Search Results (106)

The usage of fossil fuels has resulted in increasingly severe environmental problems, such as climate change, air pollution, water pollution, etc. Hydrogen energy is considered one of the most promising clean energies to replace fossil fuels due to its pollution-free and high-heat properties. However, the oxygen evolution reaction (OER) remains a critical challenge due to its high overpotential and slow kinetics during water electrolysis for hydrogen production. Electrocatalysts play an important role in lowering the overpotential of OER and promoting the kinetics. Co₃O₄-based electrocatalysts have emerged as promising candidates for the oxygen evolution reaction (OER) due to their favorable catalytic activity and good compatibility compared with precious metal-based electrocatalysts. This review presents a summary of the recent developments in the synthesis strategies and mechanisms of Co₃O₄-based electrocatalysts for the OER. Various synthesis strategies have been explored to control the size, morphology, and composition of Co₃O₄ nanoparticles. These strategies enable the fabrication of well-defined nanostructures with enhanced catalytic performance. Additionally, the mechanisms of OER catalysis on Co₃O₄-based electrocatalysts have been elucidated. Coordinatively unsaturated sites, synergistic effects with other elements, surface restructuring, and pH dependency have been identified as crucial factors influencing the catalytic activity. The understanding of these mechanisms provides insights into the design and optimization of Co₃O₄-based electrocatalysts for efficient OER applications. The recent advancements discussed in this review offer valuable perspectives for researchers working on the development of electrocatalysts for the OER, with the goal of achieving sustainable and efficient energy conversion and storage systems. Full article

Motors, as the core carriers of pollution-free power, realize efficient electric energy conversion in clean energy systems such as electric vehicles and wind power generation, and are widely used in industrial automation, smart home appliances, and rail transit fields with their low-noise and zero-emission operating characteristics, significantly reducing the dependence on fossil energy. As the requirements of various application scenarios become increasingly complex, it becomes particularly important to accurately and quickly design the sealing structure of motors. However, traditional design methods show many limitations when facing such challenges. To solve this problem, this paper proposes hybrid models of machine learning that contain polynomial regression and optimization XGBOOST models to rapidly and accurately predict the sealing performance of motors. Then, the hybrid model is combined with the simulated annealing algorithm and multi-objective particle swarm optimization algorithm for optimization. The reliability of the results is verified by the mutual verification of the results of the simulated annealing algorithm and the particle swarm optimization algorithm. The prediction accuracy of the hybrid model for data outside the training set is within 2.881%. Regarding the prediction speed of this model, the computing time of ML is less than 1 s, while the computing time of FEA is approximately 9 h, with an efficiency improvement of 32,400 times. Through the cross-validation of single-objective optimization and multi-objective optimization algorithms, the optimal design scheme is a groove depth of 0.8–0.85 mm and a pre-tightening force of 80 N. The new method proposed in this paper solves the limitations in the design of motor sealing structures, and this method can be extended to other fields for application. Full article

A model is proposed to investigate the effects of power generation source diversification and CO₂ emission control in the presence of dispatchable fossil fuel sources and non-dispatchable carbon-free renewables. In a stochastic environment in which three random factors are considered, namely fossil fuels (gas and coal) and CO₂ prices, we discuss a planning methodology for power system portfolio selection that integrates the non-dispatchable renewables available in a given energy system and optimally combines cost, risk and CO₂ emissions. By combining the deep neural network probabilistic forecasting of fossil fuel path prices with a geometric Brownian motion model for describing the CO₂ price dynamics, we simulate a wide range of plausible market scenarios. Results show that under CO₂ price volatility, optimal portfolios shift toward cleaner energy sources, even in the absence of explicit emission targets, highlighting the implicit regulatory power of volatility. The results suggest that incorporating CO₂ price volatility through market mechanisms can serve as an effective policy tool for driving decarbonization. Our model offers a flexible and reproducible approach to support policy design in energy planning under uncertainty. Full article

The transportation sector mainly relied on fossil fuel and is one of the major causes of climate change and environmental pollution. Advances in smart sensing technology are paving the way for the development of clean and intelligent vehicles that lead to a more sustainable transportation system. In response, the automotive industry is actively engaging in new sensor technologies and innovative control and diagnostic algorithms that improve energy sustainability and reduce vehicle emissions. In particular, recent regulations for diesel vehicles require the integration of smart soot sensors to deal with particulate filter on-board diagnostic (OBD) challenges. Meeting the recent, more stringent OBD requirements will be difficult using traditional diagnostic approaches. This study investigates an advanced diagnostic strategy to assess particulate filter health based on resistive soot sensors and available engine variables. The sensor data are projected to generate a 2D signature that reflects the changes in filtration efficiency. A relevant feature (character) is then extracted from the generated signature that can be transformed into an analytical expression used as an indicator of DPF malfunction. The diagnostic strategy uses an adaptive approach that dynamically adjusts the signature’s characters according to the engine’s operating conditions. A correction factor is calculated using an optimization algorithm based on the integral of engine speed measurements and IMEP set points during each sensor loading period. Different cost functions have been tested and evaluated to improve the diagnostic performance. The proposed adaptive approach is model-free and eliminates the need for subsystem models, iterative algorithms, and extensive calibration procedures. Furthermore, the time-consuming and inaccurate estimation of soot emissions upstream of the DPF is avoided. It was evaluated on a validated numerical platform under NEDC driving conditions with simultaneous dispersions on engine-out soot concentration and soot sensor measurements. The promising results highlight the robustness and superior performance of this approach compared to a diagnostic strategy solely reliant on sensor data. Full article

Green sustainable energy, especially renewable energy, is gaining huge popularity and is considered a vital energy in addressing energy conservation and global climate change. One of the most significant renewable energy sources in the UAE is solar energy, due to the country’s high solar radiation levels. This paper focuses on advanced technology that integrates parabolic trough mirrors, molten salt storage, and thermoelectric generators (TEGs) to provide a reliable and effective solar system in the UAE. Furthermore, the new system can be manufactured in different sizes suitable for consumption whether in ordinary houses or commercial establishments and businesses. The proposed design theoretically achieves the target electrical energy of 2.067 kWh/day with 90% thermal efficiency, 90.2% optical efficiency, and 8% TEG efficiency that can be elevated to higher values reaching 149% using the liquid-saturated porous medium, ensuring the operation of the system throughout the day. This makes it a suitable solar system in off-grid areas. Moreover, this system is a cost-effective, carbon-free, and day-and-night energy source that can be dispatched on the electric grid like any fossil fuel plant under the proposed method, with less maintenance, thus contributing to the UAE’s renewable energy strategy. Full article

Oceans are a major source of clean energy, harnessing the vast and consistent power of waves to generate electricity. Today, they are seen as a vital renewable and clean solution for transitioning to a complete fossil fuel-free future world. To get the most out of ocean wave potential, Wave Energy Converters (WECs) are being used to harness the power of ocean waves into usable electrical energy. To this end, to maximize the power generated from the WECs, two strategies for WEC design improvement and optimal coordination can be considered. Among these, optimal coordination is the more straightforward method to implement. However, most of the recently developed coordination strategies are dynamic-based, encountering challenges as the system’s scale expands and grows larger. Consequently, a novel Graph Isomorphic Network (GIN)-based model is presented in this paper. The proposed model consists of the following five layers: the input graph, two GIN convolutional layers (GIN Conv.1, and 2), a mean pooling layer, and the output layer. The target of total generated power is predicted based on the features of the generated power from each WEC and the related spatial coordinates $\{x_i,y_i\}$. Subsequently, based on the anticipated total power considered by the model, the system enables maximum generation. The model performs spatial coordination analyses to present the optimal coordination for each WEC to achieve the objective of maximizing total generated power. The proposed model is evaluated through several Key Performance Indicators (KPIs), achieving the least number of errors in prediction and optimal coordination performances. Full article

The growth of the urban population and the increase in e-commerce activities have resulted in challenges for last-mile delivery. On the other hand, electric vehicles (EVs) have been introduced to last-mile delivery as an alternative to fossil fuel vehicles. Electric vehicles (EVs) not only play a pivotal role in reducing greenhouse gas emissions and air pollution but also contribute significantly to the development of more energy-efficient and environmentally sustainable urban transportation systems. Within these dynamics, the Electric Vehicle Routing Problem (EVRP) has begun to replace the Vehicle Routing Problem (VRP) in last-mile delivery. While classic vehicle routing ignores fueling, both the location of charging stations and charging time should be included in the Electric Vehicle Routing Problem due to the long recharging time. This study addresses the Capacitated EVRP (CEVRP) with a novel Q-learning algorithm. Q-learning is a model-free reinforcement learning algorithm designed to maximize an agent’s cumulative reward over time by selecting optimal actions. Additionally, a new dataset is also published for the EVRP considering field constraints. For the design of the dataset, real geographical positions have been used, located in the province of Eskisehir, Türkiye. It also includes environmental information, such as streets, intersections, and traffic density, unlike classical EVRP datasets. Optimal solutions are obtained for each instance of the EVRP by using the mathematical model. The results of the proposed Q-learning algorithm are compared with the optimal solutions of the presented dataset. Test results show that the proposed algorithm provides remarkable advantages in obtaining routes in a shorter time for EVs. Full article

Industrial process heat typically requires large amounts of fossil fuels. Solar energy, while abundant and free, has low energy density, and so large collector areas are needed to meet thermal needs. Land costs in developed areas are often prohibitively high, making rooftop-based concentrating solar power (CSP) attractive. However, limited rooftop space and the low energy density of solar power are usually insufficient to meet a facility’s demands. Maximizing annual CSP energy generation within a bounded rooftop space is necessary to mitigate fossil fuel consumption. This is a different optimization objective than minimizing the Levelized Cost of Energy (LCOE) in typical open-land, utility-scale heliostat layout optimization. Innovative designs are necessary, such as compact, energy-dense central receiver systems with non-flat heliostat field topographies that use spatially efficient Tilt–Roll heliostats or multi-rooftop and multi-height distributed urban systems. A novel ray-tracing simulation tool was developed to evaluate these unique scenarios. For compact systems, optimized annual energy production occurred with maximum heliostat spatial density, and the best non-flat heliostat field topography found is a shallow section of a parabolic cylinder with an East–West focal axis, yielding a 10% optical energy improvement. Tightly packed Tilt–Roll heliostats showed a double improvement in optical energy at the receiver compared to Azimuth–Elevation heliostats. Full article

The increasing use of renewable energy sources, such as wind and solar energy, presents challenges to the stability and efficiency of other energy sources due to their intermittent and unpredictable surpluses. The unintended consequence of stabilizing the power supply system is an increase in emissions and external costs from the suboptimal use of coal power plants. The rising number of RES curtailments needs to be addressed by either the adjusting energy supply from fossil fuel or the flexible energy consumption. In Poland’s energy mix, coal-fired power plants are a critical component in ensuring energy security for the foreseeable future. Using domestic lignite to generate a total power of 8.5 GW can stabilize the national power supply, as it is currently done in Germany, where 15 GW of lignite-fueled power units provide the power supply base for the country. The leading Belchatów power plant comprises 10 retrofitted units and one new unit, with a total rating of 5.5 GW. Access to the new coal deposit, Zloczew, is necessary to ensure its longer operation. The other domestic lignite power plants are located in Central Poland at Patnów (0.47 GW from the new unit and 0.6 GW from its three retrofitted counterparts) and located in the Lusatian lignite basin at Turów (operating a brand new unit rated at 0.5 GW and retrofitted units with a total rating of 1.5 GW). The use of this fuel is currently being penalized as a result of increasing carbon costs. However, the continuous surface mining technology that is used in lignite mines is fully electrified, and large amounts of electric energy are required to remove and dump overburden and mining coal and its conveying to power units (the transport of coal from the new lignite mine Zloczew to the Belchatów power plant would be a long-distance operation). A possible solution to this problem is to focus on the lignite fuel supply operations of these power plants, with extensive simulations of the entire supply chain. A modern lignite mine is operated by one control room, and it can balance the dynamic consumption of surplus renewable energy sources (RESs) and reduce the need for reduction. When a lignite supply chain is operated this way, a high-capacity power bank can be created with energy storage in the form of an open brown coal seam. This would enable an almost emission-free supply of cheap and domestic fossil fuel, making it insensitive to changes in the world prices of energy resources for power units operating at the base of the system. Furthermore, extending the life of relatively new and efficient lignite-fired units in Poland would facilitate the decommissioning of older and exhausted hard coal-fired units. Full article

The mesquite tree (Prosopis juliflora) is cultivated across 500,000 hectares in the semi-arid region of Brazil, primarily aimed at recovering degraded areas in the northeastern part of the country, which represents 15.7% of the national territory. However, its economic potential remains underutilized. Mesquite pods are particularly rich in carbohydrates, making them a promising raw material for bioethanol production. This study investigates the production of first-generation bioethanol from mesquite pods as feedstock. Mature pods were sourced from local producers in Sumé Town, located in the Cariri Paraibano microregion of Brazil. Sugar extraction from the mesquite pods involved hydration followed by pressing, with the extracted juice adjusted to a pH of 4.3 and soluble solids (°Brix) concentrations corrected to 20, 18, and 16. The juice was then subjected to fermentation using different yeast strains (fresh yeast, granular yeast, and FLNF CA-11) at a concentration of 25 g L⁻¹. Alcoholic fermentation was carried out in a batch system, with measurements of cell concentration (biomass), soluble solids (°Brix), ethanol concentration (°GL), and pH taken at 2 h intervals over a 20 h period. The best physicochemical characterization of bioethanol was obtained using the LNF CA-11 yeast at 20 °Brix, producing a biofuel that met Brazilian legal standards set by the National Petroleum Agency (ANP). The bioethanol had a colorless appearance and was free of impurities, with a titratable acidity of 28.2 mg of acetic acid, electrical conductivity of 282.33 µS m⁻¹, a specific mass of 809 kg m⁻³, an alcohol content of 95.5 °GL, a pH of 6.28, and no evaporation residue in 100 mL. Additionally, the highest bioethanol yield was achieved with broth fermented at 18 °Brix and LNF CA-11 yeast. These results highlight the potential of mesquite pods as a renewable energy alternative, especially relevant in the context of the global climate crisis; the growing need to reduce dependence on fossil fuels; and the need to reduce environmental problems; and they promote the added-value and use of this product. Full article

The reduction in the supply of fossil fuel available, combined with global warming’s effects on the atmosphere, has led to the discovery of employing sustainable energy for everyday activities. Road energy harvesting is one example of sustainable energy that can be used, as the majority of people spend a substantial amount of their daily activities commuting from one location to another, and numerous types of transportation generate heat that can be converted into energy. This alternative energy source can be implemented on the road, considering that roads are critical infrastructure that has a significant effect on a country’s economy. Furthermore, road infrastructure has been contributing towards the affordability of urbanization and migration, whether locally or internationally. Currently, researchers are working towards integrating road energy harvesting around the world by incorporating various types of materials and technology connected via a sensing system. Many materials have been attempted, including ceramics, polymers, lead-free, nanomaterials, single crystals, and composites. Other possible sources to generate energy from roadways, such as solar power, thermal energy, and kinetic energy, have been investigated as well. However, many studies available only focused on the disclosure of novel materials or the review of technologies produced for road energy harvesting. There have been limited studies that focused on a comprehensive review of various materials and technologies and their implications for the performance of road energy harvesting. Hence, the main objective of this research is to undertake a thorough and in-depth review in order to identify the best materials and technologies for certain types of application in road energy harvesting. The paper discusses energy-harvesting technology, sensing systems, and the potential network based on them. Comprehensive analyses were conducted to evaluate in-depth comparisons between different materials and technologies used for road energy harvesting. The novelty of this study is related to the appropriate efficient, durable, and sustainable materials and technologies for their relevant potential application. The results of this review paper are original since it is the first of its kind, and, to the best knowledge of the authors’ knowledge, a similar study is not available in the open literature. Full article

This paper provides policy recommendations for accelerating the adoption of Fossil-Energy-Free Technologies and Strategies (FEFTS) in the EU agricultural sector. Faster adoption of these technologies and strategies is crucial to achieving the medium- and long-term sustainability targets laid out in EU policy. The prepared policy recommendations originate out of the key outputs and findings of the Horizon 2020 project “AgroFossilFree”, including an assessment and evaluation of the current energy use status in EU agriculture, survey results on farmers’ needs, ideas and interests on the adoption of FEFTS, FEFTS categories identified through an online inventory of FEFTS called the AgEnergy platform, and key innovative processes through national and transnational workshops that combine expertise from hundreds of keys stakeholders (researchers, innovation brokers, policymakers, farmers, and industry representatives). The policy recommendations are synthesized and presented in the form of 19 policy briefs split into three main categories: those that are related to energy issues in farming and can be applied to any farm and FEFTS type; those that are specific to certain agricultural production systems; and those that are necessary for FEFTS integration in agriculture in general. Full article

Concerns about climate change are a hot topic in the current debate about a sustainable future, and despite more than 30 years of international conferences, including the Intergovernmental Panel on Climate Change (IPCC) and the United Nations Climate Change Conference (COP), the annual usage amount of fossil fuel-based energy sources has remained largely unchanged, and the green transition to a carbon-free energy system is progressing at a much slower pace than anticipated. This paper presents an original approach that consists in addressing the green transition’s dilemmas by analyzing the complex interplay of strongly interwoven forces hindering the rapid adoption of so-called green energy sources scrutinized from a three-fold perspective: socio-psychological; political–strategic and territorial; and technological. Moreover, these forces are ranked according to the magnitude of their impact on the anticipated transition to green, and it is estimated by logistic fit extrapolation that the total share of the contribution of low-carbon sources might reach a maximum of about 25% among all energy sources in 2050. A final original picture is presented, summarizing how all the involved forces are acting upon the expected transition as well as their consequences. Full article

The defossilisation of the agricultural sector is driven by intense worldwide academic research on non-fossil, renewable and energy-efficient agriculture, and the acknowledgment of the need for sustainable farming practices. For this purpose, not only technical transformations but also socio-technical system changes towards sustainability need to take place in a co-evolutionary manner. This paper investigates structural and qualitative characteristics of the knowledge produced by research on fossil-energy-free agriculture. We provide evidence on the worldwide research directions, as well as investigate whether academic research and publicly funded research projects foster knowledge creation for the desired transformation. Bibliographic maps are constructed using a query-based methodology as social networks to investigate the efficiency of the EU-funded research to achieve the goals set for the 2050 EU Green Deal. The H2020-funded papers are further analysed with dictionary-based text analysis to quantify the relative emphasis of different types of knowledge in the text. This approach is eventually used to relate transformational capacity to project profiles in the European Union, to evaluate past funding schemes and to improve the shape of future research programs on renewable and sustainable agriculture. Full article

E-fuels represent a crucial technology for transitioning to fossil-free energy systems, driven by the need to eliminate dependence on fossil fuels, which are major environmental pollutants. This study investigates the production of carbon-neutral synthetic fuels, focusing on e-hydrogen (e-H₂) generated from water electrolysis using renewable electricity and carbon dioxide (CO₂) captured from industrial sites or the air (CCUS, DAC). E-H₂ can be converted into various e-fuels (e-methane, e-methanol, e-DME/OME, e-diesel/kerosene/gasoline) or combined with nitrogen to produce e-ammonia. These e-fuels serve as efficient energy carriers that can be stored, transported, and utilized across different energy sectors, including transportation and industry. The first objective is to establish a clear framework encompassing the required feedstocks and production technologies, such as water electrolysis, carbon capture, and nitrogen production techniques, followed by an analysis of e-fuel synthesis technologies. The second objective is to evaluate these technologies’ technological maturity and sustainability, comparing energy conversion efficiency and greenhouse gas emissions with their electric counterparts. The sustainability of e-fuels hinges on using renewable electricity. Challenges and future prospects of an energy system based on e-fuels are discussed, aiming to inform the debate on e-fuels’ role in reducing fossil fuel dependency. Full article