Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era of remarkable research and innovation. This review article thoroughly examines the recent advancements in the field, focusing on the interplay between PV systems and water desalination within the framework of AI and ML applications, along with it analyses current research to identify significant patterns, obstacles, and prospects in this interdisciplinary field. Furthermore, review examines the incorporation of AI and ML methods in improving the performance of PV systems. This includes raising their efficiency, implementing predictive maintenance strategies, and enabling real-time monitoring. It also explores the transformative influence of intelligent algorithms on desalination techniques, specifically addressing concerns pertaining to energy usage, scalability, and environmental sustainability. This article provides a thorough analysis of the current literature, identifying areas where research is lacking and suggesting potential future avenues for investigation. These advancements have resulted in increased efficiency, decreased expenses, and improved sustainability of PV system. By utilizing artificial intelligence technologies, freshwater productivity can increase by 10 % and efficiency. This review offers significant and informative perspectives for researchers, engineers, and policymakers involved in renewable energy and water technology. It sheds light on the latest advancements in photovoltaic systems and desalination, which are facilitated by AI and ML. The review aims to guide towards a more sustainable and technologically advanced future.

The food industry is responsible for supplying the food demand of the ever-increasing global population. The food chain is one of the major contributors to greenhouse gas (GHG) emissions, and global food waste accounts for one-third of produced food. A solution to this problem is preserving crops, vegetables, and fruits with the help of an ancient method of sun drying. For drying agricultural and marine products, several types of dryers are also being developed. However, they require a large amount of energy supplied conventionally from pollutant energy sources. The environmental concerns and depletion risks of fossil fuels persuade researchers and developers to seek alternative solutions. To perform drying applications, sustainable solar power may be effective because it is highly accessible in most regions of the world. Greenhouse dryers (GHDs) are simple facilities that can provide large capacities for drying agricultural products. This study reviews the integration of GHDs with different solar technologies, including photovoltaic (PV), photovoltaic-thermal (PVT), and solar thermal collectors. Additionally, the integration of solar-assisted greenhouse dryers (SGHDs) with heat pumps and thermal energy storage (TES) units, as well as their hybrid configuration considering integration with other renewable energy sources, is investigated to improve their thermal performance. In this regard, this review presents and discusses the most recent advances in this field. Additionally, the economic analysis of SGHDs is presented as a key factor to make these sustainable facilities commercially available.

The present investigation represents the effect of heat storage material on the performance of concave type stepped solar still. The performance of proposed still is tested by varying the weight proportion of heat storage materials. The experiment was conducted initially without any heat-storing material and thereafter with bricks, sand and concrete pieces with an objective to enhance the productivity of solar still during night hours. The present research reveals 12 kg of concrete pieces provides higher distillate output of 4062 ml/day including 435 ml during night hours. Concrete pieces are found to be apt heat-storing material as its performance is 11.16% & 7.37% higher than that of brick and sand pieces, respectively. The highest overall efficiency of 28.27% is achieved with concrete pieces. This paper is addressed to the investigators involved in the field of heat storage materials and desalination technology.

Machine Learning approaches from the present state either use unimodal data, unable to model elegant long spatial-temporal dependencies in warning systems or create early warning response datasets with limited quantitative interpretability sets. To address these shortcomings, this work introduces T-RUNSAFE, a multi-pronged, machine learning-based predictive prototype for thermal runaway assessment. The framework integrates five specialized modules: (1) ST-Former, a spatiotemporal transformer that encodes thermal gradients from thermal images and sensor logs using temporal self-attention over LSTMs, thus is superior to traditional LSTMs for capturing evolving thermal patterns; (2) FUSE-GEN, adversarial trained dual-encoder variational autoencoder, fusing acoustic emission (AE) signals and thermal embeddings into a shared latent space for early-stage internal degradation detection; (3) DEGRA-GNN, a graph attention network that capitalizes on battery electrode topology to model the spatial propagation of thermal faults; (4) CAUS-RUN, a counterfactual simulation engine employing structural causal models to attribute risk to specific spatial zones for interpretability; and (5) SENSOR-RL, a reinforcement learning module optimizing sensor sampling policies on real-time risk levels that cuts down on sensor power while still holding to detection accuracy. The experimental results show great early prediction accuracy (AUC-ROC > 0.96), high spatial degradation localization accuracy (93.5%), and a 37% decrease in power consumption of sensing. T-RUNSAFE predicts, interprets, and optimizes resource utilization for thermal runaway risk assessment. By integrating deep learning, physics-informed modeling, and causal reasoning, it enables real-time battery safety monitoring. Although challenges remain regarding sensor cost, computational overhead, and chemistry generalization, the study demonstrates the feasibility of advanced onboard battery management systems tailored for next-generation energy applications.