Search Results (14,205)

Models of heat transport in solids, being based on idealized elastic collisions of gas molecules, are flawed because heat and mass diffuse independently in solids but together in gas. To better understand heat transfer, an analytical, theoretical approach is combined with data from laser flash analysis, which is the most accurate method available. Dimensional analysis of Fourier’s heat equation shows that thermal diffusivity (D) depends on length-scale, which has been confirmed experimentally for metallic, semiconducting, and electrically insulating solids. A radiative diffusion model reproduces measured thermal conductivity (K = Dρc_P = D × density × specific heat) for thick solids from ~0 to >1200 K using idealized spectra represented by 2–4 parameters. Heat diffusion at laboratory temperatures (conduction) proceeds by absorption and re-emission of infrared light, which explains why heat flows into, through, and out of a material. Because heat added to matter performs work, thermal expansivity is proportional to ρc_P/Young’s modulus (i.e., rigidity or strength), which is confirmed experimentally over wide temperature ranges. Greater uptake of applied heat (e.g., c_P generally increasing with T or at certain phase transitions) reduces the amount of heat that can flow through the solid, but because K = Dρc_P, the rate (D) must decrease to compensate. Laser flash analysis data confirm this proposal. Transport properties thus depend on heat uptake, which is controlled by the interaction of light with the material under the conditions of interest. This new finding supports a radiative diffusion mechanism for heat transport and explains behavior from ~0 K to above melting. Full article

This paper addresses the potential of integrating a concentrating photovoltaic thermal (CPV/T) system with an absorption chiller for the purpose of space cooling in residential buildings in the United Arab Emirates (UAE). The proposed system consists of a low concentrating photovoltaic thermal (CPV/T) collector that utilizes mono-crystalline silicon photovoltaic (PV) cells integrated with a single-effect absorption chiller. The integrated system was modeled using the Transient System Simulation (TRNSYS v17) software. The obtained model was implemented in a case study represented by a villa situated in Abu Dhabi having a peak cooling load of 366 kW. The hybrid system was proposed to have a contribution of 60% renewable energy and 40% conventional nonrenewable energy. A feasibility study was carried out that demonstrated that the system could save approximately 670,700 kWh annually and reduce carbon dioxide emissions by 461 tons per year. The reduction in carbon dioxide emissions is equivalent of removing approximately 98 cars off the road. The payback period for the system was estimated to be 3.12 years. Full article

To implement the geographical and future climate adaptation of building moisture design for building projects, practitioners need efficient tools, such as precalculated climate indices to assess climate loads. Among them, the Frost Decay Exposure Index (FDEI) describes the risk of freezing damage for clay bricks in facades. Previously, the FDEI has been calculated for 12 locations in Norway using 1961–1990 measurements. The purpose of this study is both updating the FDEI values with new climate data and future scenarios and assessing how such indices may be suitable as a climate adaptation tool in building moisture safety design. The validity of FDEI as an expression of frost decay potential is outside the scope of this study. Historical data from the last normal period as well as future estimated climate data based on 10 different climate models forced by two emission scenarios (representative concentration pathways 4.5 and 8.5) have been analyzed. The results indicate an overall decline in FDEI values on average, due to increased winter temperatures leading to fewer freezing events. Further, the variability between climate models and scenarios necessitates explicit uncertainty evaluations, as single climate model calculations may result in misleading conclusions due to high variability between models. Full article

Intelligent loading systems are extensively employed in coal enterprises. Nevertheless, pre-loading customer vehicle scheduling predominantly depends on manual expertise. This frequently results in extended vehicle waiting periods, elevated carbon emissions, and reduced customer satisfaction, particularly in multi-customer scenarios. Therefore, this study introduces a multi-customer vehicle scheduling optimization approach for an intelligent coal loading system. Customer priorities are first identified to enhance satisfaction. Considering various customers and enterprise factors, the multi-customer vehicle scheduling model is established to minimize the total cost. The optimal vehicle scheduling scheme is obtained by using the enhanced sparrow search algorithm. The validity of the proposed approach is demonstrated through a case study of a coal mining enterprise. The results show that the total cost of the optimized plan was 79% lower than the traditional plan, which means a significant reduction in vehicle waiting time, and an improvement in customer satisfaction. Full article

The ecotone zone, located between the Cerrado and Amazon biomes, has been under intensive anthropogenic pressures due to the expansion of commodity agriculture and extensive cattle ranching. This has led to habitat loss, reducing biodiversity, depleting biomass, and increasing CO₂ emissions. In this study, we employed an artificial neural network, field data, and remote sensing techniques to develop a model for estimating biomass in the remaining native vegetation within an 18,864 km² ecotone region between the Amazon and Cerrado biomes in the state of Mato Grosso, Brazil. We utilized field data from a plant ecology laboratory and vegetation indices from Sentinel-2 satellite imagery and trained artificial neural networks to estimate aboveground biomass (AGB) in the study area. The optimal network was chosen based on graphical analysis, mean estimation errors, and correlation coefficients. We validated our chosen network using both a Student’s t-test and the aggregated difference. Our results using an artificial neural network, in combination with vegetation indices such as AFRI (Aerosol Free Vegetation Index), EVI (Enhanced Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index), which show an accurate estimation of aboveground forest biomass (Root Mean Square Error (RMSE) of 15.92%), can bolster efforts to assess biomass and carbon stocks. Our study results can support the definition of environmental conservation priorities and help set parameters for payment for ecosystem services in environmentally sensitive tropical regions. Full article

The growing concern over pollution from vehicle exhausts has underscored the need for effective detection of black smoke emissions from motor vehicles. We believe that the optimal approach for the detection of black smoke is to leverage existing roadway CCTV cameras. To facilitate this, we have collected and publicly released a black smoke detection dataset sourced from roadway CCTV cameras in China. After analyzing the existing detection methods on this dataset, we found that they have subpar performance. As a result, we decided to develop a novel detection model that focuses on temporal information. This model utilizes the continuous nature of CCTV video feeds rather than treating footage as isolated images. Specifically, our model incorporates a 3D convolution module to capture short-term dynamic and semantic features in consecutive black smoke video frames. Additionally, a cross-scale feature fusion module is employed to integrate features across different scales, and a self-attention mechanism is used to enhance the detection of black smoke while minimizing the impact of noise, such as occlusions and shadows. The validation of our dataset demonstrated that our model achieves a detection accuracy of 89.42%,showing around 3% improvement over existing methods. This offers a novel and effective solution for black smoke detection in real-world applications. Full article

This study focused on optimizing a model house for different locations and types of thermal systems to understand better how heating system type affects thermal envelope design. The study investigated six different thermal system configurations in separate optimizations for five locations. Optimization implies reducing energy consumption, minimizing greenhouse emissions (GHG), lowering operational costs, ensuring regulatory compliance, enhancing resilience, and improving occupant comfort and health. The Pareto front, multi-objective optimization, is used to identify a set of optimal solutions, considering multiple goals that may conflict with each other. In determining the least-cost building design envelope, the design balances costs with other goals, such as energy efficiency and environmental impact. The optimizations determine the life-cycle cost versus operational GHG emissions for a single-detached house in Canadian locations with varying climates, emissions factors, and energy costs. Besides natural gas, the study evaluated four electricity-heated options: (a) an air-source heat pump, (b) a ductless mini-split heat pump, (c) a ground-source heat pump, and (d) an electric baseboard. A net-zero-carbon design with grid-tied photovoltaics was also optimized. Results indicate that the heating system type influences the optimal enclosure design. In each location, at least one all-electric kind of design has a lower life-cycle cost than the optimized gas-heated model, and such designs can mitigate the majority of operational GHG emissions from new housing in locations with a low carbon electricity supply. Full article

Carbon emission reduction strategies are being implemented in the transportation sector by encouraging the adoption of eco-friendly vehicles and introducing demand management policies such as Mobility as a Service (MaaS). Nevertheless, the efficacy of MaaS in reducing carbon emissions remains uncertain. This study introduces Sustainable Public Transit (SPT) as a public transit alternative consisting of only green modes to promote sustainability. We explore the preferences of SPT in a commuting context, incorporating individual preference heterogeneity in a discrete choice model. We systematically identify the relationship between choice behaviors and individual heterogeneity in alternative attributes and psychological factors stemming from socio-demographic characteristics. The integrated choice and latent variable (ICLV) model with a mixed logit form is adopted, and the key findings can be summarized as follows: Preference heterogeneity is observed in the travel cost variable, which can be explained by characteristics such as the presence of a preschooler, household size, and income. CO₂ emissions do not have a statistically significant impact on choices. Furthermore, psychological factors are also explained through socio-demographic characteristics, and it is found that low-carbon knowledge positively influences low-carbon habits. Psychological factors significantly affect choices. Respondents who dislike transfers and prioritize punctuality are less likely to choose SPT, while those who have positive low-carbon attitudes are more likely to do so. Finally, scenario analysis is conducted to forecast mode share based on improvements in SPT alternative attributes and variations in attribute levels. Policy implications are then provided to enhance the acceptability of SPT. Full article

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century, has been considerably affecting short-term weather conditions, such as increasing weather variability and the incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, the spatial error model (SEM) and the spatial Durbin model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches, and then over that threshold, milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance. Full article

This paper investigates the development of low-carbon buildings and carbon emission control in Jiangsu Province, China, utilizing a mathematical model. Through correlation analysis and principal component analysis, the carbon emissions of the entire life cycle of residential buildings are evaluated, and a Grey Prediction Model is established. The study shows that the annual carbon emission from air conditioners is 370.92 kg, given an annual electricity consumption of 1324.71 kW and a carbon emission of 0.28 kg per kWh. It identifies the key carbon emission indicators, including precipitation, temperature, energy consumption, building area, construction materials, water, natural gas, and waste. Principal component analysis ranks building area as the most significant factor. Using the GM (1,1) model, the carbon emissions of Jiangsu Province in 2024 were predicted to be 1.5576 million tons by historical data. Emission reduction suggestions are proposed, such as constructing thicker walls, increasing green spaces, reducing construction waste, and promoting balanced economic development. Moreover, prioritizing insulation materials in building design can reduce winter energy consumption since energy consumption is higher in winter than in summer. This research supports China’s goals of achieving a carbon peak by 2030 and carbon neutrality by 2060 while encouraging low-carbon technological innovation and improving people’s living standards. This study also emphasizes the importance of locally tailored strategies for effective emissions reduction. Full article

The organic Rankine cycle (ORC) system is an important technology for recovering energy from the waste heat of internal combustion engines, which is of significant importance for the improvement of fuel utilization. This study analyses the performance of vehicle ORC systems and proposes a rapid optimization method for enhancing vehicle ORC performance. This study constructed a numerical simulation model of an internal combustion engine-ORC waste heat recovery system based on GT-Suite software v2016. The impact of key operating parameters on the performance of two organic Rankine cycles: the simple organic Rankine cycle (SORC) and the recuperative organic Rankine cycle (RORC) was investigated. In order to facilitate real-time prediction and optimization of system performance, a data-driven rapid prediction model of the performance of the waste heat recovery system was constructed based on an artificial neural network. Meanwhile, the NSGA-II multi-objective algorithm was used to investigate the competitive relationship between different performance objective functions. Furthermore, the optimal operating parameters of the system were determined by utilizing the TOPSIS method. The results demonstrate that the highest thermal efficiencies of the SORC and RORC are 6.21% and 8.61%, respectively, the highest power outputs per unit heat transfer area (POPAs) are 6.98 kW/m² and 8.99 kW/m², respectively, the lowest unit electricity production costs (EPC) are 7.22 × 10⁻² USD/kWh and 3.15 × 10⁻² USD/kWh, respectively, and the lowest CO₂ emissions are 2.85 ton CO_2,eq and 3.11 ton CO_2,eq, respectively. The optimization results show that the RORC exhibits superior thermodynamic and economic performance in comparison to the SORC, yet inferior environmental performance. Full article

The control system of oil-fired boiler units on ships plays a crucial role in reducing the emissions of atmospheric pollutants such as nitrogen oxides (NOx), sulfur dioxides (SO2), and carbon dioxide (CO2). Traditional control methods using conventional measurement sensors face limitations in real-time control due to response delays, which has led to the growing interest in combustion control methods using flame images. To ensure the precision of such combustion control systems, the system model must be thoroughly considered during controller design. However, finding the optimal tuning point is challenging due to the changes in the system model and nonlinearity caused by environmental variations. This study proposes a controller that integrates an internal model control (IMC)-based PID controller with the deep deterministic policy gradient (DDPG) algorithm of deep reinforcement learning to enhance the adaptability of image-based combustion control systems to environmental changes. The proposed controller adjusts the PID parameter values in real-time through the learning of the determination constant lambda (λ) of the IMC internal model. This approach reduces computational resources by shrinking the learning dimensions of the DDPG agent and limits transient responses through constrained learning of control parameters. Experimental results show that the proposed controller exhibited rapid adaptive performance in the learning process for the target oxygen concentration, achieving a reward value of −0.05 within just 105 episodes. Furthermore, when compared to traditional PID tuning methods, the proposed controller demonstrated superior performance, achieving a target value error of 0.0032 and a low overshoot range of 0.0498 to 0.0631, providing the fastest response speed and minimal oscillation. Additionally, experiments conducted on an actual operating ship verified the practical feasibility of this system, highlighting its potential for real-time control and pollutant reduction in marine applications. Full article

Reservoirs are an important source of methane (CH₄) emissions, but the relative contribution of CH₄ ebullition and diffusion fluxes to total fluxes has received little attention in the past. In this study, we systematically monitored the CH₄ fluxes of nine cascade reservoirs (Dahejia, Jishixia, Huangfeng, Suzhi, Kangyang, Zhiganglaka, Lijiaxia, Nina, and Longyangxia) in the upper reaches of the Yellow River in the dry (May 2023) and wet seasons (August 2023) using the static chamber gas chromatography and headspace equilibrium methods. We also simultaneously measured environmental physicochemical properties as well as the abundance of methanogens and methanotrophs in sediments. The results showed the following: (1) All reservoirs were sources of CH₄ emissions, with an average diffusion flux of 0.08 ± 0.05 mg m⁻² h⁻¹ and ebullition flux of 0.38 ± 0.41 mg m⁻² h⁻¹. Ebullition flux accounted for 78.01 ± 7.85% of total flux. (2) Spatially, both CH₄ diffusion and ebullition fluxes increased from upstream to downstream. Temporally, CH₄ diffusion flux in the wet season (0.09 ± 0.06 mg m⁻² h⁻¹) was slightly higher than that in the dry season (0.08 ± 0.04 mg m⁻² h⁻¹), but CH₄ ebullition flux in the dry season (0.38 ± 0.48 mg m⁻² h⁻¹) was higher than that in the wet season (0.32 ± 0.2 mg m⁻² h⁻¹). (3) qPCR showed that methanogens (mcrA gene) were more abundant in the wet season (5.43 ± 3.94 × 10⁵ copies g⁻¹) than that in the dry season (3.74 ± 1.34 × 10⁵ copies g⁻¹). Methanotrophs (pmoA gene) also showed a similar trend with more abundance found in the wet season (7 ± 2.61 × 10⁵ copies g⁻¹) than in the dry season (1.47 ± 0.92 × 10⁵ copies g⁻¹. (4) Structural equation modeling revealed that the ratio of mcrA/pmoA genes, water N/P, and reservoir age were key factors affecting CH₄ ebullition flux. Variation partitioning further indicated that the ratio of mcrA/pmoA genes was the main factor causing the spatial variation in CH₄ ebullition flux, explaining 35.69% of its variation. This study not only reveals the characteristics and influencing factors of CH₄ emissions from cascade reservoirs on the Qinghai Plateau but also provides a scientific basis for calculating fluxes and developing global CH₄ reduction strategies for reservoirs. Full article

The integration of numerous distributed energy sources into the power system offers exciting opportunities to enhance the resilience of distribution networks. It is worth noting that the renewable-to-ammonia system has the potential to alleviate the multi-temporal and spatial imbalance of the power system. Therefore, this paper proposes a mathematical model for a renewable-to-ammonia system, taking into account the material balance and power balance of each unit. Based on this, this paper further explores the optimization scheduling method for flexible ammonia loads in distribution networks. A relaxation method for branch flow models in distribution networks based on second-order cone programming is proposed. An optimization scheduling model for flexible ammonia loads in distribution networks is constructed to minimize network loss. Moreover, considering the environmental advantages of the renewable-to-ammonia system, this paper compares the changes in hydrogen production technologies under different carbon emission constraints. Finally, a case study of the IEEE 33-node system is adopted to verify the effectiveness of the proposed model and method. It indicates that the renewable-to-ammonia system has environmental benefits and can reduce network loss to a certain extent. Full article

Urbanization affects economic production activities and energy demand, as well as lifestyle and consumption behavior, affecting carbon dioxide emissions. This study constructs the System Generalized Method of Moments (Sys-GMM) model of the impact of urbanization rate on carbon dioxide emissions based on panel data of 136 countries and regions in the world from 1990 to 2020, grounded on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. This study found that (1) there is a negative relationship between urbanization rate and CO₂ emissions from 1990 to 2020. (2) The impact of the urbanization rate on CO₂ emissions is heterogeneous. An increase in urbanization rate in non-OECD countries significantly reduces CO₂ emissions, while the effect is not significant in OECD countries. (3) The carbon intensity of fossil energy consumption moderates the relationship between urbanization rate and CO₂ emissions, weakening the effect of urbanization rate on CO₂ emissions. Based on these findings, policy recommendations such as promoting urbanization and increasing the regulation and control of fossil energy carbon intensity are proposed. Full article