It is well known that working photovoltaic (PV) plants show several maintenance needs due to wiring and module degradation, mismatches, dust, and PV cell defects and faults. There are a wide range of theoretical studies as well as some laboratory tests that show how these circumstances may affect the PV production. Thus, it is mandatory to evaluate the whole PV plant performance and, then, its payback time, profitability, and environmental impact or carbon footprint. However, very few studies include a systematic procedure to quantify and supervise the real degradation effects and fault impacts on the field. In this paper, the authors first conducted a brief review of the most frequent PV faults and the degradation that can be found under real conditions of operation of PV plants. Then, they proposed and developed an innovative Geographic Information System (GIS) application to locate and supervise them. The designed tool was applied to both a large PV plant of 108 kWp and a small PV plant of 9 kWp installed on a home rooftop. For the large PV plant, 24 strings of PV modules were modelized and introduced into the GIS application and every module in the power plant was studied including voltage, current, power, series and parallel resistances, fill factor, normalized PV curve to standard test conditions (STC), thermography and visual analysis. For the small PV installation three strings of PV panels were studied identically. It must be noted that PV modules in this case included power optimizers. The precision of the study enabled the researchers to locate and supervise up to a third part of every PV cell in the system, which can be adequately georeferenced. The developed tool allows both the researchers and the investors to increase control of the PV plant performance, to lead to better planning of maintenance actuations, and to evaluate several PV module replacement strategies in a preventive maintenance program. The PV faults found include hot spots, snail tracks, ethylene vinyl acetate (EVA) discoloration, PV cell fractures, busbar discoloration, bubbles and Si discoloration.

The rapid expansion of photovoltaic (PV) installations across Mediterranean Europe since 2007 has resulted in a substantial increase in the need for end-of-life (EoL) management strategies for monocrystalline PV modules. This paper reviews the technical challenges and opportunities associated with the recycling of PV modules, focusing on the physical, chemical, and thermal processes currently employed. Despite advancements in recycling technology, significant gaps remain in infrastructure and regulatory enforcement, particularly in Mediterranean countries. The recovery of valuable materials such as silicon, silver, and glass presents both economic and environmental benefits, although the costs of recycling remain a key barrier to widespread adoption. Our analysis suggests that optimizing these recycling processes could improve their profitability and scalability, enabling more effective resource recovery. The paper concludes with recommendations for policy and infrastructure development to support the sustainable management of PV waste across the Mediterranean region.

In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn–rape blend. The ignition temperature (T_e) and burnout index (D_f) were selected as the responses to be optimised, while the CO₂/O₂ molar ratio, the total flow, and the proportion of rape in the blend were chosen as the influencing factors. For the RSM designs, complete, Box–Behnken, and central composite designs were performed to assess the experimental results. By applying the RSM, it was found that the principal effects of the three factors were statistically significant to compute both responses. Only the interactions of the factors on D_f were successfully described by the Box–Behnken model, while the complete design model was adequate to describe such interactions on both responses. The central composite design was found to be inadequate to describe the factor interactions. Nevertheless, the three methods predicted the optimal conditions properly, due to the cancellation of net positive and negative errors in the mathematical adjustment. The ANN presented the highest regression coefficient of all methods tested and needed only 20 experiments to reach the best predictions, compared with the 32 experiments needed by the best RSM method. Hence, the ANN was found to be the most efficient model, in terms of good prediction ability and a low resource requirement. Finally, the optimum point was found to be a CO₂/O₂ molar ratio of 3.3, a total flow of 108 mL/min, and 61% of rape in the biomass blend.

The promotion of the development of co-digestion power plants will be intensified in many European Union member states as the main target of the Union concerning energy generation is complete decarbonisation by 2050. This potential expansion prompts the need for optimal resources allocation according to several techno-economical parameters, highlighting energy costs, power infrastructures access, and social and environmental aspects and restrictions. In Spain, agricultural and livestock biogas production trough co-digestion power plants is still poorly deployed, although the EU Directive 2009/28/EU stipulates that energy from bio-fuels and bio-liquids should contribute to a reduction of at least 35% of greenhouse gas emissions in order to be taken into account, and many authors agree that biogas produced from energy crops and livestock waste fulfils this criterion. Moreover, biogas can be used to upgrade gas pipelines and may have other efficient thermal uses. In this paper, through a Geographical Information System approach, eight different co-digestion mixtures have been evaluated and the most profitable ones have been optimized for the Spanish Iberian Peninsula according to the geographical distribution of the resources. Furthermore, the best locations for co-digestion power plants siting have been calculated, minimizing transport costs and considering technical, environmental and social restrictions. In contrast with other studies, this proposed approach is focused on a holistic optimization. Results show that in Spain the most feasible co-digestion mixtures are based on slurry, glycerine and animal meals, and four areas arise with an outstanding energetic potential up to 208 MW exploitable in large electrical power plants, while 347 MW can be reserved for distributed generation based on this technology.

A novel approach to managing domestic heating systems through IoT technologies is introduced in this paper. The system optimizes energy consumption by dynamically adapting to electricity and fuel price fluctuations while maintaining user comfort. Integrating smart devices significantly reduce energy costs and offer a favorable payback period, positioning the solution as both sustainable and economically viable. Efficient heating management is increasingly critical amid growing energy and environmental concerns. This strategy uses IoT devices to collect real-time data on prices, consumption, and user preferences. Based on this data, the system adjusts heating settings intelligently to balance comfort and cost savings. IoT connectivity manages continuous monitoring and dynamic optimization in response to changing conditions. This study includes a real-case comparison between a conventional central heating system and an IoT-managed electric radiator setup. By applying automation rules linked to energy pricing and user habits, the system enhances energy efficiency, especially in cold climates. The economic evaluation shows that using low-cost IoT devices yields meaningful savings and achieves equipment payback within approximately three years. The results demonstrate the system’s effectiveness, demonstrating that smart, adaptive heating solutions can cut energy expenses without sacrificing comfort, while offering environmental and financial benefits.