A pyramid type solar still with 0.82 × 0.81 × 0.75 m has been fabricated with galvanized iron sheet and tried with different water depths of 2–4 cm integrated with biomass heat source. The tail of the basin was attached with a heat exchanger having 0.025 m diameter and 3.5 m length, and having five numbers of bends. The function of the heat exchanger is to remove high temperature energy from biomass heat source to the saline water in the still at a constant current rate using circulation pump. Various solid, sensible, latent heat storage materials and evaporative surface materials are used in the still to increase the saline water temperature. To bring down the glass cover temperature, the outer glass was cooled using sprinkler manually at regular interval of fourth dimension. Experiments were conducted with biomass heat source for once flow mode and continuous stream mode and solar heat radiation mode. Theoretical analysis was performed using response surface methodology (RSM) software trial 9.01 version and compared with experimental values. The performances of modified still were compared with conventional still of the same size running under the same meteorological conditions. Productivity of sensible and evaporative materials was analyzed using RSM. Various heat transfer coefficients and efficiency were found out and the optimization levels were found using RSM. The substantial, sensible heat storage materials produce 48% more productivity than conventional still. Also, evaporative materials produce 19% more productivity than conventional still. The efficiency of the pyramid still with continuous flow mode produces more efficiency than once flow mode and solar mode. The efficiency of conventional still was depressed when compared with all other styles of operation.

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.

The research reveals that the optimization of an intake manifold plays an important role in manifold design to maximize the quantity of air received into the cylinder for competent combustion. Traditional manufacturing of intake manifolds and the trial and error method to select an optimized manifold is time-consuming and cost-effective. This work focuses on the design of an intake manifold with different configuration such as normal, convergent and venture types and the study of the air flow through each designed manifolds has been performed using CFD. On comparison of the analysis results, an optimized intake manifold which allows maximum airflow inside the cylinder has been selected for efficient combustion. Experimental Studies on emission level reduction shows that the flow harnessing in the intake manifold of the IC engine can yield improvement in engine torque up to 10% along with a nominal decrease in emission level.

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.