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.

The scientific aim of the study is to propose a comprehensive review of thermal management systems (TMSs) used in electric vehicle (EV) battery packs on matters pertaining to performance enhancement, improvements in safety, and reliability. This includes the various thermal management strategies, addressing some of the problems posed by the dynamic nature of operating conditions, and evaluating emerging TMS technologies. From this aspect, the problem of this research focused on the description of a detailed insight into the efficiencies of TMSs inside an EV, pointing to the impacts of various cooling mechanisms, mostly liquid cooling, air cooling, and phase‐change materials. The research study further evaluates the integration of TMS in vehicle design and its effects on battery lifespan, charging speeds, and environmental impacts. The benefits, disadvantages, and specific applications of each method are discussed about EVs. Taking into consideration the fast charging, high‐power, and environmental effects, further discussion is made on the specific challenges that come with dynamic operating conditions of EVs. This is shown through the industry's constant pursuit to develop in this critical area through the discovery of novel technologies, including predictive control algorithms and superior thermal materials. It discusses in depth how heat management is integrated into the general vehicle design and how this impacts battery lifespan, charging speed, and range. In conclusion, it is a source of material for research scholars, engineers, and policymakers in charge of developing EVs by synthesizing what already exists, highlighting trends at current times, and outlining possible future directions in the continuum of optimizing TMS for the next generation of driving automobile transportation batteries.

In this study, the impacts of co-pyrolyzing wood-based biomass from Ficus benghalensis with PET on liquid oil output, reactivity, and heating values were investigated. The effects of temperature on the product distribution of individual pyrolysis and the biomass-plastic ratio on co-pyrolysis were investigated. For individual pyrolysis, a maximum amount of 40.8wt () liquid oil was obtained from biomass at 450C. On the other hand, a maximum of 59.5wt () liquid oil was obtained from PET at 500C. The co-pyrolysis experiments were conducted by blending PET with biomass at different percentages, such as 20, 40, 60, and 80. At 60 addition of PET, a more positive synergistic effect was identified due to radical secondary reactions. In addition, the physical and chemical characterization studies conducted on pyrolysis oil showed that biomass and plastic materials could be used to make valuable chemicals.

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.

This work explores a novel integration of experimental conversion of waste cooking oil (WCO) into biodiesel with advanced machine learning modeling to optimize transesterification outcomes. A reusable CaO catalyst derived from egg shells was employed, delivering a more affordable and sustainable option compared to typical homogeneous catalysts. A total of 16 experimental runs were conducted to investigate the effects of catalyst concentration (CC), reaction temperature (RT), and methanol-to-oil molar ratio (MOR) on biodiesel yield. Four boosted ML algorithms XGBoost, AdaBoost, Gradient Boosting Machine (GBM), and CatBoost were applied to model the process, with hyperparameter tuning via grid search and validation through k-fold cross-validation (k = 5) and residual plots to ensure reliability and mitigate overfitting. CatBoost emerged as the best-performing model (R² = 0.955, RMSE = 0.83, MSE = 0.68, MAE = 0.52), predicting a maximum biodiesel yield of 95% at 3% CC, 80 °C RT, and a 6:1 MOR. Feature importance and partial dependence plots identified MOR and CC as the most influential parameters. Engine performance tests further validated the practical viability of CaO-based biodiesel, showing 26% lower CO emissions and 13% lower smoke emissions compared to diesel, resulting in a marginal 2.83% decline in brake thermal efficiency alongside a 4.31% rise in fuel consumption. This interdisciplinary approach combining green catalyst development with interpretable machine learning demonstrates a promising pathway for cleaner energy applications and data-driven optimization in biodiesel research.

Lignocellulosic biomass is the potential raw material for the production of biofuels through pyrolysis. It is an effective technique for converting biomass to biofuels. However, biofuel from agricultural residues and woody-based feedstocks shows poor fuel properties due to higher oxygen content. Co-pyrolysis is a promising process to produce high-quality bio-oil by two or more different materials. Forestry, industrial, and agricultural outcomes are the ideal co-feedstocks for improved bio-oil quality. In this study, individual and co-pyrolysis of hardwood, pressed mustard oil cake and corncob were conducted at a temperature of 500C. Before conducting pyrolysis experiments, thermogravimetric analysis was conducted to evaluate thermal degradation behavior. Through individual pyrolysis, corncob yielded a maximum bio-oil of 43.9wt. On the other hand co-pyrolysis on binary blends of hardwood and corncob produced maximum bio-oil of 46.2wt. Compared to individual pyrolysis, the binary blend produced more bio-oil, suggesting a synergistic effect between hardwood and corncob. The decreased bio-oil yield of 40.1wt during co-pyrolysis of ternary blends suggests negative synergistic effects prejudiced by the volatiles available in the biomass mixture. The improved quantitative synergistic results in the co-pyrolysis process give crucial information for the development of feed-flexible, higher bio-oil production and clean operating systems. The characterization studies on bio-oil by Fourier transform-infrared spectroscopy (FTIR), gas chromatographymass spectrometry (GC-MS), and ¹H NMR spectroscopy have shown that the bio-oil is a combination of aliphatic and oxygenated compounds. The analysis of the heating value shows that the bio-oil can be utilized as a fuel for heating applications.