Search Results (1,494)

Minimizing carbon dioxide emissions is crucial due to the generation of energy from fossil fuels. The significance of carbon capture and storage (CCS) technology, which is highly successful in mitigating carbon emissions, has increased. On the other hand, hydrogen is an important energy carrier for storing and transporting energy, and technologies that rely on hydrogen have become increasingly promising as the world moves toward a more environmentally friendly approach. Nevertheless, the integration of CCS technologies into power production processes is a significant challenge, requiring the enhancement of the combined power generation–CCS process. In recent years, there has been a growing interest in process intensification (PI), which aims to create smaller, cleaner, and more energy efficient processes. The goal of this research is to demonstrate the process intensification potential and to model and simulate a hybrid integrated–intensified adsorptive-membrane reactor process for simultaneous carbon capture and hydrogen production. A comprehensive, multi-scale, multi-phase, dynamic, computational fluid dynamics (CFD)-based process model is constructed, which quantifies the various underlying complex physicochemical phenomena occurring at the pellet and reactor levels. Model simulations are then performed to investigate the impact of dimensionless variables on overall system performance and gain a better understanding of this cyclic reaction/separation process. The results indicate that the hybrid system shows a steady-state cyclic behavior to ensure flexible operating time. A sustainability evaluation was conducted to illustrate the sustainability improvement in the proposed process compared to the traditional design. The results indicate that the integrated–intensified adsorptive-membrane reactor technology enhances sustainability by 35% to 138% for the chosen 21 indicators. The average enhancement in sustainability is almost 57%, signifying that the sustainability evaluation reveals significant benefits of the integrated–intensified adsorptive-membrane reactor process compared to HTSR + LTSR. Full article

The growing global demand for electricity, driven by the development of electromobility, data centers, and smart technologies, necessitates innovative approaches to energy generation. Wind power, as a clean and renewable energy source, plays a pivotal role in the global transition towards low-carbon power systems. This paper presents a comprehensive review of generator technologies used in wind turbine applications, ranging from conventional synchronous and asynchronous machines to advanced concepts such as low-speed direct-drive (DD) generators, axial-flux topologies, and superconducting generators utilizing low-temperature superconductors (LTS) and high-temperature superconductors (HTS). The advantages and limitations of each design are discussed in the context of efficiency, weight, reliability, scalability, and suitability for offshore deployment. Special attention is given to HTS-based generator systems, which offer superior power density and reduced losses, along with challenges related to cryogenic cooling and materials engineering. Furthermore, the paper analyzes selected modern generator designs to provide references for enhancing the performance of grid-synchronized hybrid microgrids integrating solar PV, wind, battery energy storage, and HTS-enhanced generators. This review serves as a valuable resource for researchers and engineers developing next-generation wind energy technologies with improved efficiency and integration potential. Full article

The diffusion coefficient (DC) of CO₂ in brine is a key parameter in geological carbon sequestration and CO₂-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. The dataset, comprising 176 data points, spans pressures from 0.10 to 30.00 MPa, temperatures from 286.15 to 398.00 K, salinities from 0.00 to 6.76 mol/L, and DC values from 0.13 to 4.50 × 10⁻⁹ m²/s. The data was split into 80% for training and 20% for testing to ensure reliable model evaluation. Model performance was assessed using R², RMSE, and MAE. The RF model demonstrated the best performance, with an R² of 0.95, an RMSE of 0.03, and an MAE of 0.11 on the test set, indicating high predictive accuracy and generalization capability. In comparison, GBR achieved an R² of 0.925, and XGBoost achieved an R² of 0.91 on the test set. Feature importance analysis consistently identified temperature as the most influential factor, followed by salinity and pressure. This study highlights the potential of ML models for predicting CO₂ diffusion in brine, providing a robust, data-driven framework for optimizing CO₂-EOR processes and carbon storage strategies. The findings underscore the critical role of temperature in diffusion behavior, offering valuable insights for future modeling and operational applications. Full article

Forestry carbon sinks play a crucial role in mitigating climate change and protecting ecosystems, significantly contributing to the development of carbon trading systems. Remote sensing technology has become increasingly important for monitoring carbon sinks, as it allows for precise measurement of carbon storage and ecological changes, which are vital for forecasting carbon prices. Carbon prices fluctuate due to the interaction of various factors, exhibiting non-stationary characteristics and inherent uncertainties, making accurate predictions particularly challenging. To address these complexities, this study proposes a method for predicting carbon trading prices influenced by multiple factors. We introduce a Decomposition (DECOMP) module that separates carbon price data and its influencing factors into trend and cyclical components. To manage non-stationarity, we propose the KAN with Multi-Domain Diffusion (KAN-MD) module, which efficiently extracts relevant features. Furthermore, a Wave-MH attention module, based on wavelet transformation, is introduced to minimize interference from uncertainties, thereby enhancing the robustness of the model. Empirical research using data from the Hubei carbon trading market demonstrates that our model achieves superior predictive accuracy and resilience to fluctuations compared to other benchmark methods, with an MSE of 0.204% and an MAE of 0.0277. These results provide reliable support for pricing carbon financial derivatives and managing associated risks. Full article

This study presents a comprehensive analysis of CO₂ trapping mechanisms in subsurface reservoirs by integrating numerical reservoir simulations, geochemical modeling, and machine learning techniques to enhance the design and evaluation of carbon capture and storage (CCS) strategies. A two-dimensional reservoir model was developed to simulate CO₂ injection dynamics under realistic geomechanical and geochemical conditions, incorporating four primary trapping mechanisms: residual, solubility, mineralization, and structural trapping. To improve computational efficiency without compromising accuracy, advanced machine learning models, including random forest, gradient boosting, and decision trees, were deployed as smart proxy models for rapid prediction of trapping behavior across multiple scenarios. Simulation outcomes highlight the critical role of hysteresis, aquifer dynamics, and producer well placement in enhancing CO₂ trapping efficiency and maintaining long-term storage stability. To support the credibility of the model, a qualitative validation framework was implemented by comparing simulation results with benchmarked field studies and peer-reviewed numerical models. These comparisons confirm that the modeled mechanisms and trends align with established CCS behavior in real-world systems. Overall, the study demonstrates the value of combining traditional reservoir engineering with data-driven approaches to optimize CCS performance, offering scalable, reliable, and secure solutions for long-term carbon sequestration. Full article

The maritime sector’s transition to sustainable energy is critical for achieving global carbon neutrality, with container terminals representing a key focus due to their high energy consumption and emissions. This study explores the potential of hydrogen energy as a decarbonization solution for port operations, using the Chuanshan Port Area of Ningbo Zhoushan Port (CPANZP) as a case study. Through a comprehensive analysis of hydrogen production, storage, refueling, and consumption technologies, we demonstrate the feasibility and benefits of integrating hydrogen systems into port infrastructure. Our findings highlight the successful deployment of a hybrid “wind-solar-hydrogen-storage” energy system at CPANZP, which achieves 49.67% renewable energy contribution and an annual reduction of 22,000 tons in carbon emissions. Key advancements include alkaline water electrolysis with 64.48% efficiency, multi-tier hydrogen storage systems, and fuel cell applications for vehicles and power generation. Despite these achievements, challenges such as high production costs, infrastructure scalability, and data integration gaps persist. The study underscores the importance of policy support, technological innovation, and international collaboration to overcome these barriers and accelerate the adoption of hydrogen energy in ports worldwide. This research provides actionable insights for port operators and policymakers aiming to balance operational efficiency with sustainability goals. Full article

With the rise of carbon capture and storage, liquefied carbon dioxide (LCO₂) has emerged as a promising medium for large-scale marine transport. This study evaluates the structural integrity of an IMO Type C cargo tank for a medium-range LCO₂ carrier under four conditions: ultimate limit state, accidental limit state, hydrostatic pressure test, and fatigue limit state, based on IGC Code and classification rules. Seventeen load cases were analyzed using finite element methods with multi-step loading to ensure stability. The highest stress occurred at the pump dome–shell junction due to geometric discontinuities, but all stress and buckling criteria were satisfied. The fatigue damage from wave-induced loads was negligible, with low-cycle fatigue from loading/unloading operations governing the fatigue life, which exceeded 31,000 years. The findings confirm the tank’s structural robustness and its suitability for safe, efficient medium-pressure LCO₂ transport. Full article

In response to increasingly stringent emission regulations, low-carbon fuels have received significant attention as sustainable energy sources for internal combustion engines. This study investigates four representative low-carbon fuels, methane, methanol, hydrogen, and ammonia, by systematically summarizing their combustion characteristics and emission profiles, along with a review of existing after-treatment technologies tailored to each fuel type. For methane engines, unburned hydrocarbon (UHC) produced during low-temperature combustion exhibits poor oxidation reactivity, necessitating integration of oxidation strategies such as diesel oxidation catalyst (DOC), particulate oxidation catalyst (POC), ozone-assisted oxidation, and zoned catalyst coatings to improve purification efficiency. Methanol combustion under low-temperature conditions tends to produce formaldehyde and other UHCs. Due to the lack of dedicated after-treatment systems, pollutant control currently relies on general-purpose catalysts such as three-way catalyst (TWC), DOC, and POC. Although hydrogen combustion is carbon-free, its high combustion temperature often leads to elevated nitrogen oxide (NO_x) emissions, requiring a combination of optimized hydrogen supply strategies and selective catalytic reduction (SCR)-based denitrification systems. Similarly, while ammonia offers carbon-free combustion and benefits from easier storage and transportation, its practical application is hindered by several challenges, including low ignitability, high toxicity, and notable NO_x emissions compared to conventional fuels. Current exhaust treatment for ammonia-fueled engines primarily depends on SCR, selective catalytic reduction-coated diesel particulate filter (SDPF). Emerging NO_x purification technologies, such as integrated NO_x reduction via hydrogen or ammonia fuel utilization, still face challenges of stability and narrow effective temperatures. Full article

Background/Objectives: Global warming and rising CO₂ concentrations pose significant challenges to plant systems. Amid these pressures, this study contributes to understanding how tropical species respond by simultaneously evaluating reproductive and genetic traits. It specifically investigates the effects of maternal exposure to warming and elevated CO₂ on progeny physiology, genetic diversity, and population structure in Stylosanthes capitata, a resilient forage legume native to Brazil. Methods: Maternal plants were cultivated under controlled treatments, including ambient conditions (control), elevated CO₂ at 600 ppm (eCO₂), elevated temperature at +2 °C (eTE), and their combined exposure (eTEeCO₂), within a Trop-T-FACE field facility (Temperature Free-Air Controlled Enhancement and Free-Air Carbon Dioxide Enrichment). Seed traits (seeds per inflorescence, hundred-seed mass, abortion, non-viable seeds, coat color, germination at 32, 40, 71 weeks) and abnormal seedling rates were quantified. Genetic diversity metrics included the average (A) and effective (Ae) number of alleles, observed (Ho) and expected (He) heterozygosity, and inbreeding coefficient (Fis). Population structure was assessed using Principal Coordinates Analysis (PCoA), Analysis of Molecular Variance (AMOVA), number of migrants per generation (Nm), and genetic differentiation index (Fst). Two- and three-way Analysis of Variance (ANOVA) were used to evaluate factor effects. Results: Compared to control conditions, warming increased seeds per inflorescence (+46%), reduced abortion (−42.9%), non-viable seeds (−57%), and altered coat color. The germination speed index (GSI +23.5%) and germination rate (Gr +11%) improved with warming; combined treatments decreased germination time (GT −9.6%). Storage preserved germination traits, with warming enhancing performance over time and reducing abnormal seedlings (−54.5%). Conversely, elevated CO₂ shortened GSI in late stages, impairing germination efficiency. Warming reduced Ae (−35%), He (−20%), and raised Fis (maternal 0.50, progeny 0.58), consistent with the species’ mixed mating system; A and Ho were unaffected. Allele frequency shifts suggested selective pressure under eTE. Warming induced slight structure in PCoA, and AMOVA detected 1% (maternal) and 9% (progeny) variation. Fst = 0.06 and Nm = 3.8 imply environmental influence without isolation. Conclusions: Warming significantly shapes seed quality, reproductive success, and genetic diversity in S. capitata. Improved reproduction and germination suggest adaptive advantages, but higher inbreeding and reduced diversity may constrain long-term resilience. The findings underscore the need for genetic monitoring and broader genetic bases in cultivars confronting environmental stressors. Full article

An efficient integrated energy system (IES) can enhance the potential of building energy conservation and carbon mitigation. However, imbalances between user-side demand and supply side output present formidable challenges to the operational dispatch of building energy systems. To mitigate heat rejection and improve dispatch optimization, an integrated building energy system incorporating waste heat recovery via an absorption heat pump based on the flow temperature model is adopted. A comprehensive analysis was conducted to investigate the correlation among heat pump operational strategies, thermal comfort, and the dynamic thermal storage capacity of piping network systems. The optimization calculations and comparative analyses were conducted across five cases on typical season days via the CPLEX solver with MATLAB R2018a. The simulation results indicate that the operational modes of absorption heat pump reduced the costs by 4.4–8.5%, while the absorption rate of waste heat increased from 37.02% to 51.46%. Additionally, the utilization ratio of battery and thermal storage units decreased by up to 69.82% at most after considering the pipeline thermal inertia and thermal comfort, thus increasing the system’s energy-saving ability and reducing the pressure of energy storage equipment, ultimately increasing the scheduling flexibility of the integrated building energy system. Full article

Over recent decades, phase-change materials (PCMs) have gained prominence as latent-heat thermal energy storage systems in building envelopes because of their high energy density. However, only PCMs that complete a full daily charge–discharge cycle can deliver meaningful energy and carbon-emission savings. This simulation study introduces a methodology that simultaneously optimizes PCM integration for storage efficiency, indoor thermal comfort, and energy savings. Two new indicators are proposed: overall storage efficiency (EC_n), which consolidates heating and cooling-efficiency ratios into a single value, and the performance factor (PF), which quantifies the PCM’s effectiveness in maintaining thermal comfort. Using EnergyPlus v8.9 coupled with DesignBuilder, a residential ASHRAE 90.1 mid-rise apartment was modeled in six warm-temperate (Cfb) European cities for the summer period from June 1 to August 31. Four paraffin PCMs (RT-22/25/28/31 HC, 20 mm thickness) were tested under natural and controlled ventilation strategies, with windows opening 50% when outdoor air was at least 2 °C cooler than indoors. Simulation outputs were validated against experimental cubicle data, yielding a mean absolute indoor temperature error ≤ 4.5%, well within the ±5% tolerance commonly accepted for building thermal simulations. The optimum configuration—RT-25 HC with temperature-controlled ventilation—achieved PF = 1.0 (100% comfort compliance) in all six cities and delivered summer cooling-energy savings of up to 3376 kWh in Paris, the highest among the locations studied. Carbon-emission reductions reached 2254 kg CO₂-e year⁻¹, and static payback periods remained below the assumed 50-year building life at a per kg PCM cost of USD 1. The EC_n–PF framework, therefore, provides a transparent basis for selecting cost-effective, energy-efficient, and low-carbon PCM solutions in warm-temperate buildings. Full article

Biobutanol is becoming more relevant as a promising alternative biofuel, primarily due to its advantageous characteristics. These include a higher energy content and density compared to traditional biofuels, as well as its ability to mix effectively with gasoline, further enhancing its viability as a potential replacement. A viable strategy for attaining carbon neutrality, reducing reliance on fossil fuels, and utilizing sustainable and renewable resources is the use of biomass to produce biobutanol. Lignocellulosic materials have gained widespread recognition as highly suitable feedstocks for the synthesis of butanol, together with various value-added byproducts. The successful generation of biobutanol hinges on three crucial factors: effective feedstock pretreatment, the choice of fermentation techniques, and the subsequent enhancement of the produced butanol. While biobutanol holds promise as an alternative biofuel, it is important to acknowledge certain drawbacks associated with its production and utilization. One significant limitation is the relatively high cost of production compared to other biofuels; additionally, the current reliance on lignocellulosic feedstocks necessitates significant advancements in pretreatment and bioconversion technologies to enhance overall process efficiency. Furthermore, the limited availability of biobutanol-compatible infrastructure, such as distribution and storage systems, poses a barrier to its widespread adoption. Addressing these drawbacks is crucial for maximizing the potential benefits of biobutanol as a sustainable fuel source. This document presents an extensive review encompassing the historical development of biobutanol production and explores emerging trends in the field. Full article

Heating, ventilation, and air-conditioning (HVAC) systems account for the largest share of energy consumption in European Union (EU) buildings, representing approximately 40% of the final energy use and contributing significantly to carbon emissions. Latent thermal energy storage (LTES) using phase change materials (PCMs) has emerged as a promising strategy to enhance HVAC efficiency. This review systematically examines the role of latent thermal energy storage using phase change materials (PCMs) in optimizing HVAC performance to align with EU climate targets, including the Energy Performance of Buildings Directive (EPBD) and the Energy Efficiency Directive (EED). By analyzing advancements in PCM-enhanced HVAC systems across residential and commercial sectors, this study identifies critical pathways for reducing energy demand, enhancing grid flexibility, and accelerating the transition to nearly zero-energy buildings (NZEBs). The review categorizes PCM technologies into organic, inorganic, and eutectic systems, evaluating their integration into thermal storage tanks, airside free cooling units, heat pumps, and building envelopes. Empirical data from case studies demonstrate consistent energy savings of 10–30% and peak load reductions of 20–50%, with Mediterranean climates achieving superior cooling load management through paraffin-based PCMs (melting range: 18–28 °C) compared to continental regions. Policy-driven initiatives, such as Germany’s renewable integration mandates for public buildings, are shown to amplify PCM adoption rates by 40% compared to regions lacking regulatory incentives. Despite these benefits, barriers persist, including fragmented EU standards, life cycle cost uncertainties, and insufficient training. This work bridges critical gaps between PCM research and EU policy implementation, offering a roadmap for scalable deployment. By contextualizing technical improvement within regulatory and economic landscapes, the review provides strategic recommendations to achieve the EU’s 2030 emissions reduction targets and 2050 climate neutrality goals. Full article

Direct air capture (DAC), as a complementary strategy to carbon capture and storage (CCS), offers a scalable and sustainable pathway to remove CO₂ directly from the ambient air. This study presents a detailed evaluation of the amine-functionalised metal-organic framework (MOF) sorbent, mmen-Mg₂(dobpdc), for DAC using a temperature–vacuum swing adsorption (TVSA) process. While this sorbent has demonstrated promising performance in point-source CO₂ capture, this is the first dynamic simulation-based study to rigorously assess its effectiveness for low-concentration atmospheric CO₂ removal. A transient one-dimensional TVSA model was developed in Aspen Adsorption and validated against experimental breakthrough data to ensure accuracy in capturing both the sharp and gradual adsorption kinetics. To enhance process efficiency and sustainability, this work provides a comprehensive parametric analysis of key operational factors, including air flow rate, temperature, adsorption/desorption durations, vacuum pressure, and heat exchanger temperature, on process performance, including CO₂ purity, recovery, productivity, and specific energy consumption. Under optimal conditions for this sorbent (vacuum pressure lower than 0.15 bar and feed temperature below 15 °C), the TVSA process achieved ~98% CO₂ purity, recovery over 70%, and specific energy consumption of about 3.5 MJ/KgCO₂. These findings demonstrate that mmen-Mg₂(dobpdc) can achieve performance comparable to benchmark DAC sorbents in terms of CO₂ purity and recovery, underscoring its potential for scalable DAC applications. This work advances the development of energy-efficient carbon removal technologies and highlights the value of step-shape isotherm adsorbents in supporting global carbon-neutrality goals. Full article

To address the challenges posed by the instability of renewable energy output and load fluctuations on grid operations and to support the low-carbon sustainable development of the energy system, this paper integrates artificial intelligence technology to establish an economic stability dispatch framework for distributed resource aggregators. A phased multi-contract collaborative scheduling model oriented toward sustainable development is proposed. Through intelligent algorithms, the model dynamically optimises decisions across the day-ahead and intraday phases: During the day-ahead scheduling phase, intelligent algorithms predict load demand and energy output, and combine with elastic performance-based response contracts to construct a user-side electricity consumption behaviour intelligent control model. Under the premise of ensuring user comfort, the model generates a 24 h scheduling plan with the objectives of minimising operational costs and efficiently integrating renewable energy. In the intraday scheduling phase, a rolling optimisation mechanism is used to activate energy storage capacity contracts and dynamic frequency stability contracts in real time based on day-ahead prediction deviations. This efficiently coordinates the intelligent frequency regulation strategies of energy storage devices and electric vehicle aggregators to quickly mitigate power fluctuations and achieve coordinated control of primary and secondary frequency regulation. Case study results indicate that the intelligent optimisation-driven multi-contract scheduling model significantly improves system operational efficiency and stability, reduces system operational costs by 30.49%, and decreases power purchase fluctuations by 12.41%, providing a feasible path for constructing a low-carbon, resilient grid under high renewable energy penetration. Full article