Improved solar still productivity using PCM and nano- PCM composites integerated energy storage.

The study investigates the impact of Phase Change Material (PCM) and nano Phase Change Materials (NPCM) on solar still performance. PCM and a blend of NPCM are placed within 12 copper tubes submerged in 1 mm of water to enhance productivity. Thermal performance is assessed across four major scenarios with a fixed water level of 1 mm in the basin. These scenarios include the conventional still, equipped with 12 empty copper rods and 142 g of PCM in each tube, as well as stills with NPCM Samples 1 and 2. Sample 1 contains 0.75% nanoparticle concentration plus 142 g of PCM in the first 6 tubes, while Sample 2 features 2% nanoparticle concentration plus 142 g of PCM in the subsequent 6 tubes. Aluminum oxide (Al2O3) nanoparticles ranging in size from 20 to 30 nm are utilized, with paraffin wax (PW) serving as the latent heat storage (LHS) medium due to its 62 °C melting temperature. The experiments are conducted under the local weather conditions of Vaddeswaram, Vijayawada, India (Latitude-80.6480 °E, Longitude-16.5062 °N). A differential scanning calorimeter (DSC) is utilized to examine the thermal properties, including the melting point and latent heat fusion, of the NPCM compositions. Results demonstrate that the addition of nanoparticles enhances both the specific heat capacity and latent heat of fusion (LHF) in PCM through several mechanisms, including facilitating nucleation, improving energy absorption during phase change, and modifying crystallization behavior within the phase change material. Productivity and efficiency measurements reveal significant improvements: case 1 achieves 2.66 units of daily production and 46.23% efficiency, while cases 2, 3, and 4 yield 3.17, 3.58, and 4.27 units of daily production, respectively. Notably, the utilization of NPCM results in a 60.37% increase overall productivity and a 68.29% improvement in overall efficiency.

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques.

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.

Enhancing automotive cooling systems: composite fins and nanoparticles analysis in radiators.

Composites are driving positive developments in the automobile sector. In this study investigated the use of composite fins in radiators using computational fluid dynamics (CFD) to analyze the fluid-flow phenomenon of nanoparticles and hydrogen gas. Our world is rapidly transforming, and new technologies are leading to positive revolutions in today's society. In this study successfully analyzed the entire thermal simulation processes of the radiator, as well as the composite fin arrangements with stress efficiency rates. The study examined the velocity path, pressure variations, and temperature distribution in the radiator setup. As found that nanoparticles and composite fins provide superior thermal heat rates and results. The combination of an aluminum radiator and composite fins in future models will support the control of cooling systems in automotive applications. The final investigation statement showed a 12% improvement with nanoparticles, where the velocity was 1.61 m/s and the radiator system's pressure volume was 2.44 MPa. In the fin condition, the stress rate was 3.60 N/mm2.

Development of an ammonia-biodiesel dual fuel combustion engine's injection strategy map using response surface optimization and artificial neural network prediction.

The study intends to calibrate the compression ignition (CI) engine split injection parameters as efficiently. The goal of the study is to find the best split injection parameters for a dual-fuel engine that runs on 40% ammonia and 60% biodiesel at 80% load and a constant speed of 1500 rpm with the CRDi system. To optimize and forecast split injection settings, the RSM and an ANN model are created. Based on the experimental findings, the RSM optimization research recommends a per-injection timing of 54 °CA bTDC, a main injection angle of 19 °CA bTDC, and a pilot mass of 42%. As a result, in comparison to the unoptimized map, the split injection optimized calibration map increases BTE by 12.33% and decreases BSEC by 6.60%, and the optimized map reduces HC, CO, smoke, and EGT emissions by 15.68%, 21.40%, 18.82, and 17.24%, while increasing NOx emissions by 15.62%. RSM optimization with the most desirable level was selected for map development, and three trials were carried out to predict the calibrated map using ANN. According to the findings, the ANN predicted all responses with R > 0.99, demonstrating the real-time reproducibility of engine variables in contrast to the RSM responses. The experimental validation of the predicted data has an error range of 1.03-2.86%, which is acceptable.