Metallurgy & Exploration Inc.
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    Investigation on Iron Ore Grinding based on Particle Size Distribution and Liberation
    (Springer, 2020) Hanumanthappa, H.; Vardhan, H.; Raj, G.R.; Kaza, M.; Sah, R.; Shanmugam, B.K.; Pandiri, S.
    In the iron and steel industry, the production of narrow particle size distribution (PSD) for pellet feed making with acceptable liberation of valuables from the iron ore is very difficult. This study has been carried out to achieve desired pellet feed with narrow PSD and maximum liberation of hematite from the iron ore. The iron ores have been collected from three different sources (mines in Karnataka state) and milled. The iron ores and the blend feed samples were analyzed in the Optical Microscope (OM) and Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) to understand the PSD and percentage of hematite liberation. The new approach is adapted to identify the retention time (RT) of the iron ore in the mill, and the total RT taken for the blend sample in the Bond’s ball mill is considered as the reference grinding time for milling in the Laboratory Ball Mill (LBM). The desired narrow PSD (? 150/+ 10 µm) with acceptable hematite liberation is achieved at an optimal grinding time of 7 min in the LBM. © 2020, The Indian Institute of Metals - IIM.
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    Artificial neural network modeling for predicting the screening efficiency of coal with varying moisture content in the vibrating screen
    (Routledge, 2021) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.
    In India, coal is one of the prime sources of energy used in the power generation and metallurgy sector. The processing of coal below 3 mm is not successfully carried out in India. The quality of coal below 3 mm can be improved by decreasing the coal’s particle size, which reduces the ash percentage of coal. Screening is one of the significant beneficiation techniques used to reduce the size fraction of coal. The difficult to process coal of size ?3 + 1 mm was selected in the present work. In this work, an attempt has been made to screen the coal of size ?2 + 1 mm from ?3 + 1 mm using a 2 mm screen mesh in the vibrating screen generated at different moisture content, angle, and frequency of the deck. The performance of the vibrating screen was evaluated using screening efficiency. Furthermore, prediction using a feed backward artificial neural network (ANN) model was developed on the experimental results for ten different neuron conditions. From the results, it was clear that the prediction results obtained from the ANN model were in good correlation with the experimental results. © 2021 Taylor & Francis Group, LLC.
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    Design and fabrication of optimized magnetic roller for permanent roll magnetic separator (PRMS): Finite element method magnetics (FEMM) approach
    (Elsevier B.V., 2021) Mohanraj, G.T.; Rahman, M.R.; Joladarashi, S.; Hanumanthappa, H.; Shanmugam, B.K.; Vardhan, H.; Rabbani, S.A.
    In the present work, an attempt has been made to develop a PRMS in a cost effective and environmental friendly way through FEMM analysis of magnetic roller (active part of PRMS). The FEMM analysis indicates that, the optimized magnetic roller having magnet-to-steel disk thickness ratio of 5 mm: 2.5 mm was proved to be gainful in beneficiating paramagnetic minerals due to the best magnetic field value from the roller surface that is, 0.89 to 2.59 T. Prediction analysis was performed on FEMM data using artificial neural network (ANN) modelling technique. Further, the design calculations of lab scale PRMS in terms of power requirements and belt tensions were addressed. The fabricated PRMS was tested on paramagnetic mineral (hematite ore) assayed 51.24% of Fe, 10.20% of SiO2, and 2.98% of Al2O3 for different roller speeds and the belt thickness. The result showed that, at 0.5 mm belt thickness with 180 rpm roller speed the fabricated lab scale PRMS works well in terms of improvement in the Fe content up to 59.5% at the concentrate along with the Fe recovery of 71.41%. The obtained results suggest that, the FEMM analysis is more suitable to optimize the effective magnetic roller for the PRMS. © 2021 The Society of Powder Technology Japan
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    Evaluation of a new vibrating screen for dry screening fine coal with different moisture contents
    (Routledge, 2022) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.
    A new vibrating screen was developed with a circular mode of vibration for dry screening of moist coal of size fraction ?3 + 1 mm. Screen mesh of 2 mm aperture size will be used to separate the finer coal particles of size fraction ?2 + 1 mm. The new vibrating screen has the flexibility in changing the operational parameters such as the angle of the screen in upward or downward sloping direction and frequency of vibration of the screen deck. The circular mode of vibration provided to the screen deck will incorporate the inertial force on the particle in the screen deck, reducing screen clogging. The present study involves the analysis of the screening performance of the new vibrating screen with the coal feed of varying moisture content of 4%, 6% and 8%. The maximum screening efficiencies obtained for screening the coal feed with the moisture contents of 4%, 6% and 8% were 85.96%, 77.84%, and 68.27%, respectively. The higher screening performance of new vibrating screen was obtained due to good exposure time, particle mixing, particle segregation and particle stratification of coal on the screen deck. The results of the new vibrating screen will be a breakthrough in dry screening technology and accelerate the pilot-scale development. © 2019 Taylor & Francis Group, LLC.
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    Experimentation and statistical prediction of screening performance of coal with different moisture content in the vibrating screen
    (Routledge, 2022) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.
    Screening of coal is one of the processes carried out to produce clean coal suitable for the blast furnace. In this work, the screening of moist coal was carried out for different angles of the screen and frequencies. A 2 mm screen perforation was used to separate undersize coal of size +1 mm-2 mm from the +1 mm-3 mm coal samples. For each experimental condition, the screening efficiency was calculated. Maximum screening efficiency of 85.96%, 75.64%, and 63.46% was obtained at 4%, 6%, and 8% moisture content, respectively. As the moisture content of coal increases, the efficiency minimizes due to high screen clogging. After determining the screening efficiency, prediction was carried out using regression modeling. In this work, linear and second-order polynomial regression modeling was utilized to develop a prediction model for the experimental values. From the results, it was clear that the polynomial regression model has high regression coefficient (R2) percentage and low P-value in comparison with the linear regression model. After prediction, validation was carried out on the best fit model. The value of Variance Account For (VAF), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) was in the acceptable range, which shows that the developed model was most effective. © 2020 Taylor & Francis Group, LLC.
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    ANN modeling and residual analysis on screening efficiency of coal in vibrating screen
    (Taylor and Francis Ltd., 2022) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.
    In this paper, coal screening in vibrating screen was carried out with the size ranges of ?6 mm + 4 mm, ?4 mm + 2 mm, and ?2 mm + 0.5 mm. The vibrating screen was newly designed with flexibility in angle and frequency. The vibrating screen experimentation was carried out by varying screen mesh, angle, and screen frequency. During the screening, the angle was kept constant, and frequency was varied to obtain each size range’s screening efficiency. The experimental results of screening efficiency were evaluated for each size fraction range of coal. The maximum efficiency for screening coal with ?6 mm+4 mm, ?4 mm+2 mm, and ?2 mm+0.5 mm size range obtained was 87.60%, 80.93%, and 62.96%, respectively. Further, the prediction model was developed for each size range using a feed-backward artificial neural network (ANN) to consider the back-propagation error technique. For each screening condition, 10 ANN models were developed with the variation in 1–10 different neurons. ANN has provided mathematical models with a 99.9% regression coefficient for predicting each size range’s screening efficiency. Furthermore, the residuals of each optimal ANN model were analyzed using a normal probability plot and histogram. The ANN model’s accuracy was obtained from the residual analysis by evaluating four different model conditions, i.e., independence, homoscedasticity, normality, and mean error. © 2021 Taylor & Francis Group, LLC.