To overcome the challenge of solar intermittency (a major constraint in drying operations that limits activities to daylight hours and compromises efficiency and product quality), this paper presents a critical review of solar dryers combined with thermal energy storage systems, focusing on sensible heat storage (SHS) and latent heat storage (LHS) technologies. Experimental and numerical literature published from 2016 to 2026 is synthesized and classified into SHS, LHS, and hybrid systems, comparing primary performance indicators, including drying time, efficiency, specific moisture extraction rate, and economic feasibility. Key findings indicate that SHS systems using materials such as pebbles, sand, or granite enhance drying efficiency by 2%–28%, reduce drying time by up to 30% compared with open‐sun drying, and offer payback periods (PBPs) as short as 2–2.12 months. LHS systems using phase change materials (e.g., paraffin wax and lauric acid) extend drying by 3–5 h after sunset, achieve exergy efficiency up to 98.1%, and save 36–70.5 h of drying time, though with higher initial costs. Hybrid SHS–LHS systems show synergistic advantages, including 10%–14.2% improvements in drying efficiency, PBPs of 0.39–1.82 years, and improved product quality and nutrient retention. The review concludes that SHS is more beneficial for low‐cost, low‐temperature applications, whereas LHS provides superior temperature stability and extended heat storage, making it suitable for high‐value or continuous drying processes. A comprehensive mathematical modeling framework and meta‐analysis of cost‐effectiveness are also presented to guide system selection. Future work should focus on material properties, system design optimization, and cost‐efficiency to enhance adoption in sustainable agricultural and industrial drying sectors.
F. Rashid, Karrar A. Hammoodi, Najah M. L. Al Maimuri et al.· Heat Transfer· 0 citations
This review explores emerging smart technics in enhancing energy efficiency in commercial and residential buildings using systematic and bibliometric approaches from 1990 to 2024. According to the findings, the increase in internet of things applications, like AI and especially machine-learning applications, has enabled smarter buildings in recent years. The advancements of modern data analytics, predictive modelling, and real-time monitoring create a fair base for advancing into new paradigms for energy management. Energy yield prediction and building performance enhancements are ensured through machine-learning techniques, such as ensemble learning, neural networks, and support vector regression. The study found that deep reinforcement learning and fuzzy logic constitute those technologies that automate the consumption behaviors while perfectly balancing efficiency and comfort of occupants. According to the results, smart technologies offer better options toward energy efficiency but encounter major hurdles like poor internet availability, social acceptance, regulatory issues, high upfront cost, scaling issues, and data privacy. Real-time data coupled with smart technology systems should be combined to develop hybrid machine-learning models and predictive energy consumption models. For the attainment of energy efficiency goals, standardization of energy-efficient buildings and greening people's energy practices are key.
E. B. Agyekum, B. Tarawneh, S. Praveenkumar et al.· Energy Exploration & Exp...· 0 citations