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Item type:Publication, Artificial intelligence and the energy trilemma: A general-purpose technology perspective(Elsevier B.V., 2026-09-01)As the world transitions toward cleaner energy systems, balancing energy security, energy equity, and environmental sustainability has become a major challenge in energy policy. These three often conflicting objectives reflect the energy trilemma, a fundamental concept for understanding the trade-offs involved in achieving the Sustainable Development Goals. Scholars have raised awareness of the potential of artificial intelligence to address energy transition challenges. However, its contribution to the trilemma remains inconclusive. Using panel data from 68 countries over 2002 to 2022, our results suggest that artificial intelligence improves the energy trilemma index. However, this aggregate improvement masks important heterogeneity across its dimensions. While artificial intelligence enhances energy security and environmental sustainability, it has a negative effect on energy equity, revealing asymmetric trade-offs. Drawing on the General-Purpose Technology (GPT) framework, a mechanism analysis reveals that artificial intelligence influences the trilemma index via energy efficiency, green technology innovation, and industrial structure upgrading. These results align with the three defining characteristics of AI as a GPT: continuous improvement, fostering complementary innovation, and pervasiveness. Furthermore, the effect of artificial intelligence on the trilemma index is non-linear under different levels of governance effectiveness and investment liberalization. In particular, weak governance effectiveness generates unintended consequences, leading AI to exert a negative effect on the trilemma index. Moreover, artificial intelligence contributes most strongly to improving the trilemma index under moderate levels of investment liberalization. Overall, these findings have important policy implications for addressing the energy trilemma by integrating artificial intelligence into energy systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Review on Composite Materials for Energy Harvesting in Electric Vehicles(MDPI, 2023-04-01)The field of energy harvesting is expanding to power various devices, including electric vehicles, with energy derived from their surrounding environments. The unique mechanical and electrical qualities of composite materials make them ideal for energy harvesting applications, and they have shown tremendous promise in this area. Yet additional studies are needed to fully grasp the promise of composite materials for energy harvesting in electric vehicles. This article reviews composite materials used for energy harvesting in electric vehicles, discussing mechanical characteristics, electrical conductivity, thermal stability, and cost-effectiveness. As a bonus, it delves into using composites in piezoelectric, electromagnetic, and thermoelectric energy harvesters. The high strength-to-weight ratio provided by composite materials is a major benefit for energy harvesting. Especially important in electric vehicles, where saving weight means saving money at the pump and driving farther between charges, this quality is a boon to the field. Many composite materials and their possible uses in energy harvesting systems are discussed in the article. These composites include polymer-based composites, metal-based composites, bio-waste-based hybrid composites and cement-based composites. In addition to describing the promising applications of composite materials for energy harvesting in electric vehicles, the article delves into the obstacles that must be overcome before the technology can reach its full potential. Energy harvesting devices could be more effective and reliable if composite materials were cheaper and less prone to damage. Further study is also required to determine the durability and dependability of composite materials for use in energy harvesting. However, composite materials show promise for energy harvesting in E.V.s. Further study and development are required before their full potential can be realized. This article discusses the significant challenges and potential for future research and development in composite materials for energy harvesting in electric vehicles. It thoroughly evaluates the latest advances and trends in this field. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimizing Thermal Comfort and Life Cycle Cost in High-Altitude Rural Housing Using NSGA-II and EnergyPlus(Multidisciplinary Digital Publishing Institute (MDPI), 2026-06-01)Improving indoor thermal comfort in high-altitude rural housing remains a persistent challenge for low-income communities in the Peruvian Andes. This study evaluates the thermal performance of a standardized Sumaq Wasi modular dwelling in Langui (Cusco, Peru, 3969 m.a.s.l.) and proposes passive envelope modifications that enhance comfort while preserving economic feasibility. A multi-objective optimization approach combining EnergyPlus simulations with the NSGA-II algorithm was applied to minimize total thermal discomfort (TDItotal), bedroom underheating (TDIUbedrooms), and 10-year life cycle costs (LCC). The calibrated model incorporated field measurements of indoor air temperatures. Global sensitivity analysis using Morris and Sobol methods identified ceiling thermal transmittance as the dominant contributor for TDItotal, and exterior wall solar absorptance as the driver of TDIUbedrooms. Optimization reduced TDItotal and TDIUbedrooms to 22% and 8% of the base case, requiring additional investments of USD 2347 and USD 1959, respectively, above the base case cost (USD 8100). Cost-neutral strategies, raising exterior wall solar absorptance to 0.9 and increasing the skylight-to-roof ratio (13.1%), reduced bedroom underheating to 30% of the base case and outperformed a scenario with two 400 W electric heaters. These results demonstrate that context-appropriate passive design can substantially improve comfort under severe climatic and financial constraints. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling energy efficiency in industrial plants: A novel diagnostic approach(Elsevier Ltd, 2025-02-15)In industrial plants, diagnosing energy efficiency issues is essential to achieve sustainable operations and reduce costs. This paper introduces a novel diagnostic approach using advanced modeling techniques to identify inefficiencies in energy consumption within industrial environments. The proposed method uses discrete event analysis to detect and characterize abnormal energy usage patterns, providing a systematic framework for diagnosing performance issues in complex systems. Two case studies involving high-performance computing (HPC) systems illustrate the practical application of the approach, showcasing its ability to uncover critical inefficiencies and inform energy management strategies. The research addresses a significant gap in current methodologies by providing a detailed diagnostic tool customized to the unique challenges of industrial energy management. This study paves the way for future research into advanced diagnostic techniques, strengthening the importance of precise and actionable information on energy use for industrial stakeholders.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mathematical Model to Improve Energy Efficiency in Hammer Mills and Its Use in the Feed Industry: Analysis and Validation in a Case Study in Cuba(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-01)The feed industry is characterized by high energy consumption during the grinding stage, where hammer mills can account for up to 50% of total electricity usage; furthermore, efficiency analyses are based only on the classical equations reported in the literature. In this context, the present theoretical-applied research aimed to improve the efficiency of a plant operating below its nominal capacity. To achieve this, a comprehensive mathematical model was developed, integrating power and grain disintegration equations while overcoming the limitations of classical comminution theories. The model incorporates key factors such as feed rate, moisture content, absorbed power and hammer wear. Additionally, specific correction factors for temperature (Kt) and mechanical degradation (Kd) were introduced to accurately represent real operating conditions. The study was based on extensive measurements of electrical current, power factor, energy consumption, particle size distribution and thermal variations under different load conditions. The statistical analysis, which included ANOVA, ANCOVA and multiple regressions, demonstrated a predictive accuracy of 98% (R2) and a pseudo-R2 of 89%. This high correlation allowed for an 18% reduction in energy consumption equivalent to 4 kWh/t and up to a 30% improvement in particle size uniformity, surpassing typical factory performance. The findings highlight that integrating operational, thermodynamic and wear-related factors enhances the robustness of the model, promoting more reliable energy-management practices in hammer mills. Consequently, the results confirm that the developed model serves as a scientifically robust, efficient and applicable tool for improving energy efficiency and reducing environmental impacts in the agri-food industry.1