Journal Articles
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Item 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 LtdItem 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.Item 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.Item 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.Item 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 JapanItem 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.Item 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.Item CO2 detection: using the polyethyleneimine–cerium oxide nanocomposite sensing film coated on interdigitated electrode prepared from copper clad(Taylor and Francis Ltd., 2022) Naveen Kumar, N.; Prasad, P.; Savitha, M.B.; Lokesh, L.; Shanmugam, B.K.; Navaneeth Gowda, N.; Rohith, H.V.In this effective work, Polyethyleneimine (PEI) and Cerium Oxide (CeO2) with disparate weight percentage were designated for sensing Carbon dioxide (CO2). Four heterogeneous varieties of sensors with a varied weight percentage of CeO2 in PEI were fabricated by drop-casting the sensitive films on prepared Interdigitated electrodes (IDE) from copper-clad. Morphological, compositional, absorbance and X-ray studies were led on the cerium oxide nanoparticles by field-emission scanning electron microscopy (FESEM), energy dispersive X-ray analysis (EDAX), UV-Visible spectrometer and X-ray diffractometer (XRD). Response capabilities of all the four sensors at room temperature were attentively scrutinized. Unique capabilities of Repeatability, sensitivity, error-free measurements of the response time and recovery time were carefully inspected. It was summarized that the appropriate weight ratio of CeO2 and PEI was critical for sensing response. A feasible comparison between sensing responses of the fabricated sensors to CO2 under nitrogen (N2) was typically done. Relevant sensing process was investigated too. © 2020 Informa UK Limited, trading as Taylor & Francis Group.Item 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.Item Regression modeling and residual analysis of screening coal in screening machine(Taylor and Francis Ltd., 2022) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.Coal is one of the chief energy sources having significant applications in the iron and steel industry. This research investigates the screening efficiency of coal of different size range. The experiments on the screening of coal with different size range in the screening machine were carried out using different mesh sizes. The screening efficiency for different screen angles and frequency of vibration was carried out. After experimentation, regression modeling was carried out for each screening condition. The maximum efficiency of screening coal with size range +4 mm-6 mm, +2 mm-4 mm, and +0.5 mm-2 mm obtained was 87.60%, 80.93%, and 62.96%, respectively. The experimental results show that the screening efficiency decreases with the decrease in size range for screening from +4 mm-6 mm to +0.5 mm-2 mm. The reduction in screening efficiency was due to the clogging of coal to the screen mesh. Linear and quadratic modeling were performed to estimate the efficiency of all the experimental results. After prediction, the validation using residual analysis was carried out, and the results illustrate that the quadratic prediction modeling was accurate. © 2021 Taylor & Francis Group, LLC.
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