Search Results (101)

Driven by stringent emission regulations, advanced combustion modes utilizing turbulent jet ignition technology are pivotal for enhancing the performance of marine low-speed natural gas dual-fuel engines. This review focuses on three novel combustion modes, yielding key conclusions: (1) Compared to the conventional DJCDC mode, the TJCDC mode exhibits a significantly higher swirl ratio and turbulence kinetic energy in the main chamber during initial combustion. This promotes natural gas jet development and combustion acceleration, leading to shorter ignition delay, reduced combustion duration, and a combustion center (CA50) positioned closer to the Top Dead Center (TDC), alongside higher peak cylinder pressure and a faster early heat release rate. Energetically, while TJCDC incurs higher heat transfer losses, it benefits from lower exhaust energy and irreversible exergy loss, indicating greater potential for useful work extraction, albeit with slightly higher indicated specific NOx emissions. (2) In the high-compression ratio TJCPC mode, the Liquid Pressurized Natural Gas (LPNG) injection parameters critically impact performance. Delaying the start of injection (SOI) or extending the injection duration degrades premixing uniformity and increases unburned methane (CH₄) slip, with the duration effects showing a load dependency. Optimizing both the injection timing and duration is, therefore, essential for emission control. (3) Increasing the excess air ratio delays the combustion phasing in TJCPC (longer ignition delay, extended combustion duration, and retarded CA50). However, this shift positions the heat release more optimally relative to the TDC, resulting in significantly improved indicated thermal efficiency. This work provides a theoretical foundation for optimizing high-efficiency, low-emission combustion strategies in marine dual-fuel engines. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

This study investigates the impact of different combustion control strategies on marine engine combustion and emission characteristics at a high ammonia energy ratio. Compared to the strategy of maintaining a constant fuel injection duration, the strategy of keeping the fuel injection pressure constant allows the kinetic energy of diesel to remain at a higher level. This results in an increase in combustion efficiency and indicated the thermal efficiency of the engine, while also reducing CO₂ and soot emissions. However, when the ammonia energy ratio increases to more than 50%, the indicated thermal efficiency starts to decrease along with the increase in the emissions of N₂O and unburned ammonia. To address these issues, one of the potential means is to improve the in-cylinder combustion environment by increasing the in-cylinder gas temperature. This can enhance combustion efficiency and ultimately optimize the performance and emission characteristics of dual-fuel engines, which results in an increase in the combustion efficiency to 98% and indicated thermal efficiency to 54.47% at a relatively high ammonia energy ratio of 60%. Emission results indicate that N₂O emissions decrease from 1099 ppm to 25 ppm, while unburned ammonia emissions drop from 16016 ppm to 100 ppm. Eventually, the greenhouse gas emissions were reduced by about 85.3% in comparison with the baseline case. Full article

Accurate prediction of ship fuel consumption is essential for improving energy efficiency, optimizing mission planning, and ensuring operational integrity at sea. However, during complex tasks such as high-speed maneuvers, fuel consumption exhibits complex dynamics characterized by the coexistence of baseline drift and transient peaks that conventional models often fail to capture accurately, particularly the abrupt peaks. In this study, a hybrid prediction model, DGM-SVR, is presented, combining a rolling dynamic grey model (DGM (1,1)) with support vector regression (SVR). The DGM (1,1) adapts to the dynamic fuel consumption baseline and trends via a rolling window mechanism, while the SVR learns and predicts the residual sequence generated by the DGM, specifically addressing the high-amplitude fuel spikes triggered by maneuvers. Validated on a simulated dataset reflecting typical fuel spike characteristics during high-speed maneuvers, the DGM-SVR model demonstrated superior overall prediction accuracy (MAPE and RMSE) compared to standalone DGM (1,1), moving average (MA), and SVR models. Notably, DGM-SVR reduced the test set’s MAPE and RMSE by approximately 21% and 34%, respectively, relative to the next-best DGM model, and significantly improved the predictive accuracy, magnitude, and responsiveness in predicting fuel consumption spikes. The findings indicate that the DGM-SVR hybrid strategy effectively fuses DGM’s trend-fitting strength with SVR’s proficiency in capturing spikes from the residual sequence, offering a more reliable and precise method for dynamic ship fuel consumption forecasting, with considerable potential for ship energy efficiency management and intelligent operational support. This study lays a foundation for future validation on real-world operational data. Full article

The metallurgical industry has been constantly changing over the past decades. On the one hand, there has been the modernization and improvement of production efficiency, and on the other hand, we have seen a reduction in the negative impact on the environment. The possibility of using alternative materials and the circular economy is significant in this area. In the present work, research was carried out to determine the usefulness of biomass in the form of fruit seeds for the recycling processes of metal-bearing raw materials, including slags from copper production processes, which are characterized by a much higher metal content than ores of this metal. The main carbon-bearing material/reducer used in the process is metallurgical coke. The transformation of the European metal industry has been observed in recent years. To carry out the physicochemical characterization of the tested material, a research methodology was adopted using tools to determine the stability of behavior at high temperatures, chemical composition, and volatile components. Thermodynamic analysis was carried out, indicating the theoretical course of reactions of individual system components and thermal effects, allowing a determination of whether the assumed reactions are endothermic or exothermic. The planned research ends with the reduction process in conditions similar to those carried out in industrial conditions. Enforced by the guidelines for reducing CO₂ emissions, it contributes to a significant reduction in the demand for coke. This paper addresses the issue of determining the feasibility of using selected bioreducers, including cherry stones, to verify their suitability in the process of reducing copper oxides. The study used copper slag with a composition similar to slags generated at the copper production stage in a flash furnace. The results obtained in reducing copper content above 98 wt. % indicate the great potential of this type of bioreducer. It should be noted that, unlike conventional fossil fuels, the use of cherry stones to reduce copper slag can be considered an environmentally neutral method of carbon offset. The resulting secondary slag is a waste product that can be stored and disposed of without harmful environmental effects due to its low lead content. An additional advantage is the relatively wide availability of cherry stones. Full article

Supercritical combustion is a promising technique for improving the efficiency and reducing the emissions of next-generation gas turbines. However, accurately modeling combustion under these conditions remains a challenge, particularly due to the complexity of chemical kinetics. This study aims to evaluate the applicability of a reduced global reaction mechanism compared to the detailed Foundational Fuel Chemistry Model 1.0 (FFCM-1) when performing hydrogen combustion with supercritical carbon dioxide and argon as diluents. Computational fluid dynamics simulations were conducted in two geometries: a simplified tube for isolating chemical effects and a combustor with cooling channels for practical evaluation. The analysis focuses on the evaluation of velocity, temperature, and the water vapor mass fraction distributions inside the combustion chamber. The results indicate good agreement between the global and detailed mechanisms, with average relative errors below 2% for supercritical argon and 4% for supercritical carbon dioxide. Both models captured key combustion behaviors, including buoyancy-driven flame asymmetry caused by the high density of supercritical fluids. The findings suggest that global chemistry models can serve as efficient tools for simulating supercritical combustion processes, making them valuable for the design and optimization of future supercritical gas turbine systems. Full article

Microbial fuel cells (MFCs) generate electricity through the microbial oxidation of organic waste. However, the inherent electrochemical performance of carbon felt (CF) electrodes is relatively poor and requires enhancement. In this study, nickel oxide (NiO) was successfully loaded onto CF to improve its electrode performance, thereby enhancing the electricity generation capacity of MFCs during the degradation of treated wastewater. Scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy diffusion spectrometer (EDS) analyses confirmed the successful deposition of NiO on the CF surface. The modification enhanced both the conductivity and capacitance of the electrode and increased the number of microbial attachment sites on the carbon fiber filaments. The prepared CF–NiO electrode was employed as the anode in an MFC, and its electrochemical and energy storage performance were evaluated. The maximum power density of the MFC with the CF–NiO anode reached 0.22 W/m², compared to 0.08 W/m² for the unmodified CF anode. Under the C1000-D1000 condition, the charge storage capacity and total charge output of the CF–NiO anode were 1290.03 C/m² and 14,150.03 C/m², respectively, which are significantly higher than the 452.9 C/m² and 6742.67 C/m² observed for the CF anode. These results indicate notable improvements in both power generation and energy storage performance. High-throughput gene sequencing of the anodic biofilm following MFC acclimation revealed that the CF–NiO anode surface hosted a higher proportion of electroactive bacteria. This suggests that the NiO modification enhances the biodegradation of organic matter and improves electricity generation efficiency. Full article

The construction industry in China has long been confronted with significant concerns related to fossil fuel dependence and low energy efficiency. However, under the policy guidance of China’s “dual carbon” goals, it has emerged as the leading sector in achieving a reduction in carbon emissions through technological innovation in recent years. To accurately assess the carbon emission reduction potential of the construction industry and support the attainment of the dual carbon goals, this study constructs a generalized induced ordered weighted averaging (GIOWA) combination forecasting model, integrating support vector regression (SVR) and a long short-term memory neural network (LSTM). A case study is conducted based on historical data (1997–2021) from the construction industry, and the research findings indicate that: (1) the GIOWA combination forecasting model effectively integrates the algorithmic strengths of SVR and LSTM, achieving an average prediction accuracy of 98.16%, which signifies a remarkable improvement over both individual models; (2) the carbon emissions in China’s construction industry will maintain a downward trend during the period 2022–2030, although the decline rate is expected to decrease gradually; (3) by 2030, a reduction of nearly 35% in carbon emissions is anticipated relative to the historical peak. This study provides evidence-based decision support for relevant policy formulation. Full article

In this research, we present the development and validation of a compact, resource-efficient (low-cost, low-energy), distributed, real-time traffic and air quality monitoring system. Deployed since November 2023 in a small town that relies on burning various fuels and waste for winter heating, the system comprises three IoT units that integrate image processing and environmental sensing for sustainable urban and environmental management. Each unit uses an embedded camera and sensors to process live data locally, which are then transmitted to a central database. The image processing algorithm counts vehicles by type with over 95% daylight accuracy, while air quality sensors measure pollutants including particulate matter (PM), equivalent carbon dioxide (eCO₂), and total volatile organic compounds (TVOCs). Data analysis revealed fluctuations in pollutant concentrations across monitored areas, correlating with traffic variations and enabling the identification of pollution sources and their relative impacts. Recorded PM10 daily average levels even reached eight times above the safe 24 h limits in winter, when traffic values were low, indicating a strong link to household heating. This work provides a scalable, cost-effective approach to traffic and air quality monitoring, offering actionable insights for urban planning and sustainable development. Full article

Optical properties and chemical composition of atmospheric fine particles (PM_2.5) are critical to their environmental and health effects. In this study, we analyzed the organic aerosols (OA) in PM_2.5 samples in Nanjing, China, collected during the summer and winter of 2019 and 2023. Results show a decline in both concentrations and light-absorbing abilities of methanol—soluble organic carbon (MSOC) and water-soluble OC (WSOC) in OA from 2019 to 2023. Due to increased combustion activities, MSOC and WSOC concentrations, and their corresponding mass absorption efficiencies were all higher in winter than in summer. Furthermore, fluorescence indices suggest that OA in Nanjing was influenced by a mix of microbial/biogenic sources. Fluorescent properties of both WSOC and MSOC were dominated by humic-like components but the remaining contribution from protein-like components was more significant in MSOC. The molecular composition of OA did not show a remarkable difference between 2019 and 2023. Overall, CHON compounds were the most abundant species, followed by CHO and CHN compounds, and aliphatic compounds dominated all molecular types except for CHN (in positive mode) and CHON, CHOS (in negative mode). Regarding the OA sources, the numbers of molecules from fossil fuel combustion and biomass burning (BB) were a bit more in 2023 than in 2019, and signal intensities of BB-related molecules were also higher in winter than in summer; the presence of organosulfates indicate the contribution of aqueous-phase oxidation to OA, especially during high relative humidity conditions. At last, correlations between OA molecules and light absorption efficiencies indicate that the key light-absorbing species in winter and summer were likely quite different despite similar chemical compositions, and in summer, CH and CHN compounds were important to light absorption, whereas CHNS compounds became more important in winter. Full article

The continued expansion of cities in economic, population and geographical terms leads to significant environmental and infrastructural pressures, including the need for efficient municipal solid waste (MSW) management. This research focuses on the characterization of MSW generated in the city of Skopje and the investigation of its thermo-physical properties and energy utilization potential. The analyses cover physical and chemical properties, including density, moisture content, volatile matter, ash, and higher heating value, using adequate testing methods. The results indicate that MSW has a relatively high gross calorific value, surpassing typical MSW ranges comparable to those of solid fossil fuels. With approximately 79.42% volatile matter and a low ash content of 7.76%, the considered MSW demonstrates excellent combustibility. Chemical analysis reveals high carbon (53.12%) and hydrogen (7.69%) levels, supporting high energy value, while low nitrogen (0.84%) and sulfur (0.26%) levels ensure minimal NO_x and SO_x emissions. These characteristics position MSW as a suitable feedstock for energy production in incineration facilities. However, the heterogeneous composition of MSW presents challenges to process stability, necessitating prior waste preparation. The research concludes that harnessing waste energy potential could contribute to sustainability, reduce reliance on fossil fuels, and improve the environmental conditions in large urban areas. Full article

Background: The transition to low-carbon economies has become a global priority, particularly in sectors with high greenhouse gas emissions, such as maritime transport. Renewable fuels, especially methanol, have emerged as promising alternatives to conventional fossil fuels due to their potential to reduce carbon footprints and contribute to sustainable logistics systems. Methods: This study employs a combined qualitative and quantitative approach to assess the impact of renewable fuel production on maritime transport decarbonization. The analysis integrates economic feasibility, energy efficiency, and environmental benefits, providing a comprehensive evaluation of methanol’s role in reducing emissions. Results: Findings indicate that methanol offers significant potential for the decarbonization of maritime transport. Its relatively low production costs and high energy density position it as a viable alternative to traditional fuels. Additionally, the study highlights the substantial reduction in greenhouse gas emissions that methanol adoption could achieve, reinforcing its role in mitigating climate change effects. Conclusions: The study concludes that integrating methanol as a primary fuel in maritime transport can accelerate the sector’s decarbonization. However, successful implementation depends on supportive policy regulations and further research to optimize production and supply chain integration. The findings emphasize the strategic importance of renewable fuels in developing sustainable and resilient logistics systems. Full article

Promoting the development of low-carbon renewable energy is crucial for meeting the growing energy demand, reducing dependence on fossil fuels, and controlling carbon dioxide emissions. Clarifying the spatiotemporal characteristics of regional renewable energy production and its influencing factors will help optimize the spatial layout of renewable energy production and provide a solid theoretical basis for coordinating the development of all aspects of renewable energy production. Using panel data from 22 districts and counties in Ningxia from 2014 to 2021, this study employed the spatial Gini coefficient, Moran’s I index, standard deviational ellipse, and geographical detector to analyze the spatiotemporal evolution patterns and influencing factors of renewable energy production development in Ningxia. The results indicate that renewable energy production in Ningxia exhibits significant spatial agglomeration and autocorrelation. Temporally, renewable energy production shows a spatial expansion trend characterized by dynamic agglomeration patterns. The coupling degree between renewable energy generation and the spatial distribution of power production is relatively high, with notable regional disparities. Urbanization level, urban population, per capita GDP, and industrial SO₂ emissions have a positive impact on renewable energy production, while energy intensity and environmental regulation show insignificant effects. To further promote the development of renewable energy, Ningxia should strengthen power infrastructure construction at the county level, enhance the radiating and driving effects of high-value areas on surrounding cities and counties, optimize the spatial layout of power facilities based on the agglomeration trajectories of renewable energy production, integrate multiple types of renewable energy to improve overall generation efficiency and system stability, and encourage local enterprises to increase technological and economic investments in renewable energy, thereby advancing sustainable energy transition and achieving high-quality development in resource-based regions. Full article

This study investigates the effect of nanoscale zero-valent iron (NZVI) and sediment microbial fuel cells (SMFCs) on the three typical heavy metals’ (Pb, Cr and As) removal and stabilization. Results showed that the combined use of NZVI and SMFCs obtained the highest removal efficiencies in the sediment (Pb 37.7 ± 2.2%, Cr 26.4 ± 1.5% and As 30.1 ± 2.0%) and overlying water (Pb 55.8 ± 2.3%, Cr 47.6 ± 1.9% and As 45.8 ± 2.1%). The use of an NZVI electrode can transform heavy metals with relatively weak binding into forms with stronger binding, thereby diminishing their bioavailability and toxicity. After 60 days of operation with the addition of NZVI in the SMFC system, over 50% of the Pb, Cr and As in the sediment was transformed into the residual fraction. An anodic microbial communities analysis indicated that operating a SMFC can mitigate the adverse effects of NZVI on the community diversity and increase the content of electrogenic bacteria in sediments. Consequently, our findings indicated that integrating SMFCs and NZVI represents a viable approach for remediating rivers contaminated with heavy-metal-polluted sediments. Full article

Horizontal and multilateral well EGSs can potentially achieve the high-heat extraction of HDR. Herein, the performance of a multilateral horizontal well system in geothermal exploitation was investigated based on the geothermal and geological conditions of Qiabuqia geothermal field. The target reservoir is a granitic basement at 2650–3650 m depth, with initial temperatures of 151–190 °C. The simulation results indicate that the initial production temperature and electric power reached 178.4 °C and 2.90 MW and decreased by 8.3% and 17.9% over 20 years of production under the scheme of injecting 60 °C cold water at 50 kg/s, respectively. The reduction in total greenhouse gas (GHG) emissions amounts to 0.15–0.51 Mt relative to a fossil fuel plant. The multilateral horizontal well EGS outperforms conventional double vertical wells in terms of production temperature and electric power generation. The heat production efficiency of the multilateral horizontal well is significantly influenced by well depth and the arrangement of branch wells. Reservoir stimulation can effectively reduce flow impedance and improve the system economy. The multilateral horizontal well system is a promising development scheme for geothermal exploitation in Qiabuqia geothermal field. Full article