Grup de Recerca en Energia i Intel·ligència Artificial (GREiA) (INSPIRES)
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The GREiA research group (Research group in energy and artificial intelligence) is born from the union of the research group in energy GREA and the research group in artificial intelligence IA. The collaboration of these two groups begins in 2014.
The general line of research that defines the activity of the group is to provide answers and solutions related to the fields of energy engineering, industrial and construction design, sustainability and intelligence artificial. [Més informació]
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Recent Submissions
- ItemOpen AccessMSW incineration bottom ash-based alkali-activated binders as an eco-efficient alternative for urban furniture and paving: Closing the loop towards sustainable construction(MDPI, 2025) Chimenos Ribera, Josep Ma.; Cuspoca, Fabian; Maldonado-Alameda, Alex; Mañosa, Jofre; Rosell Polo, Joan Ramon; Andrés, Ana; Faneca, Gerard; Cabeza, Luisa F.Innovative approaches in the Portland cement industry, aligned with circular economy principles, offer a promising solution to reduce the environmental impacts. These methods can initially target the architectural elements with lower structural demands, such as urban furniture and paving, before being applied to areas with higher cement usage. Alkali-activated binders (AABs) made from secondary resources present a sustainable alternative to Portland cement (PC), promoting resource recovery, conservation, and a low-carbon economy. Incinerator bottom ash (IBA), traditionally landfilled, has shown potential as a precursor for AABs due to its aluminosilicate content. Repurposing IBA for urban furniture and paving transforms it into a valuable secondary resource. Accordingly, this is the first study to utilize IBA as the sole precursor for urban furniture or paving applications. Research, including state-of-the-art studies and proof of concept developed in this work, demonstrates that IBA-based AABs can produce cast concrete suitable for non-structural urban elements, meeting the technical, environmental, and ecotoxicological standards. Using IBA in AAB formulations not only reduces the reliance on primary raw materials but also contributes to significant energy savings in binder production and lowers greenhouse gas (GHG) emissions, resulting in a reduced carbon footprint. Furthermore, producing concrete from local residual resources, such as IBA, facilitates the reintegration of municipal waste into the production cycle at its point of origin, fostering a sustainable approach to urban development and supporting the circular economy.
- ItemOpen AccessNumerical analysis of an optimal metal wool-phase change material for thermal energy storage with exceptionally high power density(Elsevier, 2025) Ribezzo, Alessandro; Morciano, Matteo; Zsembinszki, Gabriel; Mani Kala, Saranprabhu; Borri, Emiliano; Bergamasco, Luca; Fasano, Matteo; Chiavazzo, Eliodoro; Prieto, Cristina; Cabeza, Luisa F.The adoption of thermal energy storage (TES) systems based on phase change material (PCM) remains limited by their low thermal conductivity, which restricts power density. Existing heat transfer enhancement techniques are often costly or come with significant drawbacks, leaving a gap for an effective and affordable solution. This study highlights metal wool as a promising alternative, offering low cost, ease of application, and retrofitting potential. While previous experiments demonstrated substantial improvements in power density using copper wool, a comprehensive numerical model to further optimize this technique is presented here. The model, incorporating CFD simulations and uncertainty analysis, was validated for bulk PCM and two copper wool-PCM composites before being extended to a wool material analysis. First, possible alternatives to copper as wool material were tested, highlighting aluminum as a viable candidate. Then, the proposed composite was found to match the discharging performance of a PCM with an effective thermal conductivity of 2.5 W/mK, a value rarely achieved by conventional enhancement techniques. Additionally, a techno-economic comparison revealed that copper wool delivered a 14.7-fold increase in thermal conductivity relative to liquid PCM at ¿6 per kg of PCM additivated¿a performance unmet by metal foams and nanocomposites. These findings confirm metal wool as a viable cost-effective and high-performance solution for improving TES systems, partially bridging the gap between efficiency and affordability.
- ItemOpen AccessCompatibility of carbonate mixtures to be used as molten salts with different metal alloys to be used as container materials(2025) Cabeza, Luisa F.; Martínez, Franklin R.; Borri, EmilianoThe energy transition can only be achieved if the global energy sector is transformed from a fossil-based system to a zero-carbon-based source system. To achieve this aim, two technologies have shown promising advances in high-temperature application. Concentrating solar power (CSP) plants are seen as a key technology to achieve the needed energy transition, and carbon dioxide (CO2) capture and storage (CCS) is a promising technology for decarbonizing the industrial sector. To implement both technologies, molten carbonate salts are considered promising material. However, their corrosive behavior needs to be evaluated, especially at high temperatures, where corrosion is more aggressive in metal structures. This paper presents an experimental evaluation of the static corrosion of two molten carbonate salts, a Li2CO3-Na2CO3-K2CO3-LiOH·H2O (56.65-12.19-26.66-4.51wt.%) mixture and a Li2CO3 salt, under an air atmosphere with five corrosion-resistant metal alloys, including Alloy 600, Alloy 601, Alloy 625, Alloy 214, and Alloy X1. In this study, the corrosion rate and mass losses were quantified. In addition, in all the cases, the results of the experimental evaluation showed corrosion rate values between 0.0009 mg/cm2·yr and 0.0089 mg/cm2·yr.
- ItemOpen AccessOptimizing the design of TES tanks for thermal energy storage applications an integrated biomimetic-genetic algorithm approach(2025) Mehraj, Nadiya; Mateu Piñol, Carles; Zsembinszki, Gabriel; Cabeza, Luisa F.Building upon an experimentally validated bio-inspired thermal energy storage (TES) tank design, this study introduced a novel computational framework that integrated genetic algorithms (GA) with biomimetic principles to systematically generate TES tank geometries. Inspired by natural thermal distribution patterns found in vascular networks, the AI-driven methodology explored 13 geometric parameters, focusing on branching structures and spatial distribution, and resulted in computationally generated designs with a 29% increase in heat transfer surface area while maintaining manufacturability constraints within a fixed tank diameter of 150 mm and height of 155 mm. Unlike previous biomimetic TES studies that relied on predefined geometric configurations, this approach developed AI-driven bio-inspired structures within experimentally validated dimensional constraints, ensuring geometric relevance while allowing for broader structural exploration. The resulting designs exhibited key characteristics of high-efficiency bio-inspired configurations while providing a systematic, scalable methodology for TES tank architecture. This study represented the first step in integrating AI-driven biomimicry into TES tank design, establishing a structured framework for generating high-performance, manufacturable configurations. While the current work focused on computational design, future research will emphasize experimental validation and real-world implementation to confirm the practical thermal and structural benefits of these AI-generated bio-inspired designs. By bridging the gap between computational intelligence and nature-inspired engineering, this research provided a scalable pathway for developing more efficient, manufacturable, and sustainable TES solutions for energy storage applications.
- ItemOpen AccessModeling of TES Tanks by Means of CFD Simulation Using Neural Networks(MDPI, 2025) Rojas Cala, Edgar F.; Béjar Torres, Ramón; Mateu Piñol, Carles; Borri, Emiliano; Romagnoli, Alessandro; Cabeza, Luisa F.Modeling of thermal energy storage (TES) tanks with computational fluid dynamics (CFD) tools exhibits limitations that hinder the time, scalability, and standardization of the procedure. In this study, an innovative technique is proposed to overcome the challenges in CFD modeling of TES tanks. This study assessed the feasibility of employing neural networks for TES tank modeling, evaluating the similarities in terms of structure and signal-to-noise ratio by comparing images generated by neural networks with those produced through CFD simulations. The results regarding the structural similarity index indicate that around 94% of the images obtained have a similarity index above 0.9. For the signal-to-noise ratio, the results indicate a mean value of 25 dB, which can be considered acceptable, although indicating room for improvement. Additional results show that our neural network model obtains the best performance when working with initial states close to the stable phase of the TES tank. The results obtained in this study are promising, laying the groundwork for a future pathway that could potentially replace the current methods used for TES tank modeling.