Faculty Publications
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Item Development and Evaluation of Dust Cleaning System for a Solar PV Panel(University of Kuwait, 2022) Tripathi, A.K.; Mangalpady, M.; Ray, S.; Rao, N.R.N.V.; Vamshi Krishna, S.; Durgesh Nandan, D.The most promising application of solar energy is the conversion of solar energy into electrical energy by using solar photovoltaic (PV) panel. The performance of solar based PV panel is definitely influenced by the amount of solar radiation, which are reaching on the panel surface. Since the solar PV panels are operating in open atmosphere dust particles get deposited on their surfaces and most of the times they have to work in this condition. These deposited dust particles create a layer of dust particles over the panel surface which prevents the 100% penetration of solar radiation into the panel surface. Therefore, proper cleaning of the panel surface becomes very necessary. In order to improve the performance of the PV panel an automatic microcontroller driven dust cleaning technique is developed which is capable of removing the accumulated dust particles from the PV panel surface. Moreover, an experimental study has been performed to analyse the efficiency of this developed techn ique. The developed cleaning system showed an improvement of 27.98% in the output power of PV panel when compared to the dusty panel. © 2022 University of Kuwait. All rights reserved.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.