Waste-to-energy conversion via pyrolysis has attracted increasing attention recently owing to its multiple uses. Among the products of this process, biochar stands out for its versatility, with its yield influenced by various factors. Extensive and labor-intensive experimental testing is sometimes necessary to properly grasp the output distribution from various feedstocks. Nonetheless, data-driven predictive models using large-scale historical experiment records can provide insightful analysis of projected yields from a variety of biomass materials, hence overcoming the challenges of empirical modeling. As such, five modern approaches available in modern machine learning are employed in this study to develop the biochar yield prediction models. The Lasso regression, Tweedie regression, random forest, XGBoost, and Gradient boosting regression were employed. Out of these five XGBoost was superior with a training mean squared error (MSE) of 1.17 and a test MSE of 2.94. The XGBoost-based biochar yield model shows excellent performance with a strong predictive accuracy of the R ² values as 0.9739 (training) and 0.8875 (test). The mean absolute percentage error value was only 2.14% in the training phase and 3.8% in the testing phase. Precision prognostic technologies have broad effects on sectors including biomass logistics, conversion technologies, and effective biomass utilization as renewable energy. Leveraging SHAP based on cooperative game theory, the study shows that while ash and moisture lower biochar yield, FPT, nitrogen, and carbon content significantly boost it. Small variables like heating rate and volatile matter have a secondary impact on production efficiency.

The Proton Exchange Membrane Fuel Cell (PEMFC) is a highly efficient and eco-friendly technology, making it a pivotal solution for sustainable energy systems. Effective thermal management of PEMFCs is essential, and nanofluids have emerged as superior coolants compared to conventional fluids. Less exploration in PEMFC cooling, particularly using reduced graphene oxide (rGO) suspended hybrid nanofluids, supports the present work on the thermal and rheological properties of rGO-based hybrid nanofluids. The experimental exploration involves five different mixtures of base fluid composition comprising ethylene glycol (EG) and water (W). The hybridization of Al₂O₃ and rGO nanoparticles was performed by dispersing both at four different concentrations in the 50:50 base fluid mixture. The experimental procedure involves evaluation of dispersion stability, viscosity, and thermal conductivity of hybrid nanofluids. The results showed that increasing the EG proportion reduced thermal conductivity while increasing viscosity. The maximum thermal conductivity ratio of 1.23 occurred at 80:20 W: EG for 1 vol% concentration at 60 °C, while the highest viscosity ratio of 1.48 was observed at 20:80 W: EG at 30 °C. The developed correlation for viscosity shows an 11.2% reduction in the coefficient of determination obtained for the thermal conductivity model. This study explores the application of Linear Regression (LR), Decision Tree (DT), and eXtreme Gradient Boosting (XGBoost) models for predicting thermal conductivity and viscosity using experimental datasets. The thermal conductivity model showed that XGBoost has the best predictive power, with Test R² = 0.9941, Test mean square error (MSE) = 0.0000, and Test KGE = 0.9613. XGBoost again beat other models in predicting viscosity, with Test R² = 0.9944, Test MSE = 0.0269, and Test KGE = 0.9903. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) graphs showed that the model outputs were greatly affected by the base fluid ratio (BFR), temperature, and concentration. This made the model outputs easy to understand both globally and locally. These findings provide valuable insights for designing efficient cooling solutions for PEMFCs, supporting their broader adoption in energy applications.

This study explores the thermal behavior of hybrid nanofluids (HNFs) composed of water mixed with equal proportions (50:50) of Al₂O₃ and CuO nanoparticles (NPs) under turbulent flow regimes. The nanofluids (NFs) are prepared in the volume concentrations range of 0–1%. Both experimental investigations and numerical simulations were carried out to evaluate the effects of NP concentration and Reynolds number (Re) on Nusselt number (Nu), friction factor, and entropy generation. Results demonstrated a marked enhancement in heat transfer with increasing NP concentration and flow rate. Notably, the use of HNFs led to a 71% reduction in total entropy generation (TEG) compared to water alone. Empirical correlations were developed to predict the Nu and friction factor accurately. Furthermore, an XGBoost machine learning model was employed to estimate thermal parameters with high precision. The model achieved an R² of 1.000 (training) and 0.991 (testing) with an MSE of 0.001 for TEG. For the friction factor, R² _training as 0.686 and R² _test as 0.916 (testing) were obtained. Nu model achieved perfect training accuracy (R² = 1.000) and strong testing performance (R² = 0.975, MSE = 29.457). These results affirm the effectiveness of XGBoost in modeling thermofluidic behavior in HNF systems.

Water scarcity is intensifying worldwide due to climate change and poor resource management, while rising sea levels threaten coastal regions and ecosystems. Addressing these interconnected challenges requires sustainable solutions to secure freshwater supplies and protect vulnerable communities. Solar-powered desalination has emerged as a promising approach for converting seawater and brackish water into potable water. This review critically examines recent advancements in solar desalination technologies, focusing on improvements in performance, efficiency, and sustainability. Solar desalination systems offer a clean, noise-free, and cost-effective alternative to conventional methods, particularly in arid and remote regions. The study highlights the benefits of integrating waste heat into desalination processes, which can reduce costs and enhance overall efficiency. Additionally, the review finds that incorporating batteries into photovoltaic (PV) reverse osmosis (RO) desalination is impractical due to high capital and maintenance costs. However, energy recovery devices eliminate the need for preheating feed water, particularly in PV RO systems. Currently, most operational RO plants rely on PV energy, while solar thermal desalination – using parabolic trough collectors (PTCs) with organic Rankine cycle (ORC) technology – remains in the research phase. Although the PTC–ORC–RO system presents a promising solution, large-scale implementation has yet to be realised.

Efficient thermal management of high‐power lithium‐ion batteries (LiBs) is critical for ensuring safety, longevity, and performance in electric vehicles (EVs). Battery thermal management systems (BTMS) play a crucial role in regulating temperature, as LiBs are highly sensitive to thermal fluctuations. Excessive heat generation during charging and discharging can degrade battery performance, reduce lifespan, and pose safety risks. Traditional cooling methods, such as air and liquid cooling, often require additional power and complex components, making them less effective for high‐energy–density batteries. As a result, recent advancements focus on immersion, indirect, and hybrid cooling solutions. Among these, phase change material (PCM)‐based BTMS has emerged as a promising passive cooling approach. PCMs efficiently absorb and store heat, maintaining optimal battery temperature without external power. Their thermal performance is further enhanced by integrating expanded graphite (EG) fillers, metal foams, or fins, improving heat dissipation. This review examines recent progress (2019–2024) in BTMS technologies, with a particular focus on PCM applications in fast‐charging conditions. It also discusses BTMS performance under extreme environments, such as high temperatures, sub‐zero conditions, and abuse scenarios. Future research directions are highlighted to optimize BTMS for next‐generation EVs, ensuring improved battery safety, efficiency, and thermal stability.