Faculty Publications
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Item 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 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.Item Investigation on the operational parameters of screening coal in the vibrating screen using Taguchi L27 technique(Taylor and Francis Ltd., 2022) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.In the present work, optimization of the newly developed vibrating screen’s operational parameters was carried out to obtain a high response parameter. The operational parameters considered in the present work were moisture content, angle, and frequency. The Taguchi L27 design technique was used to optimize three different operational parameters to obtain high screening efficiency of coal in the vibrating screen. The maximization of screening efficiency was obtained by selecting the “larger the better” condition for developing the model. The regression coefficient of 99.6% shows the close relationship between the predicted and experimental values. The lower value mean error and standard deviation of normal probability indicate that the developed model has less error. From the optimization results, it was clear that the 4% moisture content (low level), 1-degree angle (low level), and 9 Hz frequency (medium level) yielded high screening efficiency. Further, a confirmation test was carried out with the optimized condition, which has yielded a screening efficiency of 84.40%. The results showed that the Taguchi technique could be applied to study the influential operational parameters for maximizing the vibrating screen efficiency. © 2021 Taylor & Francis Group, LLC.Item Screening performance of coal of different size fractions with variation in design and operational flexibilities of the new screening machine(Taylor and Francis Ltd., 2023) Shanmugam, B.K.; Vardhan, H.; Govinda Raj, M.; Kaza, M.; Sah, R.; Hanumanthappa, H.Coal separation was usually carried out using the wet coal beneficiation technique. The waste generated by this technique pollutes the environment. So, in this work, a new mechanism of screening machine for dry coal beneficiation was developed. Dry coal screening removes ash impurities from the coal and improves its energy productivity. Hence, a new screening machine was developed with flexibility in changing the screen mesh, screen angle, and frequency of vibration. In this work, coal feed of less than 6 mm were divided into three groups of −6 + 4 mm, −4 + 2 mm, and −2 + 0.5 mm size fractions. Each size fraction was screened individually in the new screening machine by changing the screen mesh to the required perforation. The screening efficiency was determined for each size fraction by varying operational variables such as screen angle and frequency of vibration. This new screening machine provides maximum screening efficiency of 87.36%, 80.52%, and 66.42% for screening coal feed of 6 + 4 mm, −4 + 2 mm, and −2 + 0.5 mm size fractions, respectively. Highly efficient screening and higher removal of ash from coal were obtained due to the design and operational flexibilities of the screening machine. © 2019 Taylor & Francis Group, LLC.Item Comparison of the predictive model performance of Taguchi’s L27 and Box Behnken design optimization method for separating coal in vibrating screen(Taylor and Francis Ltd., 2023) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.The present research work evaluates the influential process parameters such as moisture content, angle, and frequency for separating coal in the vibrating screen. The design of the experiment for three factors with three levels was obtained using Taguchi’s and Response surface methodology’s (RSM) method. Taguchi’s L27 and RSM Box–Behnken design (BBD) method was used to conduct the separation experiment on a vibrating screen. The main effect plot of Taguchi’s L27 and BBD method was used to evaluate the optimized condition for obtaining the highest separation efficiency of the vibrating screen. The optimized condition obtained was lower moisture content (4%), lower angle (1 degree in upward slope), and medium frequency (9 Hz). The interaction plot of Taguchi’s L27 and BBD method was used to evaluate the interaction between the process parameters. From the interaction plot and ANOVA results, it was clear that the moisture content is the most significant parameter compared with the angle and frequency parameter for separating coal in a vibrating screen. From the prediction results, it was also clear the regression coefficient of Taguchi’s L27 was higher when compared with the RSM BBD method. This shows that Taguchi’s L27 is the most suitable optimization method compared with RSM. © 2022 Taylor & Francis Group, LLC.Item Comparison of the prediction performance of separating coal in separation equipment using machine learning based cubic regression modelling and cascade neural network modelling(Taylor and Francis Ltd., 2023) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.The availability of low-grade coal with a high amount of ash has urged the improvisation of separation equipment with minimal or no water utilization. The present work addresses the study on the separation equipment performance with different moisture coal. The experimental results were obtained in terms of separation efficiency. After obtaining the experimental results, the mathematical modeling results were obtained using different techniques. The cubic regression and cascade neural network models were considered to study the mathematical correlation with experimental results. The R-squared value of each mathematical modeling technique was correlated with the model fitting to check the model’s validity. The results clearly showed that the cubic model fitting for the experimental condition had provided an excellent R-squared value varying from 92% to 99%. The cascade model fitting for the experimental condition has provided a higher R-squared value, i.e., more than 99%. Results show that for all experimental conditions, the cascade model fitting of the neural network technique provides the significant mathematical modeling technique suitable for predicting the separation equipment’s performance compared to the cubic model of the regression technique. © 2022 Taylor & Francis Group, LLC.Item Evaluation of the Parametric Effects of Separation of Coal in Vibration Separator Using Plackett–Burman Design of Experiments(Springer, 2023) Shanmugam, B.K.S.; Vardhan, H.; Govinda Raj, M.; Kaza, M.; Sah, R.; Hanumanthappa, H.Plackett–Burman’s design of experiment (DOE) technique provides a mathematical interrelationship between the output parameter and influential input parameters. The vibration separator performance was evaluated by considering three input variables: moisture, inclination, and frequency. Plackett–Burman DOE consists of a minimum number of 12 experimental trials for obtaining the most influential input parameter of the vibration separator. The output parameter of the vibration separator obtained for each experimental trial was separation efficiency. So, the present work provides the most influential input parameter, which highly controls the separation efficiency of the vibration separator for the separation of coal. The model was validated using the residual analysis. Further, the revalidation of the Plackett–Burman DOE mathematical model for the separation of coal was carried out by comparing the closeness of the experimental cube plot and predicted cube plot. Furthermore, the Pareto chart, normal plot, and ANOVA table were utilized to determine the significant input parameter for obtaining higher efficiency of vibration separator. The main effect plot, interactive plots, and optimization results provide the most optimized input parameter for obtaining higher efficiency of coal separation. So, the present work will provide the most influential parameters using Plackett–Burman DOE for separation of coal in the vibration separator. © 2022, The Indian Institute of Metals - IIM.Item Experimental analysis of vibratory screener efficiency based on density variation for screening coal and iron ore(Taylor and Francis Ltd., 2024) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Hanumanthappa, H.; Reddy Byrareddy, R.; Sah, R.In the coal and mineral beneficiation industries, screening is one of the crucial physical separation methods carried out to separate the undersized fine particles from the oversize coarse particles. The vibratory screener is a relatively advanced screening technology applied for coal and iron ore beneficiation. This paper deals with the experimental investigation for assessing the efficiency of screening coal and iron ore in the vibratory screener. Furthermore, a comparative study between the test performance of screening coal and iron ore was carried out depending on moisture and density variation. Test results show that the vibratory screener can provide a high recovery of fines and increased efficiency for screening iron ore than coal material. The maximum efficiency of iron ore was attained at a higher angular position, such as 3 and 5 degrees in an upward slope, whereas the maximum efficiency of coal was attained at 1 degree in an upward slope. © 2023 Taylor & Francis Group, LLC.