Faculty Publications
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Item Performance Evaluation of Various Ni-Based Catalysts for the Production of Hydrogen via Steam Methane Reforming Process †(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Subramanya, S.N.; Reddy, V.S.C.; Madav, V.Steam methane reforming (SMR) approaches are highly recognised and pivotal in industrial H2 production, contributing over 40% to global hydrogen production. The prime objective of this study is to optimise the significant parameters involved in the SMR process to achieve the utmost conversion of CH4 to H2. To attain this, a sophisticated one-dimensional unsteady-state heterogeneous plug flow reactor (PFR) model was methodically constructed and simulated using the Aspen HYSYS V11 software. The study comprises an exhaustive comparison of seven diverse sets of catalysts, primarily categorised based on the different weight percentages of Ni in Ni/Al2O3 catalysts, along with various promoters incorporated to enhance the conversion rate in the SMR process. This comprehensive evaluation identifies the most operative catalyst configuration for optimising CH4 conversion. The results obtained through the simulations revealed that CH4 conversion intensifies with an increase in temperature, while it weakens with higher pressures within the catalyst set considered for the study. The analysis yielded promising conclusions by comparing the simulated CH4 conversion percentages at various temperatures with data from the existing literature. The maximum absolute error encountered was only 3.72%, signifying the accuracy and reliability of the developed model. Moreover, the Mean Absolute Error (MAE) calculated was a low 1.42%, suggesting the robustness of the proposed approach. The findings lay the foundation for future innovations and improvements in the field, ultimately fostering more efficient and sustainable hydrogen generation. As the demand for clean energy grows, the optimisation of the SMR process becomes increasingly vital, making this study a crucial step towards meeting global energy needs while minimising environmental impact. © 2024 by the authors.Item Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques(Nature Research, 2024) Tripathi, A.K.; Mangalpady, M.; Parida, S.; Durgesh Nandan, D.; Elumalai, P.V.; Prakash, E.; Joshua Ramesh Lalvani, J.S.C.; Koppula, K.S.The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m3, 27.89 µg/m3, 26.17 µg/m3, and 27.24 µg/m3, respectively) due to the dry nature of clay materials. Additionally, the research establishes four ML models—Decision Tree (DT), Gradient Boosting Regression (GBR), Random Forest (RF), and Linear Regression (LR)—for predicting PM pollutant concentrations. Notably, Random Forest demonstrates superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) at 1.079 and 1.497, respectively. This comprehensive solution, combining IoT-based monitoring and ML-based prediction, contributes to sustainable mining practices, safeguarding worker well-being, and preserving the environment. © The Author(s) 2024.Item Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques(Nature Research, 2025) Swamy, S.V.; Kunar, B.M.; Chandar, K.R.; Alwetaishi, M.; Shashikumar, S.; Reddy, S.Uniaxial Compressive Strength (UCS) is a fundamental parameter in rock engineering, governing the stability of foundations, slopes, and underground structures. Traditional UCS determination relies on laboratory tests, but these face challenges such as high-quality core sampling, sample preparation difficulties, high costs, and time constraints. These limitations have driven the adoption of indirect approaches for UCS prediction. This study introduces a novel indirect method for predicting uniaxial compressive strength, harnessing the grinding characteristics of a ball mill as predictive variables through supervised machine learning techniques. The correlation between grinding characteristics and UCS was examined to determine whether a linear relationship exists between them. A hybrid support vector machine-recursive feature elimination (SVM-RFE) algorithm is applied to identify the critical grinding parameters influencing UCS. Four supervised machine learning models viz., Multiple Linear Regression (MLR), k-Nearest Neighbor Regression (k-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR) were developed for UCS prediction, with hyperparameter optimization performed using RandomisedSearchCV technique. The Random Forest model outperformed others as the best prediction model, achieving a coefficient of determination (R²) of 0.95, followed by SVR (R² = 0.87), k-NNR (R² = 0.82), and MLR (R² = 0.758). Model robustness was further assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF). Internal validation by means of K-fold cross validation and external validation with independent datasets confirmed generalization capability, showing an average prediction error of ± 10%. The findings demonstrate that combining grinding characteristics with machine learning offers an accurate, cost-effective alternative to conventional UCS testing, with significant practical applications in rock engineering. © The Author(s) 2025.Item Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning(Nature Research, 2025) Mangalpady, M.; Vardhan, H.; Tripathi, A.K.; Parida, S.; RajaSekhar Reddy, N.V.; Sivalingam, K.M.; Yingqiu, L.; Elumalai, P.V.Monitoring and predicting ground vibration levels during blasting operations is essential to safeguard mining sites and surrounding communities. This study introduces an IoT-based ground vibration monitoring device specifically designed for limestone mining operations, combined with machine learning algorithms to predict ground vibration intensity. The primary aim is to provide an efficient predictive tool for anticipating hazardous vibration levels, enabling proactive safety measures. A comparative analysis with the industry-standard Minimate Blaster indicates high accuracy of the IoT device, with percentage errors as low as 0.803% across multiple blasts. The study also employed Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Random Forest (RF) algorithms to predict Peak Particle Velocity (PPV) values. Among these, the Random Forest model outperformed the others, achieving an R2 score of 0.92, Mean Absolute Error (MAE) of 0.21, and Root Mean Squared Error (RMSE) of 0.31. These findings underscore the reliability and predictive accuracy of the IoT-integrated Random Forest model, suggesting that it can significantly contribute to enhancing safety and operational efficiency in mining. The research highlights the potential of IoT and machine learning technologies to transform ground vibration monitoring, promoting safer and more sustainable mining practices. © The Author(s) 2025.