Search Results (1,888)

Prescribed burning is important for reducing wildfire risk and regulating fuel loads, but its implementation in mountainous forests is strongly influenced by the coupled effects of the wind field and topography, making it difficult to control. This study focuses on Yunnan pine (Pinus yunnanensis) forests in southwestern China. A three-dimensional Fire Dynamics Simulator (FDS) combined with measured fuel characteristics was used to simulate 21 slope (0–35°) and wind speed (0–2 m s⁻¹) combinations to quantitatively analyze the fire spread, flame structure, and gaseous emission characteristics during downslope prescribed burning. The local fire spread rate (ROS), evaluated along three lateral lines (Y = 2.5, 5.0, and 7.5 m), exhibits a non-monotonic dependence on slope over the tested range, with a minimum near 30° and a modest rebound at 35°. A downslope wind of 1 m s⁻¹ promotes near-surface heating and accelerates spread, whereas a stronger wind of 2 m s⁻¹ lifts flames away from the fuel bed and suppresses combustion. Thermal field analysis reveals that peak temperature decreases with increasing slope and that a late-stage secondary heating episode occurs at 35°. CO₂ emissions are significantly positively correlated with fuel consumption, reaching a peak of 717.5 kg under a 35° slope and no-wind conditions. CO emissions, as an indicator of combustion efficiency, reach their highest value of 2.23 kg at a 35° slope and a wind speed of 1 m s⁻¹, indicating that their trend is not entirely consistent with the ROS and temperature and that there is a certain degree of decoupling. The interaction between slope and wind speed transforms fire behavior from a cooperative to a competitive mechanism, and the topography–wind field coupling provides differentiated control over the combustion intensity and completeness. This study provides a scientific basis for the safe implementation of mountain burning programs and for regional carbon emission assessments. Full article

Brewer’s spent grain (BSG) is one of the major by-products of the brewing industry and an abundant lignocellulosic stream with potential for energy recovery and broader biorefinery use. This review evaluates the main BSG-to-energy pathways, including anaerobic digestion (AD), combustion/co-combustion, pyrolysis, gasification, and hydrothermal processes (HTC/HTL), with emphasis on technical performance, environmental aspects, implementation constraints, and integration into brewery systems. Particular attention is given to the effect of BSG heterogeneity, high moisture content, protein and ash composition, and storage instability on process selection and operability. In addition to summarizing pathway-specific evidence, the manuscript proposes a harmonized comparative framework and an integrated technical–economic–environmental interpretation of BSG valorization options. The analysis shows that wet-feed-compatible pathways, especially AD and hydrothermal processing, are generally better aligned with the intrinsic properties of fresh BSG, whereas thermochemical routes usually require more intensive feedstock conditioning and tighter control of ash-related and gas cleaning risks. The review also highlights that long-term operational reliability, scale-up constraints, and utility integration are as important as nominal conversion efficiency when assessing practical deployment. Current evidence suggests that the most realistic implementation strategies are context-dependent and should be selected according to brewery scale, energy demand profile, available heat integration, and acceptable operational risk. Future research should prioritize harmonized reporting, long-term industrial validation, and the development of robust hybrid systems and brewery-integrated biorefinery configurations. Full article

Carbon dioxide (CO₂) enrichment using fuel combustion is widely applied in greenhouse production. However, its implications for air quality and occupational safety under real operating conditions remain insufficiently characterized. This study evaluates a propane-based CO₂ enrichment system in an advanced greenhouse. The analysis integrates CO₂ dynamics, combustion-derived pollutants, and occupational exposure. High-resolution monitoring at 5 min intervals was conducted in an enriched module and a control module over a five-month period. Two operational modes were assessed: continuous and diurnal-only enrichment. The system maintained CO₂ concentrations within agronomic targets. Mean values reached 1200 ppm and 940 ppm for continuous and diurnal operation, respectively. However, significant CO₂ losses were observed due to ventilation. The maximum enrichment efficiency, expressed as the Combustion Efficiency Index (CEI), was 2.67 × 10⁻³. Combustion-related pollutants (CO, NO, NO₂, SO₂, and O₃) showed transient peaks during burner activation. However, concentrations remained below occupational exposure limits when evaluated using time-weighted averages. The incomplete combustion ratio (ICR) remained stable at approximately 1.9 × 10⁻³. This indicates predominantly complete combustion. These results provide field-based evidence on the performance and safety of propane-based CO₂ enrichment systems. They also highlight the importance of continuous monitoring and improved CO₂ retention strategies in semi-confined greenhouse environments. Full article

Aviation heavy-fuel piston engines are widely used in UAVs, general aviation, and military platforms due to their fuel efficiency and adaptability. However, emissions of NO_x, PM, and other pollutants pose significant environmental challenges. This paper reviews emission-reduction strategies, including combustion-chamber optimization, fuel-injection control, alternative fuels, and exhaust after-treatment technologies. Research indicates that optimizing combustion-chamber geometry, high-pressure common-rail injection, and turbulence enhancement improve combustion efficiency and reduce emissions. Biofuels, synthetic aviation fuels (SAF), and hydrogen-based fuels demonstrate strong potential for low-carbon emissions, while after-treatment technologies such as SCR, DPF, and EGR effectively mitigate NO_x and PM emissions. Despite technological advancements, challenges remain in balancing combustion efficiency with NO_x control and ensuring compatibility between EGR and combustion stability. Future advancements in intelligent combustion control, novel catalytic materials, low-temperature combustion, and high-efficiency after-treatment systems will drive aviation diesel engines toward lower emissions, higher efficiency, and greater intelligence, contributing to the green and sustainable transformation of aviation propulsion systems. Full article

Hybridization in vehicle powertrains extends beyond the aggregate system level and can target individual components to enhance engine performance. While prior studies have highlighted the performance benefits of electrified turbochargers, this work focuses on mitigating engine-out emissions for a medium- to heavy-duty diesel engine with an electrified airpath. Unlike conventional engines and actuators, the alternative engine architecture with an electrified airpath provided superior airpath control. This is critical for fuel-led diesel engines, where the initial combustion cycles during the tip-in phase of a transient operate at a rich equivalence ratio. In this work, a 3.2 L two-cylinder opposed piston two-stroke (OP2S) engine equipped with an Electrically Assisted Turbocharger (EAT) and an electrically operated EGR pump was experimentally tested in a Hardware in the Loop (HIL) setup under transient conditions. Actuator positions were varied to identify strategies that mitigate soot and NO_x without compromising transient response. The experiments are discussed case-wise, where the effects of each airpath actuator, including fuel rate shaping, are analyzed, showing to what extent each strategy mitigates emissions. At the end, an optimized case is presented to the readers for their perusal. The electrified airpath, along with fuel rate shaping, demonstrated cumulative soot reduction up to 92% and NO_x emissions by 77% for a transient load step between 3 and 13 bar BMEP at a mid-engine speed of 1250 rpm. Full article

This study presents a combined computational fluid dynamics (CFD) and experimental evaluation of an adjustable direct-injection reciprocating (IDR) metallurgical furnace fueled by a multicomponent residual oil–solvent mixture. An axisymmetric CFD model, incorporating k–ω SST turbulence modeling, Eddy Dissipation Concept (EDC) combustion, and Discrete Ordinates radiation, was validated against infrared thermography and Process Analytical Technology (PAT) measurements obtained under actual operational conditions. The residual mixture operated in a turbulence-controlled regime (Da < 1), reaching maximum internal temperatures of 1199 °C and achieving a thermal efficiency of 84.6% (based on LHV). Numerical predictions agreed with thermographic data to within 5% across the stabilized operational window. Under comparable process parameters, the alternative fuel reduced cycle time and operational costs compared with diesel and natural gas whilst maintaining stable combustion. Methodological clarifications encompass a consolidated, dimensionally consistent set of equations, a QoI-based mesh-independence study, and a concise summary of the experimental configuration to enhance reproducibility. Full article

Post-combustion amine scrubbing using monoethanolamine (MEA) remains a leading carbon capture technology, yet its deployment is constrained by high regeneration energy requirements and the computational expense of rigorous process simulation. This study presents an integrated framework coupling high-fidelity rate-based process simulation with explainable machine learning to systematically characterize a ten-dimensional operating space for MEA-based CO₂ absorption. Latin hypercube sampling generated 10,000 steady-state cases, and five regression architectures were benchmarked under identical protocols. A neural network achieved the highest accuracy (R² = 0.9729, RMSE = 1.43%), while XGBoost was selected as the operational surrogate due to its robust computational efficiency (1.5 ms inference latency) and native compatibility with exact Shapley value decomposition. SHAP analysis identified liquid-to-gas ratio as the dominant efficiency determinant, contributing 46.6% of total predictive importance, followed by inlet temperature and MEA concentration, with these three parameters collectively explaining 85% of efficiency variation and establishing a compact control hierarchy suitable for reduced-order control architectures. Bivariate interaction analysis located a high-efficiency operating region, while sensitivity analysis confirmed the strong influence of inlet temperature across the operating envelope. Pareto optimization via NSGA-II generated tiered operational guidelines spanning the 85% to 98% capture efficiency range, quantifying a 39% specific regeneration duty penalty (3.1 to 4.3 MJ/kg CO₂) for pursuing maximum versus baseline capture targets. The framework demonstrates how explainable machine learning converts opaque process simulations into actionable engineering knowledge, providing a transparent and computationally efficient basis for design optimization and digital twin deployment in post-combustion carbon capture systems. Full article

Emission control of diesel particulate matter (soot) combustion is important for environmental reasons. Catalysts are indispensable for optimizing these processes, as they significantly reduce the combustion temperature. In this work, mixed oxides (cerium–copper, cerium–manganese, and cerium–molybdenum) were prepared by co-precipitation under reasonably similar synthesis conditions, and the effects of their chemical composition on diesel soot combustion were evaluated using the Printex U model particulate. Thermogravimetric analysis (TG/DTG) and temperature-programmed oxidation coupled with mass spectrometry (TPO/MS) were employed for activity characterization. Structural analyses revealed the presence of nanocrystalline phases containing CeO₂ (fluorite), CuO (monoclinic), Mn₂O₃ (cubic), and MoO₃ (orthorhombic), depending on the catalyst composition. The most effective catalysts exhibited an equimolar oxide composition (CeO₂–MO_x). Tests performed at optimized calcination temperatures and with the addition of promoters led to the identification of optimal combustion conditions. The highest activity, corresponding to the lowest combustion temperature, was observed in the following order: CeO₂–Mn₂O₃ > CeO₂–CuO > CeO₂–MoO₃, with values of 382, 409, and 425 °C, respectively, under tight-contact conditions at a Printex U:catalyst ratio of 1:20. With the addition of a 10% Ag₂O promoter, the CeO₂–Mn₂O₃ catalyst further reduced the oxidation temperature to 376 °C. Reusability tests generally indicated a 10–20% decrease in catalytic activity by the third reaction cycle. Full article

The development of modern transport energy systems is driven by increasing demands for energy efficiency, environmental sustainability, and operational reliability of vehicles. One of the most critical challenges in internal combustion engine operation is the cold-start condition, which results in increased fuel consumption, intensified component wear, and elevated emissions. Under these conditions, the development of intelligent thermal conditioning systems capable of accelerating engine warm-up and maintaining optimal thermal regimes becomes essential. This study proposes an intelligent engine and vehicle thermal conditioning system based on the integration of digital twin technology and phase-change thermal (PCM) energy storage. A digital twin architecture of the engine thermal conditioning system is developed to enable the integration of monitoring, simulation and predictive control of engine thermal processes. A mathematical model of the thermal conditioning system describing the dynamic temperature behavior of the engine, coolant, engine oil and PCM-based thermal energy storage units is formulated. A model predictive control strategy is implemented within the digital twin environment to support decision-making and optimization of engine thermal conditioning processes. Simulation and experimental results demonstrate that the proposed system can reduce engine warm-up time by 17.8–68.4%, decrease fuel consumption during the cold start phase by approximately 19.5–56.25%, and reduce harmful emissions. These findings confirm the potential of integrating digital twin technologies, predictive control and phase change thermal energy storage for improving the energy efficiency and environmental performance of modern transport power systems. Full article

In gasification-based waste treatment plants, continuous generation of combustible gas is essential for stable and efficient operation. To achieve this, multiple gasification furnaces are operated alternately; however, the internal states of the furnaces cannot be directly observed, making it difficult to assess the progress of gasification. Consequently, operation planning relies heavily on the experience of skilled operators. In this study, nonlinear system identification models based on deep learning are developed to predict the valve opening that controls the injection of gasification agents, which implicitly reflects the gasification state. Several modeling approaches, including linear finite impulse response (FIR) models, block-oriented Hammerstein–Wiener (HW) models, deep Hammerstein–Wiener models, and Transformer-based models, are investigated and compared. The models are trained and validated using actual operational data obtained from an industrial waste treatment plant. The results demonstrate that nonlinear models significantly outperform linear models, particularly for long-term prediction horizons. Among the examined approaches, the Transformer-based model shows stable and competitive performance across different prediction intervals. These findings indicate that deep learning-based nonlinear modeling is effective for predicting plant operation and has the potential to support automated operation planning, thereby reducing reliance on operator expertise. Full article

Regular inspection of ignition systems in internal combustion engine (ICE) vehicles is essential as these checks influence both engine performance and emission levels. While emission testing is mandatory for road vehicles, many industrial combustion devices remain outside routine emission control. During standard service procedures such as oil changes, the ignition system can be evaluated using electronic diagnostic tools, which are commonly available in licensed service stations. These measurements provide valuable insight into the spark plug condition—a critical factor affecting ignition quality and emission formation. This article presents the design of a diagnostic system based on an oscilloscope equipped with voltage and current probes. Experimental data were obtained directly from test vehicles and include waveform records of electrical quantities, revealing clearly distinguishable differences in component behavior. The proposed system enables rapid and accurate spark plug condition assessment under various operating states. Results confirm that the selected diagnostic approach can identify characteristic variations in ignition components, thereby improving fault detection accuracy. This study introduces an innovative, non-intrusive diagnostic method applicable to the development of modern automotive tools. Overall, this work contributes to enhancing the reliability, efficiency, and emission performance of internal combustion engines. Full article

This study aims to investigate the combustion characteristics, thermal distribution, and nitrogen oxide (NO_x) formation of two radiant tube designs—conventional and staged combustion—under air–fuel ratios of 1:10 and 1:11. A three-dimensional numerical model was developed in ANSYS Fluent 2023 R1 to compare flame temperature, wall temperature gradients, and pollutant emissions. The results reveal that flame temperature is the dominant factor in NO_x formation. The conventional tube, with flame temperatures around 1800 °C, shows decreasing NO_x emissions as the air–fuel ratio increases (corresponding to lower flame temperatures). In contrast, the staged combustion tube exhibits flame temperatures exceeding 1900 °C, where the thermal mechanism dominates, leading to a sharp increase in NO_x emissions far above the conventional design. These findings highlight that in staged combustion systems, inadequate consideration of flame temperature and mixing characteristics may cause NO_x control to fail or even reverse. Full article

The decarbonization of marine transport requires the wider use of alternative low-carbon fuels that can be applied in existing compression ignition (CI) engines without major modifications. Hydrotreated vegetable oil (HVO) is considered a promising renewable drop-in fuel due to its favorable physicochemical properties and high cetane number. This study investigates the influence of neat HVO and its blends with conventional diesel fuel on the combustion characteristics, energy, and emission indicators of a CI engine operating under different load conditions and exhaust gas recirculation (EGR) ratios. Experimental tests were carried out on a four-cylinder CI engine at constant speed and variable load using diesel fuel (D100), HVO100, and their blends (D80_HVO20 and D50_HVO50). In-cylinder pressure measurements and combustion analysis were performed using AVL instrumentation and AVL BOOST software. The results show that increasing the HVO fraction slightly advances combustion phasing and increases maximum in-cylinder pressure by approximately 4–5%. The use of HVO was found to reduce brake-specific fuel consumption by up to 3.4% and increase brake thermal efficiency by about 1.9%, although volumetric fuel consumption increases due to the lower fuel density. In addition, higher HVO content significantly reduces smoke opacity by up to 42% and decreases CO₂ emissions by 4.7–6.3%, while the influence on NO_x emissions depends on the applied EGR strategy. The results indicate that HVO and its blends can be effectively applied in CI engines; however, optimal performance and emission characteristics require appropriate calibration of EGR rate and fuel injection timing. Full article

Understanding the accumulation, ecological risk, and source interactions of potentially toxic elements (PTEs) in reservoir sediments is essential for protecting drinking water safety, yet such processes remain insufficiently understood in karst tea-plantation watersheds influenced by mixed anthropogenic activities. In this study, sediment cores collected from four sites (CY-1 to CY-4) during 2022–2024 were analyzed, and an integrated framework combining the Potential Ecological Risk Index (RI), Spearman correlation analysis, Principal Component Analysis (PCA), and Positive Matrix Factorization (PMF) was applied to evaluate contamination characteristics and quantify source contributions. The results revealed significant spatial–vertical heterogeneity of PTEs, with Zn (up to 153 mg/kg) and Cr (up to 64.6 mg/kg) showing the greatest variability, and strong co-enrichment among Cu, Zn, Pb, and Ni (r > 0.85, p < 0.01). Although the overall ecological risk was low (RI = 83.15–106.69), As contributed the highest proportion of risk (28–35%). PCA indicated distinct grouping patterns among elements, while PMF resolved three major sources: domestic sewage and agricultural runoff, agricultural and coal-combustion inputs, and industrial–traffic emissions. Notably, physicochemical parameters (TP, TN, and COD) played important roles in regulating the mobility and partitioning of PTEs by influencing nutrient-associated adsorption processes, organic matter complexation, and redox-related transformations. These findings highlight the multi-source-driven accumulation mechanisms of PTEs in karst reservoirs and provide a scientific basis for targeted pollution control and watershed management in agriculturally impacted regions. Full article

This study examines operating-point alignment during full-scale sea trials of a controllable pitch propeller (CPP)-equipped vessel by reconstructing engine load from measured shaft power and relating it to engine performance, fuel-consumption behavior, and combustion indicators. Engine-side performance and fuel-oil consumption records were integrated with shaft measurement data for a MAN 5S35ME-B9.5 low-speed two-stroke marine diesel engine to establish a common propulsion-based operating-point framework. The average shaft power at the 100% speed-trial point was 3471.1 kW, differing from the rated power by only −0.11%, and was adopted as the reference for shaft-load reconstruction. The reconstructed speed-trial operating points were aligned at 24.91%, 49.04%, 80.85%, and 100.00%, while the endurance points corresponded to 76.99% at NCR and 95.29% at MCR. Relative to the corresponding speed-trial references, the endurance points showed about 4.7% lower delivered shaft power, indicating that they should not be interpreted as identical to nominal speed-trial load labels. Fuel flow and combustion-related indicators showed physically consistent variation with increasing reconstructed load. These results demonstrate that measured shaft power provides a practical basis for harmonizing sea-trial datasets and for distinguishing propulsion-side operating conditions more consistently than nominal load labels alone. The proposed framework is particularly applicable to representative operating-point alignment in full-scale sea trials of CPP-equipped low-speed two-stroke marine diesel engines under comparable test conditions. Full article