Periyar Maniammai Institute of Science & Technology

Remote communities often lack access to reliable electricity. This study investigates the feasibility of a microgrid system tailored for Kantong Kunda, a rural community in The Gambia. The community's current energy consumption and demand are determined through data collection using the Epicollect5 survey tool to characterize the local energy consumption and demand profile accurately. HOMER Pro software was employed to simulate and optimize hybrid microgrid configuration, prioritizing both cost-effectiveness and environmental sustain-ability. The proposed system integrates 79.8 kW of Solar Photovoltaic (SPV), a 60-kW diesel generator, 374 batteries, and a 22.8 kW converter. The optimized design yields a Net Present Cost (NPC) of $251,474.80 and a Levelized Cost of Energy (LCOE) of $0.08527/kWh, which is well below the region's grid electricity tariff. This configuration yields 16.1 % excess electricity, a 10.5 % Return on Investment (ROI), a 14.2 % Internal Rate of Return (IRR), and a 6.03-year payback period, while cutting total emissions by 133,981.4 kg compared to a diesel-only baseline. The work contributes a microgrid design designed for rural African communities, and the findings demonstrate that microgrids can deliver reliable, affordable, and low-carbon electricity through decentralized energy systems for remote communities.

Aerodynamicists use the windtunnels to test the scaled models of an Aircraft, components and other vehicles placed inside the test section of it. The flow properties, Lift and drag forces over the model can be studied. An experimental jet flow setup which is similar to a low speed open circuit wind tunnel has been designed and fabricated using mil steel material. This setup is capable of producing the flow using the exhaust fan which is placed at entry portion of it. This setup was made by keeping in mind that, the educators, students and aerodynamicists to to visualize the flow pattern and to determine the flow properties over the model placed in the test section. The laminar flow can be achieved at the downstream end of nozzleand it is passed in to the test section. In this paper, the exit velocity of the nozzle for varying rpm was determined and plotted. Also, the vortex formation inside test section can be easily visualized by passing the LASER light source.

The Green Metaverse is not yet eco-friendly due to limited research. This paper assesses the Green Metaverse's potential for human well-being and planetary health. Using a mixed-method approach, sustainable infrastructure accessibility and human-centred value creation are explored. Findings show that combining energy-efficient design with applications like digital learning and global collaboration offers transformative potential for a sustainable virtual future.

A machine learning model prediction of crop yield and quantify soil health using a multi-source agricultural record on twenty thousand four hundred four two crops in India. The model is a combination of the XGBoost and CatBoost models which is used to forecast the complex nonlinear relationships between soil nutrients ((N-Nitrogen), (P-Phosphorus), (K-Potassium)), pH, agricultural features, and weather conditions such as temperature, precipitation, and humidity. It is proposed that a new soil health index (SHI) from a 0-100 scale must be composed of nutrient balance indicators, pH suitability indicators, and moisture indicators. Some of the aspects that influence the sound performance of the model include rigorous preprocessing and 5fold cross-validation. Experimentally, CatBoost is greater than XGBoost in terms of R2 of 97.73 and soil health estimation of 99.13 in terms of crop yield prediction and soil health estimation, respectively. The discussion of the importance of these features has revealed that the following factors, such as production, cultivated area, rainfall and humidity. On the whole, the framework can list real agroclimatic relationships and provide valuable data on how to design and manage fertilizer, irrigation and the utilization of resources advantageously, which indicates a positive prospect of climate sensitive farming and real-time decision-support frameworks.

This project aims to tackle the problem of maintaining proper lubrication at different external conditions in the mining industry. It aims to develop a system that uses three essential sensors: a Resistance Temperature Detector (RTD) to monitor temperature, an inline viscometer to measure grease viscosity, and ultrasonic level sensors for real-time grease level measurement. These sensors supply information to the PIC16F877A microcontroller, which serves as the system's central processing unit. To achieve the required lubrication conditions, a PID (Proportional-Integral-Derivative) control algorithm is coded to the microcontroller. This algorithm makes sure that there is a quick response to fluctuations in temperature and viscosity, keeping the grease within its optimal operating range of 40° C to 80° C and 30 to 430 Centistokes. By controlling the heating elements, the control system avoids both overheating and the solidification of grease, making sure there's consistent lubrication. The design and functionality of the system was tested through simulations with Proteus software. The simulations tested the system's performance under different environmental conditions, such as extreme cold and high operational demands. The simulation results showed that the system could maintain effective lubrication by adjusting to different scenarios. This project shows the practical application of sensor integration and control algorithms within the industrial ball mill. The Automated Mill Greasing System improves the reliability and efficiency of mill operations while also reducing the need for human intervention, therefore avoiding costly downtime.