Rohit Khargotra

In this study, the techno-economic viability of electric vehicle charging stations using a combination of solar photovoltaic and wind turbine technologies is examined, with specific reference to identifying the optimal configuration of wind turbine, solar photovoltaic, battery, and power conversion equipment. The cost of energy, net present cost, and initial capital cost are examined in varying climatic conditions in India. A total of 26 sites are examined to assess the viability of solar photovoltaic, wind turbine, and battery-based electric vehicle charging stations. Key findings of the study reveal that solar photovoltaic, wind turbine, and battery configurations are the most cost-effective for 11 sites, with the lowest cost-effectiveness in Pune and the highest in Bhopal. For the remaining sites, solar photovoltaic and battery configurations are the most viable, with the highest cost-effectiveness in Jodhpur and the lowest in Srinagar. The study also reveals that wind turbine and battery configurations are not viable for any of the sites, as the cost of energy is high. It is also revealed through sensitivity analysis that higher electric vehicle load makes the systems unreliable, while aligning electric vehicle charging capacity with available resources enhances reliability and cost-effectiveness.

The development of efficient systems for removing antibiotics from wastewater is being pushed by increase in antibiotic resistance brought on by the environmental discharge of antibiotics. Antibiotic were not completely eliminated using conventional technologies like activated sludge, constructed wetland systems and many other procedures. A viable alternative for treating wastewater through adsorption, accumulation, biodegradation, photodegradation, and hydrolysis has recently been investigated using microalgae-based technology. This review focuses on effects of antibiotics on microalgae, as well as the ways in which microalgae remove antibiotics and how they work with other technologies to do so, including photocatalysis, advanced oxidation, and complementary microorganism degradation. The physiochemical and operational parameters like pH, temperature, light intensity and many more which influence the elimination of antibiotics in the wastewater treatment system. Future research requirements, further opportunities, the limitations of the available microalgae-based technologies were also discussed.

Public schools educate diverse socioeconomic groups, making air quality monitoring vital for students’ health and learning. These students are vulnerable to pollutants such as NO₂, SO₂, and PM₂.₅. Therefore air pollutants (AP) prediction helps in children’s health protection, learning enhancement, preventive measures, policy and awareness. Predictive models using ANNs, feature selection poses challenges due to data’s high dimensionality which increases over fitting risk and model complexity. Irrelevant features introduce noise, thereby reducing accuracy. So exploration of feature selection technique on ANN model accuracy remains an important area. This study explore relief algorithm (RA) for input selection to assess NO₂, SO₂, and PM₂.₅ prediction accuracy using multilayer feed forward neural network (MLFFNN), radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and multi linear regression (MLR) model. RA ranks variables like wind direction, vertical wind speed, and solar radiation. It significantly enhanced model performance, increasing R ² by 82.79% (SO₂) in RA-MLFFNN-1 and 77.37% (NO₂) in RA-MLFFNN-2. However, PM₂.₅ prediction using RA-MLFFNN-3 saw reduced accuracy, indicating model-specific effectiveness. The findings offer key insights for improving school air quality policy.

Small hydropower (SHP) systems are widely considered environmentally sustainable, but they still contribute to emissions during the construction and maintenance phase. This study evaluates the life cycle acidification potential of different SHP projects in India using life cycle assessment (LCA) techniques. SO_2eq emissions from these projects vary between 0.065 and 0.325 g/kWh, depending on the project type—run-of-river, canal-based, or dam-toe—as well as their respective size (capacity) and location. The predictive models developed here accurately estimate these emissions and provide practical tools for minimizing environmental impacts in for the prediction of future SHP projects. By optimizing project design and selecting appropriate parameters, SHP projects can significantly reduce their acidification potential, making them a cleaner alternative for renewable energy development.

Flow streams in rivers, canals, and the tail race of a hydropower plant can be transformed into usable kinetic energy with the help of a hydrokinetic turbine. In this work, the torque characteristics of the hybrid turbine having Savonius helical blade angles of 0˚ and 180˚ have been evaluated using a numerical technique. The characteristics of the turbine are driven for the range of water speed of 0.5 to 2.0 m/s and TSR of 0.3 to 1.5. It is observed from numerical analysis that the flow speed of water significantly affects the mean torque and static torque established by the hybrid turbine. The torque developed by the hybrid turbine enhances as water speed increases. However, the structure of the Savonius blade can alter the torque characteristics of the turbine. The mean torque and static torque growth by the hybrid turbine with a Savonius helical blade angle of 0˚ is more optimum than the hybrid turbine with a Savonius helical blade angle of 180˚. Although, the positive magnitude of torque is achieved at every rotor angle over one revolution by introducing a twist angle to the traditional Savonius blade in the hybrid configuration.