Gas Model Research Articles

The Gas Generation Process and Modeling of the Source Rock from the Yacheng Formation in the Yanan Depression, South China Sea

The research on deepwater oil and gas exploration areas is relatively limited, and sample collection is difficult. A drilled coal sample from Yanan Depression was used to investigate the hydrocarbon generation process, and the potential, by a gold tube thermal simulation experiment. The results show that the total gas yield was much higher than the oil yield. According to an analysis of the gas pyrolysis data, as represented by ln(C1/C2) and ln(C2/C3), the gas generation process consisted of two forms, namely, primary gas with ~1.33% Ro and secondary gas that occurred at levels greater than 1.33% Ro. The primary gas from kerogen was generated at ~1.33% Ro, which coincided with the %Ro value of the maximum oil yield. The activation energy distribution of the C1–C5 generation processes ranged from 54 to 72 kcal/mol, with a frequency factor of 6.686 × 1014 s−1 for the coal sample. We constructed the history of gas generation on the basis of the process and kinetic parameters, combined with data on the sedimentary burial and thermal history. The extrapolation of the gas history revealed that the gas has been generated from 5 Ma to the present, with a maximum yield of 178.5 mg/gTOC. This history suggests that the coal has good primary gas generation potential and provides favorable gas source conditions for the formation of gas fields. This study provides a favorable basis for expanding the effective source rock areas.

An entropy measure-based study on flow pattern of gas–liquid two-phase flow in a U-Tube

U-tubes are widely applied in gas–liquid two-phase transportation in chemical engineering. The diverse flow patterns within these tubes significantly affect the pressure loss, heat transfer efficiency, and even the fluid-induced vibration amplitude of the tubes. This study explores the complex flow pattern features in a U-tube in a vertical plane and focuses on recognizing them. For the acquisition and classification of flow patterns, a Computational Fluid Dynamics (CFD) model for gas–liquid two-phase flow is first established, and its quantitative calculation error is ensured to be less than 5%. Then, the spatiotemporal evolution characteristics of flow patterns is analyzed. The real-time pressure drop response is chosen as the representation signal, and its nonlinear features in the time and frequency domain under different flow patterns are explored. A nonlinear time series is constructed by extracting a segment from the real-time pressure drop data, and six entropy measures are applied to analyze and identify them. Finally, the sensitivity of entropy measures to both the time series lengths and the tested sections are evaluated. Results show that there are six typical flow patterns in a U-tube. According to most entropy measures, the bubble flow has the highest complexity; however, the plug flow presents the lowest complexity. In the U-bend, pressure drop signals for the bubble and annular flows show random fluctuations within a specific range, in contrast to the marked periodicity in plug flow signals, while wavy and slug flows exhibit intermittent peak values. Including the upstream and downstream straight pipes in the analysis, rather than focusing solely on the U-bend, significantly increases the complexity of the stratified, plug, and slug flows. Fuzzy entropy is an effective tool for identifying the six flow patterns, demonstrating good resilience to variations in the length of the data series. This characteristic makes it highly useful for real-time identification of flow patterns in the U-bend sections of non-transparent U-tubes, offering considerable potential in chemical equipment.

Analysis of syngas and power production from tannery waste via gasification process using integrated thermal equilibrium simulation model

Managing high-ash sludge from tanneries is a significant challenge and requires investigating their conversion to valuable goods. This study explores the use of gasification processes to convert tannery waste into syngas and the use of syngas for power generation. In this respect, the integrated process thermal equilibrium simulation model has been developed using the Aspen Plus® V12. This model consists of (1) a steam gasification model for syngas production and (2) a power generation model for converting syngas into electricity. In addition, sensitivity analysis has been carried out to determine the effects of temperature (650–900 °C), steam flow (500–2000 kg/h), and CaO flow (0.1500 kg/h) on the composition and power generation of syngas. Changes in steam flow rate at constant temperature (cao flow rate 1500 kg/h) show an increase in H2 content to 80 % and a decline in CO and CH4 content. This increases total power output from 3680 kW to 4001 kW, temperature increases from 650 to 900 °C, and steam flow increases from 500 to 2000 kg/h from 3500 to 4600 kW. Finally, the impact of CaO as a sorbent is significant in electricity generation and CO2 mitigation, increasing to over 600 kW of energy output. This work could contribute to converting waste into energy, which could have a significant financial impact on the tannery industry.

Numerical investigation of spindle position effects on steam ejector performance with a nonequilibrium condensation model

AbstractThis research studied the performance of a spindle‐controlled steam ejector using models such as ideal gas and wet steam under various operating conditions based on computational fluid dynamics (CFD). A wet steam model incorporating nonequilibrium condensation was employed to simulate the complex flow phenomena within the ejector. The structure of the flow, entrainment ratio (Er), and shock wave characteristics of the steam ejector were examined in two different models. Results indicate that the spindle position has a substantial impact on steam ejector performance. For the ideal gas and wet steam models, the optimal spindle position (SP‐5) at a .1 MPa motive pressure achieves the highest entrainment ratios (Er) of 1.01 and 1.042, respectively. However, an ejector with a fixed geometry achieves Er values of only .517 and .549 for the ideal gas and wet steam models, under identical working conditions. This represents a substantial improvement of 89.8% over the fixed‐geometry ejector. The wet steam model consistently predicts 2%–4% higher Er values compared with the ideal gas model across all spindle positions. The study also reveals that increasing the motive pressure from .1 to .3 MPa reduces Er by up to 45.8% at the optimal spindle position, with the shock train length extending to 35% of the mixing chamber at .3 MPa. These findings offer insights for improving the design and optimization of variable‐geometry steam ejectors, potentially increasing efficiency in industrial applications.

Eigenstate plateau transition and equilibration in one-dimensional quantum lattice models

We report a remarkable spectral phenomenon in a generic quantum lattice gas model. As the interaction strength increases, eigenstates spontaneously reorganize, leading to plateaus in the interaction energy. Gaps between plateaus can emerge similarly to continuous phase transitions. Our perturbation analysis indicates that, aside from these gaps, the plateau structure of the spectrum remains stable in the thermodynamic limit. The structured spectrum naturally manifests in far-from-equilibrium dynamics, exhibiting multiple stages. Our findings reveal a connection between emergent properties in high-energy states and equilibration dynamics in closed quantum systems, offering insights into the impact of interactions across the entire energy spectrum. These results directly relate to experiments probing equilibration in quantum spin systems and lattice gases. Published by the American Physical Society 2024

Sustainable Solutions for Co-Gasification of Sewage Sludge and Biomass: Insights from Aspen Plus, Response Surface Methodology, and Multi-Criteria Decision-Making Integration

This paper presents a novel framework integrating process simulation, optimization, and multi-criteria decision-making (MCDM) to identify sustainable co-gasification solutions for sewage sludge (SS) and biomass. Gasifier modeling in Aspen Plus, validated with experimental data, evaluated five feedstock blend routes. Sensitivity analysis was conducted on key gasification parameters, while Response Surface Methodology (RSM) optimized process routes by revealing complex relationships between variables and performance metrics. MCDM was applied to assess alternatives based on eight criteria covering environmental, economic, and social sustainability. The study introduced an integrated objective weighting method, leveraging data variations and correlations, alongside a vector-based ranking approach for prioritizing solutions. Results indicated that a biomass-to-sewage sludge ratio of 3:7, under co-gasification conditions of 649.72°C, 1.13atm, and a steam to feedstock ratio of 0.5, achieved the highest sustainability score of 0.506. This configuration outperformed other alternatives in terms of sustainability. Validation through three distinct analytic methods confirmed the robustness of the MCDM framework. In general, this work integrates simulation, RSM optimization, and MCDM analysis into a streamlined tool, offering a systematic progression from process simulation to decision-making. It reduces the reliance on costly trial-and-error experimentation while ensuring optimal operating conditions and comprehensive sustainability evaluation. These contributions tackle key challenges in data handling, optimization, and assessment, enabling more sustainable and cost-effective environmental technology innovations.

Artificial Intelligence based Leak Detection in Blended Hydrogen and Natural Gas Pipelines

In the transition towards a hydrogen-based energy system, the strategic use of pipelines is crucial for efficient hydrogen distribution. Leveraging existing natural gas pipelines to carry a mix of hydrogen and natural gas offers a cost-effective alternative to building new infrastructure. This study explores the development of a leak detection system compatible with existing pipelines, specifically tailored for the blended hydrogen and natural gas mix. Given the scarcity of leak data for blended hydrogen-natural gas pipelines, the study introduces a Real-Time Transient Model (RTTM) for blended gases, simulating leak dynamics to generate necessary data. Additionally, a leak detection system (LDS) is developed using a fusion of Convolutional Neural Network (CNN) and Explainable Artificial Intelligence (XAI) through Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This LDS framework overcomes the “black box” issue common in AI-driven systems, enabling reliable detection. The integration of Explainable and traditional AI techniques holds promising implications for blended hydrogen pipelines by enhancing the safety and efficiency of hydrogen transportation, thereby mitigating economic and environmental impacts, and addressing public concerns.

Effect of the ash melting on heat and mass characteristics and reaction rate during corn stalk pellet gasification

In order to investigate the effect of ash melting on the gasification process of biomass pellet, a high-temperature visual gasification system was set up, meanwhile, a biomass melting gasification model for a single pellet was built. Considering that the fusion temperature range of ash is from 1418 K to 1558 K, 1396 K (below 1418 K), 1491 K (between 1418 K and 1558 K), 1591 K and 1677 K (above 1558 K) were chosen as the experimental temperatures. Results show that ash melting could absorb heat, thereby resisting the heat transfer inside pellet. When the gasification temperature is higher than 1558 K, as the larger spherical particles caused by ash melting and coalescing peels off from the pellet surface, the pellet covered by melting ash could be directly exposed to the environment, which is conducive to the heat and mass transfer.

Assessment on the rings cleavage mechanism of polycyclic aromatic hydrocarbons in supercritical water: A ReaxFF molecular dynamics study

The ring opening reaction of polycyclic aromatic hydrocarbons (PAHs) is one of the rate-determining steps of coal gasification within supercritical water, while the involvement of water remains debated between addition and hydrogen abstraction theories. In this study, reactive molecular dynamics (MD) simulations were performed to explore the ring opening reaction of naphthalene, the simplest PAH, at temperatures of 2500–2700 K. Species and elementary reactions were quantitatively extracted from bond order trajectories using an in-house program. Reaction path analysis shows that ring opening paths of naphthalene mainly include thermolysis, H˙ atom abstraction, and O˙H radical addition. Flux analysis indicates O˙H radical largely from the H˙ + ▪ = O˙H + ▪ reaction, while the water dissociation ▪ = O˙H + H˙ reaction is near equilibrium, seldom contributing to the O˙H source. This atomistic kinetics analysis is intended to help the modeling of supercritical water gasification of coal.

FLEXPART version 11: improved accuracy, efficiency, and flexibility

Abstract. Numerical methods and simulation codes are essential for the advancement of our understanding of complex atmospheric processes. As technology and computer hardware continue to evolve, the development of sophisticated code is vital for accurate and efficient simulations. In this paper, we present the recent advancements made in the FLEXible PARTicle dispersion model (FLEXPART), a Lagrangian particle dispersion model, which has been used in a wide range of atmospheric transport studies over the past 3 decades, extending from tracing radionuclides from the Fukushima nuclear disaster, to inverse modelling of greenhouse gases, and to the study of atmospheric moisture cycles. This version of FLEXPART includes notable improvements in accuracy and computational efficiency. (1) By leveraging the native vertical coordinates of European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) instead of interpolating to terrain-following coordinates, we achieved an improvement in trajectory accuracy, leading to a ∼8 %–10 % reduction in conservation errors for quasi-conservative quantities like potential vorticity. (2) The shape of aerosol particles is now accounted for in the gravitational settling and dry-deposition calculation, increasing the simulation accuracy for non-spherical aerosol particles such as microplastic fibres. (3) Wet deposition has been improved by the introduction of a new below-cloud scheme, by a new cloud identification scheme, and by improving the interpolation of precipitation. (4) Functionality from a separate version of FLEXPART, the FLEXPART CTM (chemical transport model), is implemented, which includes linear chemical reactions. Additionally, the incorporation of Open Multi-Processing parallelisation makes the model better suited for handling large input data. Furthermore, we introduced novel methods for the input and output of particle properties and distributions. Users now have the option to run FLEXPART with more flexible particle input data, providing greater adaptability for specific research scenarios (e.g. effective backward simulations corresponding to satellite retrievals). Finally, a new user manual (https://flexpart.img.univie.ac.at/docs/, last access: 11 September 2024) and restructuring of the source code into modules will serve as a basis for further development.

Analysis of HEM applicability for a subcritical flashing ejector at low motive pressure

Validated CO2 ejector models are essential for developing high-performance refrigeration and heat pump cycles. This study focuses on assessing the Homogeneous Equilibrium Model’s applicability to simulate a CO2 flashing ejector at a reduced pressure of 0.47. The model was implemented in FLUENT, integrating a user-defined real gas model. Simulation results with different boundary condition options were compared to experimental data. The analysis was carried out to evaluate the predictive capabilities of the model and assess the experimental data quality. The results indicate that the developed model accurately estimated the motive mass flow rate, with a maximum relative error of 5.7%, showing better performance than previously reported data. The entrained flow rate, assuming double choking operation, was significantly higher than the experimental measurement, and the CFD-predicted wall static pressure underestimated the experimental profile, suggesting single-choked ejector operation. In contrast, the outflow density was better predicted under the same assumption, with an average error of 8.6%. Nevertheless, the simulated temperature profiles showed good agreement with the experimental data, especially when using the experimental entrained mass flow rate as a boundary condition.

Simba-EoR: early galaxy formation in the simba simulation including a new sub-grid interstellar medium model

ABSTRACT We update the dust model present within the simba galaxy simulations with a self-consistent framework for the co-evolution of dust and molecular hydrogen populations in the interstellar medium, and use this to explore $z \ge 6$ galaxy evolution. In addition to tracking the evolution of dust and molecular hydrogen abundances, our model fully integrates these species into the simba simulation, explicitly modelling their impact on physical processes such as star formation and cooling through the inclusion of a novel two-phase sub-grid model for interstellar gas. Running two cosmological simulations down to $z \sim 6$ we find that our simba-EoR model displays a generally tighter concordance with observational data than fiducial simba. Additionally we observe that our simba-EoR models increase star formation activity at early epochs, producing larger dust-to-gas ratios consequently. Finally, we discover a significant population of hot dust at $\sim 100$ K, aligning with contemporaneous observations of high-redshift dusty galaxies, alongside the large $\sim 20$ K population typically identified.

Heavy quark mass near the phase transition

Abstract Assuming that the number densities of heavy flavor in hadron gas and in QGP are same at $T_c$, we obtain the effective mass of heavy quark at $T_c$ from the comparison with the hadron resonance gas model which well describes particle yield in heavy-ion collisions. We find that charm quark mass at vanishing baryon chemical potential is around 1.8 GeV which is much heavier than QCD bare mass and close to $D$ meson mass. The mass slightly increases with increasing baryon chemical potential and then decreases.
On the other hand, anticharm quark mass constantly decreases with increasing baryon chemical potential. Bottom quark mass has a similar pattern. Extending the hadron resonance gas model to a bit higher temperature beyond $T_c$, the effective masses of charm and bottom quarks decrease with increasing temperature.

Numerical Investigation of Two-Phase Shock Waves in CO2 Flows Using a Modified Hertz–Knudsen Model

Abstract Understanding the complex behavior of two-phase shocks in CO2 flows is essential for a variety of applications, including carbon capture and storage () and transcritical refrigeration cycles. This study presents a comprehensive numerical investigation of two-phase shock waves using the multispecies user-defined real gas model in Ansys Fluent. The simulations are performed for de-Laval nozzles, exploring the two-phase shock features for three-dimensional (3D), two-dimensional (2D), and two-dimensional axisymmetric geometries. The nonequilibrium condensation, subsequent evaporation, and denucleation occurring across the shock are modeled through a set of user-defined scalar transport equations implemented within Ansys Fluent. The two-phase computational fluid dynamics (CFD) simulations are carried out in proximity to the critical point where real gas effects are relevant. The CO2 real gas properties are computed using an in-house Python code and integrated into the solver via user-defined functions as external look-up tables. This study provides valuable insights into the physical processes underlying two-phase shocks in CO2 flows and their sensitivity to geometric variations and thermodynamic conditions. The findings contribute to the development and modification of predictive models and optimized designs for systems involving two-phase CO2 flows. The results highlight the influence of geometry configurations and thermodynamic conditions on shock location and intensity, providing comparisons for shock waves occurring in two-phase flows and supercritical single-phase flows.

Study on Atomization Mechanism of Oil Injection Lubrication for Rolling Bearing Based on Stratified Method

The atomization mechanism of lubrication fluid in rolling bearings under high-speed airflow between the rings was investigated. A simulation model of gas–liquid two-phase flow in angular contact ball bearings was developed, and the jet lubrication process between the bearing rings was simulated using FLUENT computational fluid dynamics software (Ansys 19.2). The complex motion boundary conditions of the rolling elements were addressed through a layered approach. We can obtain more accurate boundary layer flow field changes and statistics of the diameter of oil particles in lubricating oil atomization, which lays the foundation for analyzing the law of influence on lubricating oil atomization. The results show that as the number of boundary layer layers increases, the influence of the boundary layer flow field on the lubricating oil is more obvious. The oil particle size is excessively flat, and the concentration of large particles of oil appears to decrease. As the speed increases, the amount of oil in the cavity decreases, but the oil droplets are also fragmented, which intensifies the atomization and reduces the particle diameter. This reduces the Sauter Mean Diameter (SMD), which is not conducive to the lubrication of the bearing. Under different injection pressures, when the injection pressure is large, it is beneficial to the lubrication of the bearing.

Study of net-baryon higher moments in PNJL model and their expectation for net-proton using the subensemble acceptance method for the search of QCD critical point

The critical point is one of the most important aspects of the QCD phase diagram of strongly interacting matter. The nonmonotonic behavior of the higher-order moments of conserved quantities like net-baryon (ΔB), net-charge (ΔQ), and net-strangeness (ΔS) are believed to be the signatures of the QCD critical point as a function of energy. We study the effect of the QCD critical point on moments of net-baryon in the Polyakov loop enhanced Nambu-Jona-Lasinio (PNJL) model of QCD with six-quark and eight-quark interactions for finite and infinite volume systems. The study is performed at energies similar to Relativistic Heavy-Ion Collider beam energy scan. Experimentally measuring conserved quantities is difficult due to systematic limitations. Therefore, net-proton, net-pion, and net-kaon are measured as the proxy of ΔB, ΔQ, and ΔS. Recent studies in the subensemble acceptance method on the hadron resonance gas (HRG) model show the dependency of the measured higher-order moment on the experimental acceptance. We applied the subensemble acceptance method to analyze the behavior of κσ2 of net-baryon distribution within the subvolume system for various acceptance fractions. These results can be directly mapped to the subvolume (particle) percentage of the total volume (conserved quantities). Our findings are compared to the solenoidal tracker at RHIC (STAR) net-proton and proton data with different energies to investigate the presence of the critical point. Additionally, these results are also compared with the ultrarelativistic quantum molecular dynamics (UrQMD), HRG model, and available lattice data. Published by the American Physical Society 2024

Monte Carlo calculations taking into account the DLP theory to study multilayer adsorption of C2H4 and CF4 on graphite

Physisorption potentials are useful for deducing the properties of adsorption systems. The adsorption behavior is a consequence of the actual potential that describes the interactions among the adsorption system. Determining the actual isotherm is a test for the correctness of a calculated adsorption potential. The physisorption interactions decrease with the film thickness, d, according to a power law as −γ/d3, where γ is the Van der Waals coefficient. In the Dzyaloshinskii, Lifshitz, and Pitaevskii (DLP) theory, γ is a function of d where γ(d) decreases with d. In the present work, the general theory of Van der Waals forces, given by DLP, is used to derive a more exact treatment of interactions among the physisorbed systems. The Monte Carlo method is used to investigate multilayer films using a lattice gas model adsorption isotherm. Critical temperatures are determined for ethylene (C2H4) and tetrafluoromethane (CF4) on graphite. These systems exhibit comparable behavior. Unlike the findings of mean field theory, our data shows that the critical temperature rises gradually as the layer number increases. On the other hand, the comparison with experimental results and measurements has shown that our calculations are more reliable and provide a better approximation in predicting film thickness. Our isotherms appear to be in better agreement with the experimental ones.

Nonlinear Dynamics for the Ising Model

We introduce and analyze a natural class of nonlinear dynamics for spin systems such as the Ising model. This class of dynamics is based on the framework of mass action kinetics, which models the evolution of systems of entities under pairwise interactions, and captures a number of important nonlinear models from various fields, including chemical reaction networks, Boltzmann’s model of an ideal gas, recombination in population genetics and genetic algorithms. In the context of spin systems, it is a natural generalization of linear dynamics based on Markov chains, such as Glauber dynamics and block dynamics, which are by now well understood. However, the inherent nonlinearity makes the dynamics much harder to analyze, and rigorous quantitative results so far are limited to processes which converge to essentially trivial stationary distributions that are product measures. In this paper we provide the first quantitative convergence analysis for natural nonlinear dynamics in a combinatorial setting where the stationary distribution contains non-trivial correlations, namely spin systems at high temperatures. We prove that nonlinear versions of both the Glauber dynamics and the block dynamics converge to the Gibbs distribution of the Ising model (with given external fields) in times O(nlogn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n\\log n)$$\\end{document} and O(logn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\log n)$$\\end{document} respectively, where n is the size of the underlying graph (number of spins). Given the lack of general analytical methods for such nonlinear systems, our analysis is unconventional, and combines tools such as information percolation (due in the linear setting to Lubetzky and Sly), a novel coupling of the Ising model with Erdős-Rényi random graphs, and non-traditional branching processes augmented by a “fragmentation” process. Our results extend immediately to any spin system with a finite number of spins and bounded interactions.

Transpiration and plant hydraulics of Abies veitchii under fluctuating environmental conditions in cool montane forest

AbstractIn subalpine fir wave forests, strips of dead and weakened trees occur perpendicular to the slope next to strips of healthy trees. To reveal the transpiration by weakened Abies veitchii trees exposed to increased atmospheric evaporative demand, we investigated the ecophysiological traits closely related to the growth and transpiration, comparing them with those of the healthy trees and saplings in the fir wave of Mt. Shimagare in central Japan. The transpiration rate (E) was investigated using sap flux sensors to measure heat pulse velocity and compared with the surrogate for the needle water demand, which was computed using a multilayered gas and energy transfer model (modeled E, Emod). Weakened trees exhibited smaller diameter growth and narrower sapwood than healthy trees, as well as lower heat pulse velocity compared with healthy saplings. However, needle‐level traits did not differ significantly between weakened and healthy trees. Needle water potential at midday was as negative as the needle turgor loss point, and the measured heat pulse velocity increased linearly with Emod but leveled off above a certain Emod value in weakened trees and healthy saplings, suggesting that trees restricted E to balance the needle water budget. Heat pulse velocity of weakened trees leveled off at Emod lower than that of healthy saplings, probably due to lower capacity for water supply to the needles. Restriction of E would occur less frequently but be necessary for both weakened and healthy A. veitchii on Mt. Shimagare to avoid hydraulic failure, sacrificing photosynthetic carbon assimilation.

Greenhouse Gas Emissions Characteristics and Driving Factors Analysis in the Eutrophic Saline Lake Daihai Lake

To investigate the greenhouse gas emission characteristics and driving factors of eutrophic saline lakes in northern China, considering Daihai Lake in Inner Mongolia as an example, 10 monitoring sites were selected based on hydrological distribution characteristics in April, July, and October 2023. Using headspace gas chromatography and modeling methods, dissolved concentrations and exchange fluxes of carbon dioxide （CO2）, methane （CH4）, and nitrous oxide （N2O） were determined in the nearshore zone, open lake area, and lake center surface water. During the study period, Daihai Lake exhibited significant seasonal variations in greenhouse gas concentration and flux. The average concentrations of CO2, CH4, and N2O in surface water were （26.52 ± 17.58） μmol·L-1, （282.30 ± 172.30） nmol·L-1, and （9.09 ± 1.64） nmol·L-1, respectively. The average fluxes were （5.29 ± 11.98） mmol·（m2·d）-1, （178.24 ± 63.34） μmol·（m2·d）-1, and （-0.74 ± 1.28） μmol·（m2·d）-1, with cumulative emissions of 50 770.77, 543.52, -4.21 kg·km-2 and a global warming potential （expressed in CO2-equivalent） of 50 770.77, 15 218.49, -1 254.48 kg·km-2. Daihai Lake acted as a source of atmospheric CO2 and CH4 but a sink for N2O during the study period. Correlation and stepwise regression analyses revealed that pH and total dissolved solids （TDS） influenced CO2 concentration and flux, while the factors affecting CH4 were water temperature （WT）, water depth （WD）, wind speed （WS）, oxidation-reduction potential （ORP）, and total nitrogen （TN）. For N2O, the influencing factors were WT, WS, and TN. Additionally, Daihai Lake's eutrophication and salinity characteristics influenced the generation and emission of greenhouse gases. This study provides insights into the greenhouse gas dynamics and environmental factors in eutrophic saline lakes like Daihai Lake.