Search Results (2,126)

LPG fuel supply systems are increasingly important for improving energy efficiency and reducing carbon emissions in the shipping industry. The primary objective of this research is to investigate the heat transfer phenomena to enhance the thermal performance of double-pipe heat exchangers (DPHEs) in LPG fuel supply systems. This study investigates the heat transfer performance of a glycol–steam double-pipe heat exchanger (DPHE) within an LPG fuel supply system under varying operating conditions. A computational model and methodology were developed and validated by comparing the numerical results with experimental data obtained from commissioning tests. Additionally, the effects of turbulence models and parametric variations were evaluated by analyzing the glycol–water mixing ratio and flow direction—both of which are critical operational parameters for DPHE systems. Numerical validation against the commissioning data showed a deviation of ±2% under parallel-flow conditions, confirming the reliability of the proposed model. With respect to the glycol–water mixing ratio and flow configuration, thermal conductance (UA) decreased by approximately 11% in parallel flow and 13% in counter flow for every 20% increase in glycol concentration. Furthermore, parallel flow exhibited approximately 0.6% higher outlet temperatures than counter flow, indicating superior heat transfer efficiency under parallel-flow conditions. Finally, the heat transfer behavior of the DPHE was further examined by considering the effects of geometric characteristics, pipe material, and fluid properties. This study offers significant contributions to the engineering design of double-pipe heat exchanger systems for LPG fuel supply applications. Full article

The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development. Full article

A computational fluid dynamics (CFD) numerical simulation methodology was implemented to model transient heating processes in steel industry reheating furnaces, targeting combustion efficiency optimization and carbon emission reduction. The effects of oxygen concentration (O₂%) and different fuel types on the flow and heat transfer characteristics were investigated under both oxygen-enriched combustion and MILD oxy-fuel combustion. The results indicate that MILD oxy-fuel combustion promotes flue gas entrainment via high-velocity oxygen jets, leading to a substantial improvement in the uniformity of the furnace temperature field. The effect is most obvious at O₂% = 31%. MILD oxy-fuel combustion significantly reduces NOx emissions, achieving levels that are one to two orders of magnitude lower than those under oxygen-enriched combustion. Under MILD conditions, the oxygen mass fraction in flue gas remains below 0.001 when O₂% ≤ 81%, indicating effective dilution. In contrast, oxygen-enriched combustion leads to a sharp rise in flame temperature with an increasing oxygen concentration, resulting in a significant increase in NOx emissions. Elevating the oxygen concentration enhances both thermal efficiency and the energy-saving rate for both combustion modes; however, the rate of improvement diminishes when O₂% exceeds 51%. Based on these findings, MILD oxy-fuel combustion using mixed gas or natural gas is recommended for reheating furnaces operating at O₂% = 51–71%, while coke oven gas is not. Full article

This study employs k-means clustering to group 30 Chinese provinces into four CO₂ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While Shanghai, Jiangxi, and Hebei retained their original classifications, provinces such as Beijing, Fujian, Tianjin, and Anhui transitioned from higher to lower emission patterns, indicating notable reversals in emission trajectories. To identify the determinants of these transitions, GDP growth rate, population growth rate, and energy investment are incorporated as time varying covariates. The empirical findings demonstrate that GDP growth substantially increases interpattern mobility, thereby weakening state persistence, whereas population growth and energy investment tend to reinforce emission pattern stability. These results imply that policy responses must be tailored to regional dynamics. In rapidly growing regions, fiscal incentives and technological upgrading may facilitate downward transitions in emission states, whereas in provinces where emissions remain persistent due to demographic or investment related rigidity, structural adjustments and long term mitigation frameworks are essential. The study underscores the importance of integrating economic, demographic, and investment characteristics into carbon reduction strategies through a region specific and data informed approach. Full article

The main advantage of using biomass for energy generation is the reduction in carbon dioxide emissions. For a fast reduction effect, it is important to use biomass characterised by an annual growth cycle. These may be fallen leaves. The fuel properties of the leaves can change during the growth period. These changes can result from both the natural growth process and environmental factors—particulate matter adsorption. The main objective was to determine changes in the characteristics of leaves and needles during the growth period (from May to October). Furthermore, to determine the effect of adsorbed particulate matter, the washing process was carried out. Studies were carried out for three tree species: Norway maple, horse chestnut and European larch. Proximate and ultimate analysis was performed and mercury content was determined. During the growth period, beneficial changes were observed: an increase in carbon content and a decrease in hydrogen and sulphur content. The unfavourable change was a significant increase in ash content, which caused a decrease in calorific value. The increase in ash content was caused by adsorbed particulate matter. They were mostly absorbed by the tissues of the needle and leaves and could not be removed by washing the surface. Full article

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

The accelerating integration of electric vehicles (EVs) into contemporary transportation infrastructure has underscored significant limitations in traditional charging paradigms, particularly in accommodating heterogeneous user requirements within dynamic operational environments. This study presents a differentiated optimization framework for EV charging strategies through the systematic classification of user types. A multidimensional decision-making environment is established for three representative user categories—residential, commercial, and industrial—by synthesizing time-variant electricity pricing models with dynamic carbon emission pricing mechanisms. A bi-level optimization architecture is subsequently formulated, leveraging deep reinforcement learning (DRL) to capture user-specific demand characteristics through customized reward functions and adaptive constraint structures. Validation is conducted within a high-fidelity simulation environment featuring 90 autonomous EV charging agents operating in a metropolitan parking facility. Empirical results indicate that the proposed typology-driven approach yields a 32.6% average cost reduction across user groups relative to baseline charging protocols, with statistically significant improvements in expenditure optimization (p < 0.01). Further interpretability analysis employing gradient-weighted class activation mapping (Grad-CAM) demonstrates that the model’s attention mechanisms are well aligned with theoretically anticipated demand prioritization patterns across the distinct user types, thereby confirming the decision-theoretic soundness of the framework. Full article

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. 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

In the era of “carbon dioxide peaking and carbon neutrality”, low-carbon development of road traffic and transportation has now become a rigid demand in China. Considering the fact that socioeconomic and demographic characteristics vary significantly across Chinese cities, proper city-level transportation development strategies should be established. Using detailed data from cities at prefecture level and above in China, this study investigates the spatially heterogeneous effects of various factors on road traffic CO₂ emissions. Another theoretical issue is concerned with the analytic method for zonal CO₂ emission modeling. We combine the concepts of geographically weighted regression (GWR) and machine learning for nonparametric regression, proposing a modified random forest (RF) algorithm, named “geographically weighted random forest” (GWRF). Our empirical analysis indicates that, when an appropriate weight parameter is applied, GWRF is able to achieve significantly superior performance compared to both the traditional RF and GWR methods. Moreover, the influences of various explanatory variables on CO₂ emissions differ across cities. These findings suggest that low-carbon transportation strategies should be customized to reflect regional heterogeneity, rather than relying on a unified national policy. Full article

Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of the single-objective approach in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility. Full article

Co-firing large-sized straw biomass in pulverized coal boilers is a potential pathway for carbon emission reduction in China’s thermal power plants. However, experimental data on large-sized straw combustion under pulverized coal boiler combustion conditions are critically lacking. This study selected typical large-sized wheat straw particles. Employing a two-mode experimental setup in a drop tube furnace (DTF) system simulating pulverized coal boiler conditions, we systematically investigated the combustion behavior and alkali metal release characteristics of this large-sized straw biomass, with combustion processes summarized for diverse particle types. The findings reveal asynchronous combustion progression across particle surfaces due to heterogeneous mass transfer and gas diffusion; unique behaviors distinct from denser woody biomass, including bending deformation, fiber branching, and fragmentation, occur; significant and morphology-specific deformations occur during devolatilization; fragmentation universally produces particles of varied shapes (needle-like, flaky, blocky, semi-tubular) during char combustion; and potassium release exceeds 35% after complete devolatilization and surpasses 50% at a burnout degree exceeding 80%. This work provides essential experimental data on the fundamental combustion characteristics and alkali metal release of large-sized wheat straw particles under pulverized coal boiler combustion conditions, offering engineering application guidance for the direct co-firing of large-sized flexible straw biomass in pulverized coal boilers. Full article

The current research trends of production engineering are based on optimizing the machining process concerning human and environmental factors. High-performance materials, such as hardened steels, nickel-based alloys, fiber-reinforced polymer (FRP) composites, and titanium alloys, are classified as hard-to-cut due to their ability to maintain strength at high operating temperatures. Due to these characteristics, such materials are employed in applications such as aerospace, marine, energy generation, and structural. The purpose of this article is to investigate the machinability of these alloys under various cutting conditions. The purpose of this article is to compare cryogenic cooling and cryogenic processing from the perspective of machinability and sustainability in the manufacturing process. Compared to conventional machining, hybrid techniques, which mix cryogenic and minimal quantity lubricant, led to significantly reduced cutting forces of 40–50%, cutting temperatures and surface finishes by approximately 20–30% and more than 40%, respectively. A carbon footprint is determined by several factors including power consumption, energy requirements, and carbon dioxide emissions. As a result of the cryogenic technology, the energy consumption, power consumption, and CO₂ emissions were reduced by 40%, 28%, and 35%. Full article

With the intensification of urbanization, resulting in the growing building stock, building operations have become the main contributors to greenhouse gas emissions. However, the relationship between urban form and carbon emissions remains unclear, which limits the sustainable development of cities. This study reviews the definition of carbon sources, data characteristics, and evaluation methods of carbon emissions. In addition, the impact of urban form on building carbon emissions at the macro, meso, and micro scales is reviewed, and low-carbon design strategies for urban form are discussed. Finally, the existing problems in this field are pointed out, and future research directions are proposed. Our review found that small and medium-sized compact cities tend to have less carbon emissions, while large cities and megacities with compact urban forms have more carbon emissions. The carbon reduction design of urban form at the meso scale is often achieved by improving the microclimate. Developing a research framework for the impact mechanism of building carbon emissions in a coordinated manner with multi-scale urban forms can effectively promote the development of low-carbon sustainable cities. This review can assist urban planners and energy policymakers in selecting appropriate methods to formulate and implement low-carbon city analysis and planning projects based on limited available resources. Full article