Marco Fossa

This study presents a quasi-steady-state performance assessment of a solar Linear Fresnel Collector (LFC) for a laboratory-scale Multi-Stage Flash (MSF) desalination system under the climatic conditions of Bou-Ismail, Algeria, using Typical Meteorological Year (TMY) data. A literature-verified MATLAB (release R2025a) model is developed to evaluate the collector performance of the LFC and its integration with the MSF unit. The solar field is represented using a prototype-specific incidence angle modifier (IAM) correlation and real climatic data from the PVGIS-SARAH-2 database, enabling hourly simulations under realistic operating conditions. The LFC achieves a peak overall efficiency of 20.5%, with an annual-average total efficiency of 4.6%. Based on the annual thermal demand of the laboratory-scale MSF unit, a solar field comprising 11 identical LFC modules with a total aperture area of 198 m² is required. Under these conditions, the system produces 92.52 m³/year of freshwater, corresponding to a calendar-average production of 253.47 kg/day. The annual specific thermal energy consumption is 198.30 kWh/m³. The results demonstrate the technical feasibility of coupling Linear Fresnel Collector technology with MSF desalination under realistic climatic conditions and provide a basis for the future development of solar-driven desalination systems in arid regions.

The growing deployment of photovoltaic (PV) systems worldwide has amplified the need for efficient, non-invasive diagnostic techniques to monitor their performance and ensure long-term reliability. Infrared (IR) thermography has emerged as a powerful tool for detecting thermal anomalies such as hotspots, cell mismatches, shading effects, and degradation in PV modules under real operating conditions. This review presents a comprehensive overview of recent advancements in thermographic analysis applied to PV diagnostics. It discusses the principles of thermal imaging, imaging protocols, and data interpretation techniques, alongside common thermal defects encountered in field and laboratory settings. Furthermore, the integration of irradiance mapping, drone-assisted surveys, and AI-based image analysis is examined for enhancing detection accuracy and scalability. The review also highlights standardization challenges, environmental influences, and emerging trends in automation and predictive maintenance. By consolidating current research, this study underscores the critical role of thermography in optimizing PV performance, reducing maintenance costs, and supporting the transition to smarter, more resilient solar energy infrastructures.

The goal of maximizing the optical efficiency of a Linear Fresnel Collector is typically addressed through Monte Carlo Ray Tracing simulations. This study introduces FresnelSim, a 3D ray tracing software developed at the University of Genoa, Italy, for the optical analysis of Linear Fresnel Collectors. FresnelSim has been herein used to examine key geometric factors and their impact on a reference plant's producibility, allowing to almost triple the irradiance at the secondary concentrator with respect to a reference base configuration. Through a series of fast parametric simulations, an optimized geometry for such a system is proposed, highlighting to which extent different efficiency indicators are dependent on mirrors’ gap, field length and mirrors’ radius of curvature. Then, an assessment of the effect of the plant’s azimuthal orientation in the range from 0° to 45° is presented, showing that the site’s yearly average producibility is reduced up to 3.3% as an effect of primary mirror alignment. Finally, an analysis of the Compound Parabolic Collector is presented in terms of sub hourly opto-energy efficiency for 3 reference days, resulting in values ranging from 80.9% to 87.4%. Energy flux circumferential uniformity at the absorber tube is evaluated under 6 different incidence angles, highlighting that irradiance uniformity could be further enhanced by a properly defined mirrors’ motion law.

This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms (active, passive, semi-passive, manual, and chronological), and control strategies (open-loop, closed-loop, hybrid, and AI-based). Fixed-tilt PV systems serve as a baseline, with single-axis trackers achieving 20–35% higher energy yield, and dual-axis trackers offering energy gains ranging from 30% to 45% depending on geographic and climatic conditions. In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. Sensor technologies such as LDRs, UV sensors, and fiber-optic sensors are compared in terms of precision and environmental adaptability, while microcontroller platforms—including Arduino, ATmega, and PLC-based controllers—are evaluated for their scalability and application scope. Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. The review also emphasizes adaptive tracking strategies for backtracking, high-latitude conditions, and cloudy weather, alongside emerging applications in agrivoltaics, where solar tracking not only enhances energy capture but also improves shading control, crop productivity, and rainwater distribution. The findings underscore the importance of selecting appropriate tracking strategies based on site-specific factors, economic constraints, and climatic conditions, while highlighting the central role of solar tracking technologies in achieving greater solar penetration and supporting global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action).

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.