Raja Kandasamy

1. The increasing need for clean, potable water has made reverse osmosis (RO) systems a critical component in both domestic and industrial water purification processes. RO systems effectively remov...

For saline or wastewater, solar distillation is a suitable, environmentally friendly technique for purifying water. The problem is that there is no distillation process that produces salt water, and that salt water used on plants may cause plant death. The techniques described in this work aim to enhance the solar distillation process using solar stills with a steam generator and a condenser. The distilled water was heated between 11 am and 4 pm, with best output of 1200 ml at 80-90°C. One method used to achieve faster evaporation is to increase the heating rate so that the temperature changes rapidly, such as 100°C/min, which results in lower total yields due to overheating. The novelty of this work is that the heating rate to the steam generator is increased, thereby increasing the overall temperature and the surface water temperatures of the solar still and the water surface. The other advantage of the extended heating for three hours was that it also resulted in higher yields, and sunlight alone did not guarantee that high temperatures were equivalent to moisture-loss temperatures above 41°C.Various thermal enhancement techniques were used in the study, including sensible heat storage synthesis, warming in an aquatic basin, and altering surface absorptivity. Furthermore, external thermal support to increase heat supply and improve thermal conduction pathways to the basin was designed to keep high temperatures throughout the low-light availability period. This study examined the effect on system efficiency and the performance comparison of different approaches to increase the efficiency of the solar dehydration process under realistic operating conditions. This study provides a scalable, energy- efficient, and renewable-energy solution to locally produce freshwater for the benefit of global health and climate goals.

This study explores the performance, emission characteristics, and machine learning-based predictive modeling of a compression ignition engine operating on diesel–fusel oil blends. Experiments were carried out at ten engine speeds ranging from 1000 to 3250 rpm under full-load conditions using six blends containing 0%–37% fusel oil by volume. The brake-specific fuel consumption (BSFC) rose from 296 g/kW h for neat diesel to 358 g/kW h for the 37% blend at 2250 rpm, corresponding to a 20.9% increase, while a 22.7% rise was noted at 1000 rpm. Maximum power output declined from 6.0 kW (diesel) to 5.78 kW (37% blend) at 3250 rpm, accompanied by a 12% reduction in engine torque (ET) at higher fusel proportions. Emission measurements indicated a 15%–20% decrease in nitrogen oxides (NOx) with greater fusel content, whereas carbon monoxide (CO) and unburned hydrocarbon emissions increased by 35% and 18%, respectively. Five machine learning models—extreme gradient boosting (XGBoost), random forest (RF), support vector regression, artificial neural network, and multi-layer perceptron, were employed to predict engine parameters using 58 experimental data samples. Hyperparameter optimization through a fivefold cross-validated grid search revealed that XGBoost achieved the highest predictive accuracy, with R2 values exceeding 0.90 for seven of nine target outputs, including 0.94 for BSFC, 0.99 for engine power and NOx, and 0.96 for ET and CO. The RF model exhibited similar reliability, whereas neural networks were less consistent due to data limitations. This hybrid framework effectively minimizes experimental effort by ∼88% while maintaining over 90% prediction accuracy.

One of the practical components that can be found in aircraft engines, the Delaval nozzle, works to improve the aircraft's performance in terms of its stability and its capacity to manoeuvre at different speeds of operation. This research concentrates on the acoustic behaviors shown by this nozzle during the transition from low rate to sonic speed. The modification of the nozzle tip at various degrees of inclination on the transition velocity is the procedure used to minimize the amount of acoustic energy emitted. The acoustic behavior is analyzed based on the FW-H acoustics model utilized, and the optimization problem is solved using the practical eddy simulation approach in CFD. The acoustic Power, measured in dB, is estimated at various angles on the nozzle tip, and the performance of the Delaval nozzle is used to show how the redesigned nozzle tip affects the nozzle's acoustic behavior.

This research was aimed to find the structural behaviour of Calotropis procera fibres added concrete and the effect of Azadirachta indica leaf slurry blended to concrete and exposed to seawater. Conventional and fibre reinforced concrete samples fabricated keeping the curing time as 7, 14 and 28 dayswere subjected to compression, split tensile, rebound hammer and ultrasonic pulse velocity test. Increase in curing time increased the structural properties and Calotropis procerafibres added samples produced respectively 19.5%, 15%, 10.3 and 14.5% improvement in the compression, tensile, rebound hammer and ultrasonic pulse velocity values. These fibres reduced the brittleness of the specimen and avoided unprecedented failures. Accelerated corrosion and gravimetric mass loss test conducted to estimate the anti-corrosive property of Azadirachta indica leaf slurry showed decrease in corrosion rate. The corrosion rate of concrete samples without and with inhibitors was 0.0654 ± 0.008 and 0.056 ± 0.011 mm/year respectively evincing the anti-corrosive effects of Azadirachta indica leaves. Thus green materials are compatible with concrete and can be used in making sustainable concrete structures.

The technological advancement in all countries leads to massive energy demand. The energy trading companies struggle daily to meet their customers’ power demands. For a good quality, disturbance-free, and reliable power supply, one must balance electricity generation and consumption at the grid level. There is a profound change in distribution networks due to the intervention of renewable energy generation and grid interactions. Renewable energy sources like solar and wind depend on environmental factors and are subject to unpredictable variations. Earlier, energy distribution companies faced a significant challenge in demand forecasting since it is often unpredictable. With the prediction of the ever-varying power from renewable sources, the power generation and distribution agencies are facing a challenge in supply-side predictions. Several forecasting techniques have evolved, and machine learning techniques like the model predictive controller are suitable for arduous tasks like predicting weather-dependent power generation in advance. This paper employs a Model Predictive Controller (MPC) to predict the solar array’s power. The proposed method also includes a system identification algorithm, which helps acquire, format, validate, and identify the pattern based on the raw data obtained from a PV system. Autocorrelation and cross-correlation value between input and predicted output 0.02 and 0.15. The model predictive controller helps to recognize the future response of the corresponding PV plant over a specific prediction horizon. The error variation of the predicted values from the actual values for the proposed system is 0.8. The performance analysis of the developed model is compared with the former existing techniques, and the role and aptness of the proposed system in smart grid digitization is also discussed.

Solar photovoltaics has become the most popular renewable energy source due to its simplicity in installation and maintenance. However, the dependence on the availability of solar energy at the instant makes its operation non-linear. Various optimizing solutions are proposed to rule out this disadvantage. This paper dwells on a machine language approach to solve this problem. A maximal tracker for power points relies on fuzzy logic control. An embedded power optimizer is designed and tested under different environmental conditions through simulation. The results presented allow researchers to test various artificial intelligence techniques for renewable energy extraction processes.