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This master thesis explores the integration of Artificial Intelligence (AI) in Environmental, Social, and Governance (ESG) reporting, focusing on its potential to enhance the accuracy, transparency, and efficiency of ESG disclosures. It investigates how AI can transform ESG reporting from a reactive to a proactive tool, enabling companies to better assess and mitigate ESG-related risks. The study examines the perceptions of industry professionals regarding AI's role in ESG, considering factors such as age, education, and professional background. It highlights the significant impact AI can have on sectors like finance, energy, and healthcare, while addressing challenges and ethical concerns. The findings suggest that AI can be a powerful enabler of ESG transparency, but its adoption requires sector-specific adaptations and careful ethical considerations. Future research is recommended to explore long-term effects and deeper ethical issues in AI-driven ESG reporting. Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura Objectius de Desenvolupament Sostenible::16 - Pau, Justícia i Institucions Sòlides

Thermo-economically optimal design = optimal molecule + optimal process + optimal equipment.

Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.6 - Per a 2030, reduir l’impacte ambiental negatiu per capita de les ciutats, amb especial atenció a la qualitat de l’aire, així com a la gestió dels residus municipals i d’altre tipus 2n Premi Domènec Valero 2024 Award-winning

This paper presents a stochastic bi-level approach for tariff design in the electricity market where the leader is represented by a retailer and the follower by a residential prosumager, i.e. a consumer equipped with an energy system consisting of photovoltaic panels and a battery storage device. Both players solve an optimization problem subject to uncertainty in market prices, weather-related variables and electricity demand. To account for the retailer’s attitude towards risk, the upper level problem includes a safety measure to maximize. The model allows to determine a dynamic pricing scheme with time-variant rates delivering the average profit that can be gained in a given percentage of unfavorable realizations of the uncertain parameters and the optimal load pattern that minimizes the expected prosumager's electricity bill. The stochastic bi-level problem is reformulated as a single level model and different approaches to deal with the lower level problem and the non linearity in the objective function are analyzed and empirically investigated. A large number of numerical experiments have been carried out on real test cases. The results have shown the efficacy of the proposed approach as a tool to define pricing schemes that reflect the retailer's risk attitude underlining the importance of explicitly dealing with uncertainty.

The opportunities and challenges of applying micronutrients (MiNs) in full-scale anaerobic digestion (AD) plants has been reviewed. The review discusses the underlying mechanisms and the role of different micronutrients (Fe, Ni, Co, Mo, Zn, Cu, Se) in the enhancement of AD performance, as well as their environmental and economic implications in full-scale AD systems. Bioavailability is a key factor affecting the effectiveness of micronutrients application on the biochemical aspects of AD. Accordingly, the technical aspects of AD with a direct impact on bioavailability have been identified and critically addressed. Mono-supplementation is not the most favorable strategy to increase micronutrient bioavailability due to limited solubility, formation of insoluble compounds, interaction with other compounds, and specific microbial requirements. Nonetheless, co-supplementation can increase the bioavailability due to the simultaneous synergetic effects of co-micronutrients supplementation on the biochemical aspects of AD. However, the inconsistency of reported lab-scale results and the lack of protocols or guidelines for analyzing the bioavailability of micronutrients limit results interpretation and full-scale application. The environmental and economic implications of these micronutrients are other critical factors that need further research. The economic results showed that the mono-supplementation can be economically favorable when a methane enhancement of 20% is achieved. Co-supplementation of micronutrients is the most economically feasible option since this strategy allows reducing the total dosage of micronutrients when compared with mono-supplementation. The authors are grateful for the scholarship from the TUM SEED Center of the Technical University of Munich, which is part of the DAAD (German Academic Exchange Service) program “exceed” supported by DAAD as well as the German Federal Ministry for Economic Cooperation and Development (BMZ) and in cooperation with the hosting Chair of Urban Water System Engineering of TUM. Sergi Astals is thankful to the Spanish Ministry of Science, Innovation and Universities for his Ramon y Cajal. Peer Reviewed

Pump as turbine (PAT) is a common method of energy recovery, however, vortices are a negative phenom for these units. The objective of this research is to study the effect of vortex motion on the hydraulic loss of pump as turbine, and establishing the correlation mechanism between vortex intensity and turbulence loss. The research method adopts theoretical analysis and model test and numerical simulation. Based on the entropy production theory, the hydraulic loss and the turbulent dissipation in boundary layer induced by vortex motion are studied, revealing the influence of vortices on the energy loss. Results show that the vortex motion can be decomposed into a synchronous component v_sy and a rotational component v_ro, among them, the rotational component v_ro meets to Biot-Savart Law. The turbulent dissipation rate in the boundary layer is closely to the vortex motion, which can characterize the boundary turbulence height. Turbulent flow induced by vortex can propagate in the flow channel of the unit, causing lot of additional hydraulic loss. In the end, a mathematical model between entropy production (Sk) induced by vortex and vortex strength (¿k) was established, indicating that Sk changes with ¿k in the form of a quadratic function.

The primary objective of this paper is to develop a hybrid grey box model that integrates air and thermal dynamics to improve accuracy in both domains. The methodology involved developing four grey box models to estimate ventilation airflows using indoor CO2 concentration data and six thermal models to estimate thermal properties and heat gains using indoor temperature data. To ensure accurate parameterization, measurements of outdoor conditions, occupancy, and HVAC operations were incorporated. The results revealed that models treating infiltration and mechanical ventilation as mutually exclusive (IAQ-3 and IAQ-4) and those integrating ventilation heat gains from estimated airflows (T-6) performed most effectively. This hybrid approach underscores the benefits of incorporating ventilation heat loos or gains, based on airflow estimation derived from indoor air quality (IAQ) models, into thermal modelling, significantly improving accuracy and reducing parameter variability. The findings demonstrate the potential of this methodology for applications in ventilation management and HVAC optimization. By enhancing energy efficiency and improving indoor air quality, this approach supports the development of healthier, more sustainable indoor environments. This research was supported by a predoctoral contract grant (reference no. PRE2021-099606) as part of the research and development project IAQ4EDU (reference no. PID2020-117366RB-I00), funded by MCIN/AEI/10.13039/501100011033/FEDER, and is part of the project BINAFET (reference no. TED2021-130047B-C22), funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR. Moreover, this study was supported by the Catalan agency AGAUR through its research group support programme (2021 SGR 00341).

AbstractTomato residues are a form of solid waste that can be converted into methane through anaerobic digestion (AD). However, methane production is often limited due to incomplete hydrolysis caused by the high lignocellulosic content of tomato waste. Enzymatic pretreatments represent a promising approach to enhance methane yields by facilitating substrate hydrolysis. This study evaluated four commercial enzymatic blends – Celluclast 1.5 L, Maxoliva HC L, Viscozyme, and Novozym 435 – using biomethane potential (BMP) tests with two operational strategies: (i) preincubation of enzymes with tomato waste prior to AD, and (ii) direct addition of enzymes to the anaerobic digester. Maxoliva achieved the highest methane yield (348 ± 20 mL CH4 g−1 volatile solids (VS)) under preincubation, representing 99.5% of the theoretical BMP and a 90% increase in comparison with the control. Kinetic analysis using the modified Gompertz equation revealed that Maxoliva also exhibited the highest maximum methane production rate (RMAX = 5.5 ± 0.2 mL CH4 g−1 VS day−1) with direct addition. Conversely, Viscozyme showed limited effectiveness, reaching only 47% of the theoretical BMP value. The enhanced methane production observed with certain enzymatic blends is likely attributable to cellulase activity, which facilitates the breakdown of complex carbohydrates into easily biodegradable polysaccharides.