Search Results (621)

The global shipping fleet uses vast quantities of fossil fuels and releases significant levels of pollution. Supplying ships moored at quays in ports with onshore power allows them to shut down onboard engines, cutting fossil fuel use and reducing emissions. This is particularly significant when ports utilize green electricity. Equipping ports to connect serviced ships to onshore power grids involves substantial investments, which must be carefully optimized. The aim of this article is to develop a methodology, grounded in probability theory, for determining the electrical power required to connect ships to onshore power grids in ports. The proposed methodology was developed and validated through a case study of container terminal operations. By applying this methodology and considering the conditions of ship service in ports, it is possible to estimate both the number of ships and their berthing durations at quays, as well as the electrical power required from onshore networks to connect the vessels. The results of this research may be of interest to port managers, terminal operators, shipowners, and other stakeholders involved in the development of onshore power grids for ship connections in ports. Full article

Thermochemical energy storage technology offers an effective approach to address the intermittency and instability of solar energy supply, thereby enhancing its utilization efficiency and reducing dependence on fossil fuels. The CaCO₃/CaO system provides a low-cost, abundant, and safe thermal energy storage solution with high energy density, suitable for large-scale use. However, the low effective thermal conductivity of the storage material in fixed-bed reactors often leads to limited heat transfer performance. To address this issue, this study investigates the internal temperature distribution and reaction field in a finned reactor, with a focus on the effects of fin geometry (including layout, number, and dimensions) on the carbonation reaction performance. The results demonstrate that the incorporation of fins significantly enhances heat transfer within the reactor. Hor-izontal fins increased the reaction rate by 11.81%, while vertical fins resulted in a more pronounced improvement of 41.17%. Furthermore, variations in fin structural parameters markedly influenced the carbonation process. Increasing the number of vertical fins from four to eight improved the reaction rate by 24.23%. Under the conditions studied, the optimal fin configuration, with a thickness of 0.002 m, a length of 0.03 m, and a total of eight fins, achieved the shortest carbonation time. This study provides valuable insights into the design of efficient reactor structures for enhanced thermochemical energy storage. Full article

During liquid drainage from intermediate vessels in various industrial processes such as continuous steel casting, aircraft fuel supply, and chemical separation, free-surface vortices commonly occur. The formation and evolution of these vortices not only entrain surface slag and gas, but also lead to deterioration of downstream product quality and abnormal equipment operation. The vortex evolution process exhibits notable three-dimensional unsteadiness, multi-scale turbulence, and dynamic gas–liquid interfacial changes, accompanied by strong coupling effects between temperature gradients and flow field structures. Traditional macroscopic numerical models show clear limitations in accurately capturing these complex physical mechanisms. To address these challenges, this study developed a mesoscopic numerical model for gas-liquid two-phase vortex flow based on the lattice Boltzmann method. The model systematically reveals the dynamic behavior during vortex evolution and the multi-field coupling mechanism with the temperature field while providing an in-depth analysis of how initial perturbation velocity regulates vortex intensity and stability. The results indicate that vortex evolution begins near the bottom drain outlet, with the tangential velocity distribution conforming to the theoretical Rankine vortex model. The vortex core velocity during the critical penetration stage is significantly higher than that during the initial depression stage. An increase in the initial perturbation velocity not only enhances vortex intensity and induces low-frequency oscillations of the vortex core but also markedly promotes the global convective heat transfer process. With regard to the temperature field, an increase in fluid temperature reduces the viscosity coefficient, thereby weakening viscous dissipation effects, which accelerates vortex development and prolongs drainage time. Meanwhile, the vortex structure—through the induction of Taylor vortices and a spiral pumping effect—drives shear mixing and radial thermal diffusion between fluid regions at different temperatures, leading to dynamic reconstruction and homogenization of the temperature field. The outcomes of this study not only provide a solid theoretical foundation for understanding the generation, evolution, and heat transfer mechanisms of vortices under industrial thermal conditions, but also offer clear engineering guidance for practical production-enabling optimized operational parameters to suppress vortices and enhance drainage efficiency. Full article

Manganese is a critical raw material and there is currently a great interest in decarbonization in the metallurgical sector for its production. Hydrogen use in manganese and its alloys’ production is in principle possible for sustainable production; however, this requires a technological shift from traditional carbothermic processes to completely new processes; like the HAlMan process. To design a process, it is crucially important to optimize the process conditions (such as temperature) and minimize the quantity of hydrogen gas and the related energy consumptions. In the present work, energy and mass balances for a hydrogen-based reduction reactor were carried out employing thermodynamics software and analytical approaches from room temperatures to 900 °C. It was found that the quantity of hydrogen gas required for the pre-reduction of manganese ore can be significantly reduced via coupling the reduction reactor with a calciner and the hot charge of the calcined ore into the reduction reactor. Moreover, hot H₂-H₂O gas mixture from the reduction reactor outlet can be used for preheating the hydrogen feed of the reactor, and the calcination of the ore, while a portion or all its hydrogen can be recovered and looped. The integrated coupled calcination-reduction process was found to be operated with no external energy supply, or insignificant fuel use. Full article

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

Proton exchange membrane fuel cells (PEMFCs) represent a new generation of clean and efficient energy conversion devices, demonstrating broad application prospects in transportation, distributed power generation, and other fields. The geometric configuration of the cathode gas channel (GC) and the surface microstructure of the gas diffusion layer (GDL) are core factors influencing the efficiency of reactant gas transport and water management performance. However, conventional rectangular flow channels suffer from insufficient convective enhancement and restricted oxygen supply beneath the fins. Furthermore, homogeneous GDLs exhibit limited diffusion and drainage capabilities, often leading to oxygen depletion and flooding downstream of the cathode, significantly limiting overall cell performance. To address these challenges, this study designs a novel centrally positioned fin-type barrier block. A three-dimensional multiphysics numerical model integrating GDL surface microporosity with the internal barrier block flow channels is constructed to systematically investigate the synergistic mechanisms of microporous topology and flow channel structure on two-phase flow distribution, oxygen mass transfer, and electrochemical performance. The results demonstrate that this model accurately captures the dynamic evolution of flow fields within the GDL. Compared to conventional structures, significant coupling effects exist between the GDL microporous structure and the novel barrier block. Their synergistic interaction forms multi-scale mass transfer enhancement and dewatering pathways, providing quantifiable optimization pathways and structural parameter references for high-power-density PEMFC cathode design. Full article

The accelerating pace of urbanization and the pressing need for sustainability have compelled cities worldwide to integrate renewable energy into their infrastructure. While solar, wind, and hydro sources offer cleaner alternatives to fossil fuels, their inherent variability creates challenges in maintaining balance between supply and demand in urban energy systems. Traditional statistical forecasting methods are often inadequate for capturing the nonlinear, weather-driven dynamics of renewables, highlighting the need for advanced artificial intelligence (AI) approaches that deliver both accuracy and interpretability. This paper proposes a spatio-temporal framework for smart city energy management that combines a Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) for renewable energy generation forecasting, a Gradient Boosting Machine (GBM) for urban demand prediction, and Particle Swarm Optimization (PSO) for cost-efficient energy allocation. The framework was first validated using Spain’s national hourly energy dataset (2015–2018). To rigorously test its generalizability, the methodology was further validated on a separate dataset for the German energy market (2019–2022), proving its robustness across different geographical and meteorological contexts. Results indicate strong predictive performance, with solar generation achieving a 99.03% R² score, wind 96.46%, hydro 93.02%, and demand forecasting 91.56%. PSO further minimized system costs, reduced reliance on fossil-fuel generation by 18.2%, and improved overall grid efficiency by 12%. These findings underscore the potential of AI frameworks to enhance reliability and reduce operational costs. Full article

This review examines how the integration of circular bioeconomy principles with digital technologies can drive climate change mitigation, improve resource efficiency, and facilitate sustainable biorefinery development. This highlights the urgent need to transition away from fossil fuels and introduces the bio-circular economy as a regenerative model focused on biomass valorization, reuse, recycling, and biodegradability. This study compares linear, circular, and bio-circular approaches and analyzes key policy frameworks in Europe, Latin America, and Asia linked to several UN Sustainable Development Goals. A central focus is the role of digitalization, particularly artificial intelligence (AI), the Internet of Things (IoT), and blockchain. Examples include AI-based biomass yield prediction and biorefinery optimization, IoT-enabled real-time monitoring of material and energy flows, and blockchain technology for supply chain traceability and transparency. Applications in agricultural waste valorization, bioplastics, bioenergy, and nutraceutical extraction are also discussed in this review. Sustainability tools, such as automated life-cycle assessment (LCA) and Industry 4.0 integration, are outlined. Finally, future perspectives emphasize autonomous smart biorefineries, biotechnology–nanotechnology convergence, and international collaboration supported by open data platforms. Full article

This study examines the organization of offshore bunkering operations with the aim of improving their economic and logistical efficiency. A mathematical model is proposed that minimizes the total cost of fleet refueling while accounting for technical limitations of vessels, service time windows, and external operational constraints. The formulation extends classical vehicle routing approaches by incorporating fixed and variable costs as well as penalties for delays. A case study based on the Sea of Okhotsk fleet illustrates the application of the model to ten client vessels and four bunkering ships. Using mixed-integer programming combined with heuristic route construction, optimal routing solutions were obtained and tested under varying fuel prices, demand volumes, and fleet sizes. In a stylized one-day case study with ten client vessels located within a 100 km radius around Magadan, the results indicate that reducing the number of active bunkering vessels from four to three can lower overall operating costs while maintaining service quality, yielding indicative savings of approximately 12–18% relative to a simple sequential baseline policy in which bunkering vessels serve customers in a fixed order and the client set is partitioned roughly equally among vessels. The proposed approach provides a practical framework for decision-makers to enhance planning, resource allocation, and operational reliability in marine fuel supply chains. Full article

The high energy consumption characteristics across all segments of the agricultural supply chain, coupled with rural areas’ excessive reliance on traditional power grids and fossil fuel-based energy supply models, not only result in persistently high energy utilization costs and low efficiency but also inflict ongoing negative environmental impacts. This undermines sustainable development and the achievement of energy security. In response, this paper proposes a multi-timescale robust operation scheme for the coordinated operation of rural integrated energy systems and agricultural supply chains. Its core components are as follows: (1) Establish a collaborative operation framework integrating renewable energy-based rural integrated energy systems with agricultural supply chains; (2) Holistically consider energy consumption characteristics across supply chain segments, leveraging sensor-based environmental parameters for crop yield forecasting and hourly energy consumption assessment. This effectively addresses misalignments between crop growth and energy optimization scheduling, as well as inconsistent energy measurement scales across supply chain segments, thereby advancing agricultural sustainability; (3) Introducing a two-stage robust optimization model to quantify the impact of environmental uncertainty on the collaborative framework and integrated energy system, ensuring optimal operation of supply chain equipment under worst-case conditions; (4) Identifying critical energy consumption nodes in the supply chain through system performance analysis and revealing optimization potential in the collaborative mechanism, enabling flexible load shifting and cross-temporal energy allocation. Simulation results demonstrate that this coordinated operation scheme enables dynamic estimation and optimization of crop growth and energy consumption, reducing system operating costs while enhancing supply chain reliability and renewable energy integration capacity. The two-stage robust optimization mechanism effectively strengthens system robustness and adaptability, mitigates the impact of renewable energy output fluctuations, and achieves spatiotemporal optimization of energy allocation. Full article

Nowadays, renewable energy sources are gaining importance, yet global energy demand is primarily met by burning fossil fuels. Fluctuations in fossil fuel availability, driven by geopolitical tensions, supply–demand changes, and natural disasters, can lead to sudden energy price spikes or supply shortages, adversely affecting the global economy. Despite its negative impact on carbon emissions and climate change, Heating Oil (HO) offers advantages over other fossil fuels in efficiency, reliability, and availability. Accurate time series prediction models for HO are crucial for stakeholders. This study proposes a novel hybrid model, integrating the Chaotic Adaptive Fitness-Distance Balance-based Stochastic Fractal Search (AFDB-SFS) algorithm with a Bidirectional Long-Short Term Memory (Bi-LSTM) network, for HO close price prediction. The dataset comprises daily observations of five financial time series (close, open, high, low, and volume) over 4260 trading days, yielding a total of 21,300 data points (4260 days × 5 variables). During the feature extraction stage, financial signal processing methods such as Demand Concentration Curve (DCC) and traditional technical indicators are utilized. A total of 189 features are extracted at appropriate intervals for each indicator. Due to the large number of features, the AFDB-SFS algorithm then efficiently identifies the most compatible feature subsets, optimizing the Bi-LSTM model based on three criteria: maximizing R², minimizing RMSE, and minimizing feature count. Experimental results demonstrate the proposed hybrid model’s superior performance, achieving high accuracy (R² of 0.9959 and RMSE of 0.0364), outperforming contemporary models in the literature. Furthermore, the model is successfully implemented on the Jetson Orin Nano Developer Platform, enabling real-time, high-frequency HO price predictions with ultra-low latency (1.01 ms for Bi-LSTM), showcasing its practical utility for edge computing applications in commodity markets. Full article

The hybrid energy storage system (HESS) significantly improves the dynamic response and energy utilization efficiency of the propulsion system in fuel cell vessels while maintaining the stability of the power grid. To address the issue of inaccurate power allocation and unreasonable capacity configuration caused by modal aliasing during power decomposition, this article innovatively proposes a power distribution method for hybrid energy storage systems. First, the Multi-Verse Optimizer (MVO) is used to optimize Variational Mode Decomposition (VMD) in order to address the issue of VMD being highly dependent on parameter selection. Then, power is decomposed twice to resolve the modal aliasing problem associated with single decomposition, achieving a more accurate power breakdown and providing a more stable power output. Finally, the decomposed powers are cross-allocated: low frequencies are assigned to lithium batteries that can provide long-term stable energy supply, while high frequencies are allocated to supercapacitors capable of delivering short-term efficient energy supply. The simulation results indicate that the MVO-CVMD method proposed in this paper effectively addresses the modal aliasing problem, enhances the accuracy of power decomposition, and reduces the cost of capacity configuration. Full article

The use of non-renewable energy resources is one of the main drivers of climate change. In response, the United Nations established the seventh Sustainable Development Goal, “Affordable and clean energy”, which promotes the transition toward renewable and environmentally friendly sources such as wind and solar energy. However, the intermittent nature of these resources poses challenges for maintaining a stable, continuous power supply, highlighting the need for hybrid technology approaches, such as Hybrid Renewable Energy Systems (HRES), which integrate complementary renewable sources with energy storage. In this context, this study applies a Particle Swarm Optimisation (PSO)-based approach to determine the optimal sizing and operating strategy for a hybrid system comprising photovoltaic, wind, battery storage, and diesel backup units under various synthetic load profiles. The results indicate that diesel-assisted configurations achieve lower levelized costs of energy (0.23–0.35 USD/kWh) and maintain high reliability (LPSP < 0.25%), although at the expense of higher fuel consumption and CO₂ emissions. Conversely, fully renewable configurations present higher energy costs (0.29–0.44 USD/kWh), but reduce annual CO₂ emissions by up to 50% and create more employment opportunities, particularly in regions with abundant wind resources such as La Guajira, Colombia. Full article

In conventional energy system planning, cost optimisation is usually the decisive factor. The objective of the research, on which this article is based, is to develop alternatives to cost-optimised energy supply concepts that are near optimal cost and also meet the criterion of increased resilience. The methodology presented here thus expands the solution space for the planning of energy systems and the consideration of additional criteria beyond pure cost optimisation. The transition from a fossil fuel-based energy system to one reliant on renewable sources brings significant structural changes and uncertainties. Resilience management offers a guiding concept to address the non-linear complexities and unpredictability of this transformation process and to cope with uncertain and unknown stressors. Thus, a comparative assessment of the resilience of different future energy concepts is crucial to provide a basis for decision making and implementation of resilient energy systems. This research approach entailed the optimisation of a heat supply concept for an urban district and the investigation of near-optimal alternatives in the vicinity of the optimal solution. The resilience of these near-optimal solutions was then analysed. For this purpose, certain resilience-enhancing structures and functionalities (diversity, redundancy, buffer capacity) were evaluated by quantifiable indicators. The analysis of the heat supply scenarios has shown that resilience, measured by the indicators used, could be increased at a low additional cost. In the top-performing alternative-heat-supply scenarios generated, the diversity has been increased by 585%, redundancy by 18% and buffer capacity by 98%. The majority of the generated alternatives that were examined showed that an increase in diversity and redundancy could be achieved at a relatively low additional cost. Full article

Against the backdrop of the full market integration of renewable energy, determining a reasonable proportion between medium- and long-term (MLT) contracts and spot trading has become a core issue in power market reform. Current Chinese policy requires that the share of MLT contracts should not be less than 90%, which helps ensure system security but may suppress the price discovery function of the spot market and limit renewable energy integration. This paper constructs a three-layer model: the first layer describes spot market clearing through Direct Current Optimal Power Flow (DC-OPF), yielding system energy prices and nodal prices; the second layer models bilateral contract decisions between generators and users based on Nash bargaining, incorporating risk preferences via a mean–variance framework; and the third layer introduces two evaluation indicators—contract penetration rate and economic proportion—and applies outer-layer optimization to search for the optimal contract ratio. Parameters are calibrated using coal prices, wind speed, solar irradiance, and load data, with numerical solutions obtained through Monte Carlo simulation and convex optimization. Results show that increasing the share of spot trading enhances overall system efficiency, primarily because renewable energy has low marginal costs and high supply potential, thereby reducing average market prices and mitigating volatility. Simulations indicate that the optimal contract coverage rate may exceed the current policy lower bound, which would expand spot market space and promote renewable energy integration. Sensitivity analysis further reveals that fuel price fluctuations, renewable output, load structure, and risk preferences all affect the optimal proportion, though the overall conclusions remain robust. Policy implications suggest moderately relaxing the constraints on MLT contract proportions, improving contract design, and combining this with transmission expansion and demand response, in order to establish a more efficient and flexible market structure. Full article