Upgrading clean energy fuels, such as biogas, natural gas, and shale gas, requires the capture of CO 2 to enhance their heating value. Ionic liquids (ILs) are promising absorbents for this purpose, but the vast number of available ILs necessitates an efficient screening method. The Comparative Absorption Factor ( CAF ) developed in our previous study can estimate the total annual cost ( TAC ) of CO 2 capture from biogas, which is a key advantage over alternative screening methods. However, CAF does not consider the effects of operating conditions such as CO 2 concentration, pressure, and temperature. To address this limitation, a modified CAF ( CAF modified ) that incorporates these factors was proposed. Given the linear relationship between the original CAF and TAC , three representative ILs ([C 10 mpy][DCA], [C 1 mim][tfo], and [C 1 py][tfo]) were selected from 490 ILs based on their melting point, viscosity, and original CAF values. Subsequently, process simulations for these ILs were conducted using Aspen Plus, considering a wider range of operating conditions: CO 2 concentrations of 30–50 vol%, temperatures of 303.15–318.15 K, and pressures of 7–15 bar. These simulations were used to determine the Aspen Plus-derived TAC , which served as the basis for proposing CAF modified . Finally, data for Aspen Plus-derived TAC from the previous study and for 6 additional ILs over a broad range of operating conditions were used to compare with the TAC values estimated by CAF modified . The results showed an average relative deviation of 16%, indicating that CAF modified is effective for screening ILs for CO 2 capture under varying operating conditions. A comparison of the total annual costs ( TAC ) calculated by the original and modified Comparative Absorption Factor ( CAF ), incorporating the effects of operating conditions. • Modified CAF factor integrates CO 2 concentration, temperature and pressure effects. • 15.82% accuracy in TAC prediction across 30–50 vol% CO 2 , 303–318 K, 7–15 bar. • Rapid ILs screening for biogas/shale/natural gas upgrading without process simulation.

Solvent screening strategy of ZIF-8 slurry towards C2+ hydrocarbons recovery from natural gas

Sector-specific ambient air pollution and biomarkers of liver injury. Findings of a cross-sectional population-based survey.

Simulation on Frosting and Heat Transfer of High-CO2 Content in the Pressurized Liquefaction of Natural Gas

Effect of hydrogen-blended ratio on the leakage and explosion characteristics of hydrogen-blended natural gas in residential kitchens

Root cause analysis of corrosion-induced perforation in a natural gas pipeline elbow

Techno-economic and environmental analysis of coal-to-olefin integrated with nature gas co-gasification and green hydrogen

A probabilistic model for natural gas pipeline failure under climate-induced Natech hazards: Toward AI-based safety management

Enhanced methanol metabolism via reinforced cellular energy and reducing power supply for sustainable carbon conversion.

Extraction of type sII natural gas hydrates via CO2 replacement combined with thermal stimulation and depressurization

Air liquefaction process in liquid air energy storage integrated with liquefied natural gas cold energy: Simulation and experiment

Influence of H2 blending on NOx formation mechanism in natural gas laminar premixed flames

Real-time prediction of gas leakage and diffusion for buried natural gas pipelines by deep learning and dimensionality reduction methods

Damage assessment method for natural gas pipeline dents via modified Mohr-Coulomb criterion

• A process integrating turquoise hydrogen with a blast furnace was proposed. • Four cases were proposed based on the heat supply method and hydrogen purity. • An increase in injection temperature is correlated with a higher replacement ratio. • All proposed cases achieved negative CO 2 emissions. • Supplying high-purity hydrogen was the best strategy for environment and economy. Efforts to decarbonize the steel sector primarily follow two pathways: the use of alternative low-carbon fuels (e.g., hydrogen, ammonia) for blast furnace (BF)-based ironmaking, and the adoption of electrified processes utilizing direct reduced iron in electric arc furnace-based ironmaking. In this study, synergistic process integration is proposed for hydrogen-based BF ironmaking, and its techno-economic and environmental impacts are assessed. Turquoise hydrogen, produced via natural gas pyrolysis, is designed across four cases to examine how variations in injection temperature and hydrogen purity affect the balance among process design, economic performance, and CO 2 mitigation potential. Heat supply strategies, including hydrogen purification units, are also considered. Each case is evaluated in terms of energy consumption, BF injection performance, economic feasibility, and environmental impact. The findings reveal that Case A achieved the highest energy efficiency of 60.4%, while Case D showed the lowest at 47.6%. Regarding BF performance, increasing the injection temperature of high-purity H 2 improved the H 2 -to-coke replacement ratio from 1.10 to 1.46 kg_coke/Nm 3 -gas, enabling a significantly higher H 2 injection rate of up to 41 kg H2 /tHM. Economically, the integration proved highly competitive due to the solid carbon byproduct; Case D achieved the most favorable unit production cost (UPC) of − 0.29 US$/kg-gas, compared to 0.016 US$/kg-gas for Case A. Environmentally, Case D also demonstrated the superior sustainability profile with a net-negative CO 2 emission of − 7.43 kg CO 2 -eq./kg-gas. Overall, the proposed integration of turquoise H 2 with BF ironmaking demonstrates strong economic and environmental performance. A remaining challenge is determining the optimal degree of hydrogen purification for alternative applications within the ironmaking process.

A hybrid deep learning model driven by physical mechanisms and data for predicting corrosion in natural gas pipelines

Numerical investigation on turbulent jet ignition and combustion processes near dilution limit in a natural gas pre-chamber spark-ignition engine

Transportation performance of hydrogen-blended natural gas under optimal hydrogen injection angle

Oscillation and instability features of hydrogen-blended natural gas mixtures deflagration in a semi-open vertical pipeline

Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high-throughput density functional theory (DFT) and interpretable machine learning to accelerate the rational design of catalysts. Computational screening of face-centered-cubic (FCC) bimetallic catalyst surfaces identifies the bond cleavage energies of the first and the second C─H bonds and methyl adsorption energy as a key descriptor governing successive C─H activation and the shift in the rate-determining step (RDS). Through the synergistic interaction of these descriptors, machine learning models can be constructed more effectively, leading to the discovery of a bimetallic catalyst for consecutive C─H bond cleavages that outperforms conventional natural gas engine aftertreatment systems. Based on the computationally derived DFT dataset, four machine learning models were trained using a particle swarm optimisation (PSO) algorithm, from which the optimal model capable of accurately predicting C─H bond energies was selected. This model also further revealed the dominant electronic structural features of the predictive model through SHapley additive interpretability (SHAP) analysis. This work establishes an interpretable, data-driven methodology for designing high-efficiency multicomponent catalysts.