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Organic amine solution is currently the most commonly used chemical absorbent for carbon dioxide (CO₂) capture, but its drawback such as high energy consumption for regeneration of CO₂ loaded solution has seriously hindered its wide application in industry. Development of catalysts could reduce the regeneration energy consumption of the process. In this study, the catalytic effects of raw multi-walled carbon nanotubes (MWCNTs) and acid-treated MWCNTs in 30 wt% monoethanolamine solution on both absorption and desorption procedures were investigated. The results showed that the addition of raw MWCNTs and acid-treated MWCNTs as catalysts both promoted the CO₂ desorption rate and amount, among which the 700 ppm acid-treated MWCNTs increased the CO₂ desorption amount by 48.49 %. Furthermore, both MWCNTs shortened the desorption time of CO₂ as well. Stability of acid-treated MWCNTs has been examined by 20 cycles in the capture system. Characterization results indicate that the acid treatment increased the surface area of MWCNTs, and created more acid sites on their surfaces through the defected area. Both characteristics contribute to the enhanced CO₂ desorption performance. Therefore, acid-treated MWCNTs can be considered as promising catalysts for promoting CO₂ desorption and reducing energy consumption for CO₂ capture.

Methane explosions can occur during methane transport of methane. A custom experimental platform was designed to conduct experiments on suppressing methane explosions with ultrafine palygorskite powder with different particle sizes. The purpose was to reduce disaster losses caused by methane explosions and ensure safe transportation. The power index of the explosion, flame suppression ratio, and maximum impact strain were used to characterize the explosion suppression performance of the powder. Material characterization analysis and simulation simulations were used to describe the explosion suppression mechanism. The results indicate that the ultrafine palygorskite powder with a particle size of 5.5–7.5 μm has the highest explosion suppression performance for methane explosions in pipeline networks. The ultrafine palygorskite powder absorbs reaction heat through pyrolysis. The oxygen atoms of the palygorskite molecules reduce the number of free electrons and the content of active free radicals in the reaction system by adsorbing hydrogen atoms from the methane molecules. The palygorskite molecules significantly slow the chain reaction rate during the methane explosion, performing chemical suppression. The results of this study provide a theoretical basis for preventing and controlling methane explosion disasters.

Spent catalysts are a significant source of metal-containing waste, and their disposal can pose environmental and economic challenges. Recycling these spent catalysts can not only reduce waste but also recover valuable metals, which can be used as raw materials for synthesizing new catalysts, as well as produce substrates for other industrial catalytic applications. Here we explore the recycling of spent fluid catalytic cracking catalysts (FCCCs) to obtain zeolite-based materials. Such substrates have been further doped with nickel via wet impregnation method to generate fresh catalysts for dry reforming of methane (DRM) reaction. Comprehensive analyses, including X-ray diffraction (XRD), BET surface area, scanning and transmission electron microscopy (SEM and TEM), H₂-temperature programmed reduction (H₂-TPR), NH₃-temperature programmed desorption (NH₃-TPD), and Ni dispersion via H₂-pulse chemisorption, were employed to characterize these catalysts. The performance of these recycled zeolite materials was evaluated and benchmarked against commercial zeolites. Our findings reveal that acid-leached, recycled zeolite obtained from spent FCCC catalyst results in the highest overall CO₂ and CH₄ conversion among the studied catalysts, as well as exhibiting a high stability over 20-hour testing, underscoring the potential of recycling strategies in catalyst production.

The accurate and rapid prediction of hydrocarbon type was a precondition for the utilization of fossil fuels with high efficiency and safety. In this study, machine learning based techniques were used to predict the type and equivalence ratio of flames of C₂-C₄ alkane and alkene fuels based on the differences in flame morphology between various combustion conditions. The test results of different machine learning algorithms, including ANN, SVM, SVR, KNN, MLR, and RF were compared in detail using statistical methods. Results indicated that ANN, SVM, KNN, and RF all exhibited an outstanding performance in predicting the types of C₂-C₄ alkane and alkene flames, achieving accuracies of 95.7 %, 96.3 %, 93.8 %, and 96.5 %, respectively. For the prediction of the equivalence ratio among these fuels, the mean absolute percentage errors of the ANN, SVR, MLR, and RF were only 5.6 %, 3.8 %, 8.2 %, and 3.8 %, respectively. The performance of SVM, SVR, and RF algorithms was significantly superior to that of ANN, MLR, and KNN algorithms for flame prediction. Moreover, the data of feature analysis revealed that the importance level of designed features exhibited a significant distinction between different prediction targets. For predicting the type of C₂-C₄ alkane and alkene fuels, the features associated with blue region showed a stronger importance level. However, the yellow region related features played a more significant role for the prediction of the equivalence ratio.

In situ XRD and XANES experiments were used to analyze structural changes influencing the catalytic activity of Ni-Fe/MgAl₂O₄ catalysts during the ethanol steam reforming (ESR) reaction. Activation with H₂ reduced nickel and iron oxides to their metallic forms, producing Ni-Fe alloy on the bimetallic catalysts. Phase composition changes during activation depended on the Ni:Fe ratio. Catalysts with Ni ≥ Fe showed no Fe_xO_y XRD peaks, while when Ni < Fe, these peaks were exhibited during reduction. In situ XANES showed higher reduction for bimetallic catalysts, compared to monometallic catalysts, with the reduction decreasing as more iron was added. Catalytic tests were performed in conventional ESR and H₂-added (ESRH) atmospheres. The in situ techniques revealed restructuring during ESR, where contact with the reactants resulted in the Ni-Fe particles being slightly oxidized. As the temperature increased, iron migrated and was oxidized to Fe_xO_y, while nickel re-reduced. The extent of restructuring depended on reactant composition and iron content. A milder impact was exhibited in ESRH, leading to catalyst stabilization. This phenomenon enabled elucidation of the catalytic features linked to the accessibility of metallic sites responsible for cleaving C–C and C–H bonds. Furthermore, Fe_xO_y aided oxidation of the carbon residue, leading to lower carbon accumulation and greater stability of the bimetallic catalyst.

The micromechanical properties of coals are key parameters affecting the development of coalbed methane (CBM). In order to understand the relationship between the micromechanical properties and the nanocarbon structure of coals, high resolution transmission electron microscopy (HRTEM) and nanoindentation were used to investigate the micromechanical properties of vitrinite in different coals and the response to changes in nanocarbon structure. Coals with higher coal rank have greater elastic modulus and hardness. The end-of-loading displacement and creep displacement decrease with the increase of coal rank, both of which have a good negative correlation with the elastic modulus and hardness. Fringe length, degree of orientation, proportion of straight fringes, and proportion of stacks all increase with increasing coal rank. The curvature of the fringes decreases with increasing coal rank. The variation of the nanocarbon structure has an important effect on the micromechanical properties. The nanocarbon structure parameters of fringe length, proportion of straight fringes, degree of orientation, and proportion of stacks have good linear relationships with the micromechanical parameters of elastic modulus and hardness. In addition, this study analyzes the effect of the micromechanical properties of coal on the effectiveness of the proppant. Therefore, the evolution of the nanocarbon structure should be emphasized in future studies on the micromechanical properties of coal.

The significance of ammonia (NH₃) as an alternative fuel is increasing due to its potential as a carbon-free energy carrier. However, ammonia is known for its difficult combustion characteristics. One possible solution is to blend it with a high reactivity diesel fuel. In this work, n-dodecane has been chosen as the diesel surrogate. In order to investigate the combustion of ammonia blends with n-dodecane, a new skeletal chemical kinetic mechanism is developed. The proposed model consists of 75 species and 451 reactions, making it the most concise state-of-the-art mechanism. For the mechanism development, (a) experimental data from literature has been gathered, (b) 8 objectives have been defined for 17 experimental data sets, (c) sensitive reactions for the objectives have been identified with the open-source Cantera software, (d) the pre-exponential factors pertaining to 86 sensitive reactions have been optimized within the relevant uncertainty ranges, and (e) the JAYA algorithm has been utilized for the optimization. The optimized mechanism shows roughly a 70% improvement for the specified objectives, such as ignition delay times, laminar burning velocities, and species profiles, compared to the unoptimized starting mechanism. Additionally, very good agreement is observed with literature data for a wide range of operating conditions during validation. The proposed mechanism is accompanied by an open source graphical user interface for efficient mechanism optimization.

The chemical pulping of biomass involves the recycling of calcium through the calcination of lime mud, which is mostly comprised of calcium carbonate (CaCO₃). Lime mud decomposes under elevated temperatures to generate calcium oxide (CaO) and carbon dioxide (CO₂), the kinetics of which are strongly influenced by the CO₂ partial pressure and temperature. Oxy-fuel combustion and electrified lime kilns for lime mud calcination are intriguing methods to decarbonize this highly polluting operation within the biomass pulping industry. However, the high CO₂ concentration in oxy-fuel and electrified calcination processes alters the kinetics and overall reactivity of lime mud. For the first time, a model-fitting method is used to determine the kinetic parameters for lime mud calcination under a wide range of temperatures (550 °C–1250 °C) and under different concentrations of CO₂ (0 %, 15 %, 50 %, and 90 %). A kinetic model is developed that accurately predicts the reaction rates as a function of temperature and CO₂ concentration. The apparent activation of energy for lime mud calcination is elevated under a high CO₂ environment. Relative to inert gas (N₂, Ar), the temperature window for calcination is much smaller under high CO₂ environments. The presence of Na in lime mud does not seem to affect calcination under a high CO₂ environment. Finally, particle size variation does not have a significant effect on calcination under a high CO₂ environment.

Hydrogen is a promising clean energy carrier, but its widespread adoption relies on the development of efficient and safe storage solutions. Solid-state materials have emerged as attractive candidates for hydrogen storage due to their high capacities, favorable thermodynamics and kinetics, and enhanced safety. First principle calculations have played a crucial role in advancing the understanding and design of these materials. This comprehensive review critically assesses the state-of-the-art in applying first principle methods to study various hydrogen storage materials, including binary hydrides, intermetallic hydrides, and complex hydrides. By examining the electronic structures, thermodynamic and kinetic properties, and reaction mechanisms, we highlight the key insights gained from first principle calculations in elucidating hydrogen storage mechanisms. We discuss strategies for optimizing the composition and structure of storage materials and assess the capabilities and limitations of computational techniques such as density functional theory, molecular dynamics simulations, and machine learning. This review emphasizes the importance of integrating computational and experimental studies and identifies future research directions to address challenges in developing practical solid-state hydrogen storage solutions. With its comprehensive scope and critical analysis, this work provides valuable guidance for researchers, engineers, and policymakers working towards a sustainable hydrogen economy and highlights the vital role of first principle calculations in accelerating the discovery and optimization of advanced hydrogen storage materials.

This research delves into an innovative solar energy integrated combined plant, a cutting-edge technology that produces a range of valuable outputs including power, freshwater, hydrogen, and hot water for heating. The developed scheme incorporates a solar collector, supercritical Brayton cycle, transcritical Rankine cycle, multi-effect desalination unit, and PEM electrolyzer. A comprehensive evaluation of the system’s thermodynamic and economic performance, including energy and exergy efficiency, exergy destruction rate, hydrogen generation cost, and total investment cost rates, is conducted. The analysis revealed a net power production load of 505.8 kW, hydrogen capacity of 0.0004139 kgs⁻¹, and a freshwater production rate of 5.698 kgs⁻¹.

The research yielded promising results, with the total exergy destruction rate calculated at 5706 kW, and the solar collector identified as the most efficient component. The energetic and exergetic performance of the developed scheme is determined to be 33.92 % and 30.83 %, respectively, indicating a high level of efficiency. The economic cost studies further revealed that the entire investment cost rate of the proposed scheme is a mere 0.0019 $s⁻¹, demonstrating the potential for cost-effective implementation.