Greenhouse Gases: Science and Technology最新文献

Amino acids have shown promising results for carbon dioxide (CO₂) capture when functionalised on solid materials; however, the functionalisation often relies on commercial synthetic amino acids. This study investigated the optimal CO₂ adsorption performance of amino acid–functionalised material synthesised from palm shell–based activated carbon and natural amino acids, specifically egg white (EW) solution, in a continuous adsorption column. The process conditions of the column were optimised using response surface methodology. Four parameters, namely, the gas flow rate, adsorption temperature, CO₂ concentration and EW concentration in the impregnation solution, were identified as significantly affecting CO₂ adsorption performance. Good agreements were obtained between the predicted and experimental data, with the coefficients of determination ranging from 0.9639 to 0.9784. A maximum CO₂ adsorption capacity of 1.1793 mmol/g was achieved under optimal process conditions: a gas flow rate of 200 mL/min, an adsorption temperature of 25°C, a CO₂ concentration of 25 vol.%, and an EW concentration of 15 wt.%. The validation results further confirmed the reliability of the developed model equation in predicting the maximum CO₂ adsorption capacity at a fixed 15 vol.% CO₂ concentration, with low estimation error. The comparable results obtained using EW waste in this study represent a significant finding in the potential for waste valorisation, aligning with Sustainable Development Goal (SDG) 12 of the United Nations Sustainable Development Goals, as well as contributing to climate action as outlined in SDG 13.

This study outlines a comprehensive process design utilising glycerol-steam reforming for an H₂-enriched gas stream, coupled with carbon dioxide removal via a chemical absorption system, followed by a techno-economic analysis. The Aspen Plus economic analyser assesses the developed model, incorporating simulation results and literature data. Initially, the CO₂ capture unit was planned with a standalone absorber and stripper, later integrated for solvent makeup calculation. Findings reveal that as catalyst loading increased from 5 to 50 kg, glycerol conversion and product molar fraction improved. For a targeted H₂ production of 10 t/day, optimal reactor dimensions are 3.2 m diameter and 30 m length, corresponding to a reactant flow of 105 t/day and a 2.52 MW heat duty at stoichiometry conditions. To capture 95% CO₂ from the reformed product stream, absorber and stripper packing heights of 12 and 7 m, respectively, with column diameters of 1.25 and 2.71 m are necessary. The production cost of H₂ is determined to be $3.8 per kg, as revealed by the techno-economic analysis. Calculated values for net present value, discounted payback period, and internal rate of return stand at $30 million, 5 years, and 25.0%, respectively. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Ammonia carbon capture is characterized by low corrosion, resistance to oxidation and degradation, and low energy consumption for regeneration. However, it also presents challenges such as a slow absorption rate and notable ammonia escape. Current ammonia decarbonization research primarily focuses on the flue gas from power plants, which differs in composition from ship exhaust gas. To address this, we constructed a small carbon absorption test bench and used a mixture of CO₂ and N₂ as the ship exhaust gas. Ammonia solution and alcohol served as absorbents and additives, respectively, to explore the effects of the additive hydroxyl number, the concentrations of the additive and ammonia solution, and the reaction temperature on carbon loading, absorption rate, and ammonia escape. Results indicated that n-propanol was most effective in inhibiting ammonia escape, and a low concentration of ammonia solution was more suitable for absorbing CO₂. Specifically, when the concentration of ammonia was 4% and the concentration of n-propanol was 0.2 mol/L, the cumulative ammonia escape was reduced by 34% compared to the scenario without additives. Additionally, the carbon loading and average absorption rate reached 0.49 mol CO₂/mol NH₃ and 2.33 × 10⁻³ mol·kg⁻¹·min⁻¹, respectively, representing increases of 34.2 and 60.7%. However, as the reaction temperature increased, the effectiveness of n-propanol in enhancing ammonia absorption diminished. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

The reverse water-gas shift (RWGS) reaction offers an effective method for mitigating CO₂ emissions. Due to its affordability and physicochemical stability, iron has garnered significant attention as a potential catalyst for RWGS. The incorporation of nickel and copper promoters can enhance CO₂ conversion and CO selectivity in Fe-based catalysts. This study focuses on modifying the strength of the Strong Metal-Support Interaction (SMSI) through particle size optimization. Doping Cu into NiFe-based catalysts restricts particle size, which influences the curvature of the Ni⁰@FeO_x interface. This curvature enhances the electron coupling between Ni⁰ and FeO_x, promoting the formation of a denser and thicker Ni⁰ and FeO_x layer. This results in a nearly 90% increase in the CO₂ reaction rate during the sintering resistance test by anchoring Ni⁰ and facilitating electron transfer to active sites. Such morphological evolution improves high-temperature resistance to sintering during RWGS. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

As energy demand continues to rise and conventional fuel sources dwindle, there is growing emphasis on previously overlooked reservoirs, such as tight reservoirs. Shale and coal formations have emerged as highly attractive options due to their substantial contributions to global gas reserves. Enhanced shale gas recovery (ESGR) and enhanced coalbed methane recovery (ECBM) based on gas injection are advanced techniques used to increase the extraction of gas from shale and coal formations. One of the key challenges associated with these formations and their enhanced recovery methods is accurately predicting the sorption process and its profile. This is crucial because it affects how methane (CH₄) and carbon dioxide (CO₂) are stored and released from the rock, and it significantly impacts the evaluation of gas content and the potential productivity of these formations. Due to the high cost of experimental procedures and the moderate accuracy of existing predictive approaches, this study proposes various cheap and consistent data-driven schemes for predicting the sorption of CH₄ and CO₂ in shale and coal formations. In this regard, three intelligent models, including generalized regression neural network (GRNN), radial basis function neural network (RBFNN), and categorical boosting (CatBoost), were taught and tested using more than 3800 real measurements of CH₄ and CO₂ sorption in shale and coal formations. To find automatically their appropriate control parameters and improve their prediction ability, RBFNN and CatBoost were evolved using grey wolf optimization (GWO). The obtained results exhibited the encouraging prediction capabilities of the suggested models. In addition, it was found that CatBoost-GWO is the most accurate scheme with total root mean square (RMSE) and determination coefficient (R²) of 0.1229 and 0.9993 for CO₂ sorption, and 0.0681 and 0.9970 for CH₄ sorption, respectively. Additionally, this approach demonstrated its physical validity by respecting the real sorption tendencies with respect to operational parameters. Furthermore, the CatBoost-GWO model outperforms recently published machine learning approaches. Lastly, the findings of this study offer a significant contribution by demonstrating that the suggested model can greatly improve the ease of estimating CO₂ and CH₄ sorption in tight formations, thereby facilitating the simulation of other parameters related to this process. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Large-scale geological storage of carbon dioxide (CO₂) is indispensable for mitigating climate change but faces significant challenges, especially in the accurate quantitative assessment of leakage risks to ensure long-term security. Given these circumstances, this paper proposes an innovative approach for quantitatively assessing CO₂ leakage risk to address the previous limitations of limited accuracy and insufficient data. We construct a fault tree and transform it into a Bayesian network–directed acyclic graph, and then use judgment sets along with fuzzy set theory to obtain prior probabilities of root nodes. The feature, event, and process method was utilized to identify key components and subsequently determine the conditional probability table (CPT) of the leaf node. The subjective experience assessments from experts are defuzzified to obtain the CPTs of intermediate nodes. The obtained basic probability parameters are input into the directed acyclic graph to complete the model construction. After calculating the leakage probability using this model, it is combined with the severity of impacts to conduct a comprehensive risk assessment. Furthermore, critical CO₂ risk sources can be determined through posterior probability calculations when intermediate nodes are designated as deterministic risk events. The gradual implementation process of the proposed model is demonstrated via a typical case study. The results indicate an overall CO₂ leakage probability of 29%, with probabilities of leakage along faults/fractures, caprock, and well identified as 32%, 28%, and 19%, respectively. The project is categorized as a medium-low risk level. When leakage is confirmed, tectonic movement, thickness, and delamination at interface connections/the presence of cracks are the critical risk sources, and measures to mitigate key risks are outlined. The identified key risk factors conform to empirical evidence and previous research, validating the accuracy of the model. This study is instrumental in CO₂ geological storage risk assessment and scalable development program design. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.