Combustion Applications Research Articles

Spinel-Based Catalysts That Enable Catalytic Oxidation of Volatile Organic Compounds.

Volatile organic compounds (VOCs) have caused serious harm to human health and ecological environment, and have received much attention in recent years. Despite the successful applications of catalytic combustion of VOCs as the core technology of VOCs removal in industry, the development of efficient catalysts that can mineralize VOCs into nontoxic CO2 and H2O at low temperatures remains a great challenge. Recent studies show that spinel-based materials as efficient catalysts were extensively used in the catalytic oxidation VOCs field due to their synergistic effect, manifold compositions, and electron configurations. However, most of the pollutants are complex, consisting of multiple VOCs, water vapor, CO2, SO2 and other substances, which presents a significant challenge in constructing highly active and stable catalysts. To meet the future demand for efficient catalysts capable of removing various types of VOCs, it is urgent to rationally design and scientifically prepare spinel catalysts based on existing knowledge. This work reviews the research and development of various spinel catalysts with an emphasis on their catalytic performance in VOCs oxidation. The catalytic performance of spinel-based catalysts for different sorts of VOCs was summarized and compared. Moreover, the effects of the reaction conditions on the catalytic performance of spinel-based catalysts were examined to accommodate complicated operating conditions. Subsequently, the regulation of spinel oxides in structure and defect was coherently reviewed to guide the development and design of efficient catalysts. Especially, the research techniques for the reaction mechanism over spinel catalysts were displayed to better deepen the understanding of catalytic oxidation of VOCs. Finally, the current development and challenges were proposed and put forward for future research. This review provided a systematic understanding of the VOCs oxidation over spinel-based catalysts and offered guidance for the development of high-performance catalysts for VOCs elimination.

Effect of sludge-based additive on particulate matter emission during the combustion of agricultural biomass pellet

In this study, the effects of a sludge-based additive on particulate matter emission during the cornstalk pellet combustion were investigated using a vertical fixed bed reactor combined with a Dekati low pressure impactor (DLPI). It was found that the PM1 and total PM10 yields from the cornstalk pellet combustion were much higher than those from the individual combustion of sludge-based additive. With the addition of sludge-based additive, the alkali and alkaline earth metals (AAEMs) in biomass would react with the Al/Si-containing and P-containing inorganic minerals in sludge-based additive to form various aluminosilicates, silicates, and K-Ca-P compounds, which could enhance the potassium fixation and retain it in the coarse particles, such as fly ash and residual ash. The PM1 and total PM10 yields from the composite pellet combustion could be effectively reduced by 62%, with the mixing ratio of sludge-based additive increasing to 75%. The mixing ratio of sludge-based additive showed a positive synergistic effect in reducing the emission of PM and a mixing ratio of <30% was recommended based on comprehensive considerations to produce relatively high-quality pellet fuel with respect to good heating value and low ash content and PM emission in later combustion application.

Experimental Determination of the Temperature‐Dependent Broadening Effect of Water Vapour on the S‐Branch Raman Linewidths of Nitrogen

ABSTRACTThis work reports the S‐branch Raman linewidths of N2 broadened by H2O vapour at temperatures 870–1900 K determined using the picosecond time‐resolved pure rotational coherent anti‐Stokes Raman scattering (RCARS) approach. The coherent dephasing time constant of nitrogen in a binary mixture was determined from the time trace fitting of the RCARS signal. Raman linewidths of N2 for four different binary mixture compositions up to 20% H2O were obtained for each temperature and the N2‐H2O broadening coefficient was determined. The decay of nitrogen broadened by water vapour is faster than that of self‐broadened nitrogen for all temperatures leading to a higher Raman linewidth. Respectively, the S‐branch Raman linewidths of N2‐H2O deviate significantly in magnitude and slope from the Q‐branch Raman linewidth calculated by the modified energy gap (MEG) law. Therefore, taking into account the broadening effect of water molecules in nitrogen has the potential to significantly improve thermometry and species concentration determination. The determined S‐branch Raman linewidths and broadening coefficient will therefore find application in combustion and reactive flow diagnostics.

A Deep Learning Model for Predicting the Laminar Burning Velocity of NH3/H2/Air

Both NH3 and H2 are considered to be carbon-free fuels, and their mixed combustion has excellent performance. Considering the laminar burning velocity as a key characteristic of fuels, accurately predicting the laminar burning velocity of NH3/H2/Air is crucial for its combustion applications. The study made improvements to the XGBoost model and developed NH3/H2/Air Laminar Burning Velocity Net (NHLBVNet), which adopts a composite hierarchical structure to connect the functions of feature extraction, feature combination, and model prediction. The dataset consists of 487 sets of experimental data after the exclusion of outliers. The correlation coefficient (R2 &gt; 0.99) of NHLBVNet is higher than that of the XGBoost model (R2 &gt; 0.93). Robustness experiment results indicate that this model can obtain more accurate prediction results than other models even under small sample datasets.

Agricultural Waste for Energy Storage, Conversion and Agricultural Applications

Agricultural waste residues (agro-waste) present a significant source of carbohydrates that are often underutilized despite their valuable properties. With increasing urbanization and limited non-renewable resources, the valorization of agro-waste is imperative. The global energy demand is on the rise, driven by factors such as population growth, industrialization, and a desire for enhanced living standards. Traditional energy sources, especially fossil fuels, have come under scrutiny for their environmental impact and limited availability. Consequently, there is an increasing focus on developing renewable and sustainable energy solutions, particularly through the use of agricultural waste. Agricultural waste—including crop residues, animal manure, and byproducts from food processing—represents a largely untapped resource for energy production. Converting this waste into valuable energy can help meet rising energy needs while offering environmental and economic advantages, such as mitigating waste disposal issues and creating additional income sources for the agricultural sector. Despite the significant potential of agricultural waste for energy storage and conversion, several challenges remain. These include issues related to logistics and transportation, the need for pretreatment, and concerns about economic feasibility. Future research should aim to enhance conversion technologies and better integrate agricultural waste into energy and agricultural systems. This review discusses various energy conversion technologies and applications of agricultural waste, including biofuels, biogas, and direct combustion, while exploring its role in energy storage through biochar. This review examines the composition and properties of agricultural waste, the various technologies available for energy conversion, and how agricultural waste can be utilized as a feedstock for biofuels, biogas, and direct combustion. It also investigates the integration of agricultural waste in energy storage solutions like biochar and explores other agricultural applications beyond energy production.

Two-Stage Global Biomass Pyrolysis Model for Combustion Applications: Predicting Product Composition with a Focus on Kinetics, Energy, and Mass Balances Consistency

This work presents a comprehensive model for lignocellulosic biomass pyrolysis, addressing kinetics, energy balances, and gas product composition with the aim of its application in wood combustion. The model consists of a two-stage global mechanism in which biomass initially reacts into tar, char, and light gases (non-condensable gases), which is followed by tar reacting into light gases and char. Experimental data from the literature are employed for determining Arrhenius kinetic parameters and key energy parameters, like tar and char heating values and the specific enthalpy of primary and secondary reactions. A methodology is introduced to derive correlations, allowing the model’s application to diverse biomass types. This work introduces several novel approaches. Firstly, a pyrolysis model that determines the composition of light gases by solving mass, species, and energy balances is developed, limiting the use of correlations from the literature only for tar and char elemental composition. The mass rate of light gases, tar, and char being produced is also determined. Secondly, kinetic parameters for primary and secondary reactions are determined following a Shafizadeh and Chin scheme but with a modified Arrhenius form dependent on Tn, significantly enhancing the accuracy of product composition prediction. Additionally, correlations for the enthalpies of reactions, both primary and secondary, are determined as a function of pyrolysis temperature. Primary reactions exhibit an overall endothermic behavior, while secondary reactions exhibit an overall exothermic behavior. Finally, the model is validated using cases reported in the literature, and results for light gases composition are presented.

Production of hydrochar fuel by microwave-hydrothermal carbonisation of olive pomace slurry from olive oil industry for combustion application

This work is the first investigation on microwave-assisted hydrothermal carbonisation (MHTC) of real olive pomace (OP) slurry from the olive oil industry to produce hydrochars with improved fuel properties for combustion applications (e.g., in boilers). Experiments were conducted based on the central composite design of response surface methodology with two main process variables: temperature (180–250 °C) and holding time (2–30 min). Severity factors (log R0) were calculated from the above variables and used to explain the process effect in a simpler way. Increasing the MHTC severity resulted in significant changes in the structure of OP (studied by FTIR and NMR analyses) as well as reductions in yield, bulk density, volatile matter, and ash content in the hydrochars. The high-severity hydrochar was positioned in the lignite zone (van Krevelen diagram). It also exhibited substantial reductions in the alkali index (86.53 %), slagging index (76.89 %), and fouling index (96.07 %) compared to the raw material. Overall, the best conditions for hydrochar production with improved combustion characteristics were found to be 250 °C for 30 min (HHV = 28.45 MJ/kg, energy densification ratio = 1.25, equilibrium moisture content = 31.1 mg/g, comprehensive combustibility index = 2.94 × 10−7%2 min−2 ºC−3). These properties indicate that high-severity hydrochars could be utilised as biofuels for energy applications.

STABILITY AND COMBUSTION/EMISSION CHARACTERISTICS OF STRATIFIED DUAL-SWIRL OXY-METHANE FLAMES: EFFECTS OF OXYGEN FRACTION

Abstract The stability, combustion, and emission features of stratified oxy-methane (CH4/O2/CO2) flames stabilized over a dual annular counter-rotating swirl (DACRS) burner, developed for gas turbine combustion applications, were investigated experimentally. The experiments were performed at fixed velocity ratio (Vr=Vp/VS=3.0) in both the primary and secondary streams at a constant primary stream velocity, Vp of 5 m/s and at fixed primary stream equivalence ratio, φP=0.9, and over ranges of oxygen fractions (OFP for the primary stream, OFS for the secondary stream) and secondary stream equivalence ratios. Measurements of flame macrostructure, temperature profiles, and exhaust emissions were recorded to characterize the flames and validate future numerical models. The testing findings revealed no flame flashback within the operational ranges of OFP and OFS and up to φs = 1.0. However, the near stoichiometric operation of the primary stream (φp = 0.9) at OFp =0.38, permitted the main secondary flame to tolerate exceptionally lean conditions (φs = 0.397 at OFs =0.34 and φs = 0.223 at OFs =0.39), raising the thresholds for flame blowout. Increasing OFP from 0.21 to 0.38 significantly reduced φS at blowout from 0.537 to 0.223, corresponding to a decrease in the combustor's global equivalence ratio (φg) at blowout from 0.554 to 0.254 at global oxygen fraction (OFg) from 0.38 to 0.39. Lower OFp values caused earlier flame lift-off, indicating the greater influence of OFp on flame macrostructures.

CFD modeling investigation of oxy-fuel combustion application in an industrial-scale FCC regenerator

The increase in atmospheric CO2 concentration and its consequential impact on climate change have elicited increased public concern. The refinery units including fluid catalytic cracking (FCC) generate substantial quantities of CO2. To mitigate the emission from the FCC process, oxy-fuel combustion has emerged as a prospective carbon capture and storage technology. This study presents the first trial for the modeling investigation of a 70 kt/a industrial FCC regenerator under the scenario of retrofitting it with oxy-fuel combustion technology. Employing the Eulerian-Eulerian model, a CFD model integrating heat transfer and coke combustion reactions has been established. The detailed hydrodynamics, temperature, and species concentration distribution inside the regenerator are obtained under both air-firing and oxy-firing conditions, which are further compared to exploit the possibility of oxy-fuel combustion retrofitting. As has been found, decreases in gas temperature and carbon conversion rate were observed for 21 % O2/79 % CO2 atmosphere in comparison to the air reference case due to the differences in gas properties between N2 and CO2. This discrepancy resulted in a drop of 17 K in dilute phase temperature and 2 K in dense phase temperature. The bed density also exhibited a large with the oxy-firing conditions, with notable observations revealing a lower bed density below a height of 4.2 m, transitioning to a higher density above said height. Sensitivity analysis was also conducted for three principal operating parameters, including superficial gas velocity, oxygen partial pressure, and catalyst circulation rate. An increase of oxygen partial pressure to 27 % or a decrease of the catalyst circulation rate to 20.7 kg/s proved effective in achieving the same temperature profile and even a slightly better carbon conversion in comparison to air-firing regeneration.

Development of laminar burning velocity prediction model and correlation of iso-octane air mixtures using artificial neural network

Surrogate fuels provide an economical alternative for forecasting the combustion properties of transportation fuels like diesel, gasoline, and kerosene. Iso-octane (2,2,4-trimethylpentane), a primary reference fuel for gasoline, is extensively used as a surrogate component. This study utilizes a Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) to predict the laminar burning velocity (LBV) of iso-octane/air mixtures. The ANN model was developed using a dataset of 6339 data points, including 3788 experimental data points from literature since 2004 and 2551 simulation data points from reduced kinetic reaction mechanism (RKM) CHEMKIN simulations. The grid search cross-validation (CV) method was employed to optimize the ANN hyperparameters. Implemented in Python using Keras, the ANN model addresses research gaps by forecasting LBV for hydrocarbons beyond n-heptane and modelling LBV under conditions reflective of actual engine operations. Unlike prior studies, we optimized various ML models, their complexity, size, and hyperparameters to achieve high prediction accuracy. We also integrated a hybrid model combining Particle Swarm Optimization (PSO) with Genetic Algorithms (GA) for hyperparameter optimization, ensuring efficient configuration of the ANN. The constructed ANN model was compared to other ML models, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), and XGBoost regression. For the testing set (20 % of the total dataset), the ANN model outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R2) of 0.9903, root mean square error (RMSE) of 1.911, and mean absolute error (MAE) of 1.206. Optimizing the ANN model with PSO further enhanced prediction accuracy, achieving a correlation coefficient of 0.9973 and a reduced mean absolute error of 0.753. To evaluate the ANN model's predictive ability, a reduced mechanism from the Lawrence Livermore National Laboratory (LLNL) was used. Compared to the LLNL RKM, the ANN predicted values showed lower percentage deviation from experimental data. Additionally, computation cost comparisons revealed that while RKM simulations in CHEMKIN took 244.2 s per case, the PSO-optimized ANN model required only 900 s for 150 cases. A 16-term correlation for laminar burning velocity (LBV) was derived from ANN predictions, serving as a practical tool to predict LBV based on pressure, temperature, and equivalence ratio. Unlike existing literature correlations, this newly developed correlation is valid across a wide range of operating conditions, offering a fundamental database for real-time combustion applications.

Stability and combustion characteristics of dual annular counter-rotating swirl oxy-methane flames: Effects of equivalence and velocity ratios

An experimental study was carried out to investigate flow/flame interactions, stability, combustion, and emissions properties of CH4/O2/CO2 stratified flames stabilized on a dual annular counter-rotating swirl (DACRS) burner for gas turbine combustion applications. The effects of two targeted parameters, namely equivalence ratios (for primary - ϕp, secondary - ϕs, and global - ϕg streams) and velocity ratio (Vr = Vp/Vs: primary to secondary stream inlet velocity ratio), are studied at fixed volumetric oxygen fraction (OF) in the oxidizer mixture (O2 + CO2) of both primary and secondary streams of 34 % at fixed Vp of 5 m/s. The experimental results indicated that no flame flashback was recorded in the domain of any operational ϕp up to stoichiometric operation of the secondary stream (ϕs = 1.0). At near stoichiometric operation of the primary stream, the main secondary flame can persist in extremely lean conditions (ϕs = 0.45 @ Vr = 3), thereby extending the thresholds of flame blowout. Moreover, raising ϕp from 0.4 to 1.0 results in significant reduction of ϕs at blowout from 0.595 to 0.456, corresponding to reducing the combustor ϕg at blowout from 0.577 to 0.499. At lower ϕp, the oxy-methane flame tends to lift and extinguish earlier.

Application of Boundary Layer Combustion in High-Mach-Number Scramjets

Boundary layer combustion is an approach to the reduction of skin friction in hypersonic propulsion systems based on reducing the Reynolds shear stress in the turbulent boundary layer by combustion heat release. In this study, the Reynolds-averaged Navier–Stokes methodology is adopted to study the drag reduction characteristics of boundary layer combustion in high enthalpy flow. Two novel explanations of the local increase in skin friction caused by ignition delay are given in terms of flow structure and the Kármán momentum integral equation, respectively. In addition, it is found that the inhibitory effect of combustion heat release on Reynolds shear stress is gradually weakened with increasing total enthalpy, which is detrimental to drag reduction. Finally, a combination of radical farming and boundary layer combustion is proposed to improve the drag reduction effect. This approach is verified by using a two-dimensional simplified M12-02 scramjet model, with the results indicating that radical farming can considerably suppress ignition delay and thereby further benefit drag reduction and combustion.

Long-Term performance of composite Cu-Fe ore oxygen carrier by Extrusion-Spheronization in chemical looping combustion

High-performance and low-cost oxygen carrier is vital for engineering demonstration and industrial application of chemical looping combustion. In this work, binary-ore (copper ore and iron ore) oxygen carriers are prepared at a large scale (25 kg/h) by the extrusion-spheronization method, using montmorillonite as the inert support. The anti-sintering ability and long-term stability are systematically investigated through well-organized tests in a thermogravimetric analyzer (TGA) and batch fluidized bed reactor (BFB). Four kinds of low-cost composite oxygen carriers are prepared with montmorillonite content of 10 wt%∼40 wt%, and the montmorillonite loading ratio is optimized as 10 wt% through 100-cycle TGA tests, with the fastest reduction rate of 0.155 %/s. The results show that the CO2 yield is always beyond 90 %, suggesting the stable reactivity of FeCu-10 M, and the average attrition rate is 0.108 %/h. No significant sintering or agglomeration is detected in the inner surface or among the particles, as indicated by SEM images. The specific surface area is developed from 0.125 to 0.185 m2/g, and the rich pore structure remains in the 100-cycle test. Additionally, the crushing strength of FeCu-10 M is higher than 2 N throughout the cyclic experiment, and the lifetime is 2 ∼ 7 times longer than that of the original iron ore. The extrusion-spheronization method has the potential in the industrial-scale preparation of oxygen carriers, and the FeCu-10 M is a favorable oxygen carrier to the chemical looping combustion process.

The Planar Mixing Layer Flame (PMLF): a canonical configuration for studying non-premixed combustion chemistry and soot inception

The study of fuel chemistry and soot inception in non-premixed combustion can be advanced by characterizing flame configurations in which the advection and diffusion transport can be finely controlled, with the ability to decouple pyrolysis from oxidation. Also, the ideal flames to be investigated should be perturbed minimally by probes and thick enough for sampling techniques to yield spatially resolved measurements of their structure. The Planar Mixing Layer Flame (PMLF) configuration introduced herein is established between a fuel and an oxidizer slot jet adjacent to each other and shielded from the ambient air by annularly co-flowing inert nitrogen. The PMLF flow is kept laminar and steady by an impinging flat plate equipped with a rectangular exhaust slit opening which anchors the position of the hot combustion products via buoyancy. The PMLF is accessible to sampling and its flow stability is preserved when using any tested probe. The experiments are complemented with 2D- Computational Fluid Dynamics (CFD) modeling with detailed chemical kinetics. The results demonstrate that the PMLF has a self-similar boundary layer structure whose horizontal cross-sections are equivalent to properly selected and equally thick 1D- Counterflow Flames (CFs). The equivalence allows for excellent predictions of the PMLF thermochemical structure characterized experimentally but at a small fraction of the 2D-CFD computational cost. The 1D-CF equivalence affects even aromatics less than twofold despite their kinetics being known to be very sensitive to the temperature field. Importantly, the PMLF thickness is several millimeters and grows at increasing HABs so that the equivalent 1D-CFs have strain rates as small as 7.0 /s which cannot be studied in CF experiments. As a result, the PMLF emerges as a promising canonical non-premixed flame configuration for studying flame chemistry and soot inception on time scales of tens of milliseconds typical of many combustion applications.

Assessment of RANS-based turbulence models for isothermal confined swirling flow in a realistic can-type gas turbine combustor application

The present study assesses RANS-based turbulence models to simulate the isothermal confined swirling flow in a combustor representing a constituent can combustor of the can-annular configuration used in jet engines. The two-equation models (standard k−ϵ, realizable k−ϵ, standard k−ω, SST k−ω), and seven-equation model (linear pressure strain-Reynolds stress model, LPS-RSM), are assessed by comparing their predictions of mean axial velocity, mean transverse velocity, turbulent kinetic energy, and shear stress with the experimental data at two different positions (i.e., the primary and dilution hole planes) in the combustor. While the two-equation models generally have failed to predict the confined swirling flow at both positions accurately, the SST k−ω model yielded the most accurate, followed by standard k−ω and realizable k−ϵ models. The discrepancies between the computational and experimental results could be attributed to the isotropic turbulence assumptions, which, however, are invalid for confined swirling flows. Further, the two-equation model formulations cannot capture the intricacies of vortex flow and its interaction with the surroundings in confined swirling flows. The LPS-RSM, which considers turbulence anisotropy, showed some promise, although overpredicted results follow the trend with experimental values at the primary holes plane. However, at the dilution holes plane, the model overpredicted the velocity field, and underestimated the turbulence field, including turbulent kinetic energy and shear stress. These observed discrepancies can be ascribed to the pressure-strain correlation in the LPS-RSM, which assumes the pressure is a linear function of the strain-rate tensor. However, this linear assumption is quite simplistic for complex flows. Further, the influence of discretization (SOU and third-order MUSCL) schemes of convective terms is also assessed, and the differences in predictions resulted from MUSCL scheme having lower diffusion and superior ability to capture sharper gradients, however, did not translate into improving the solution accuracy. Hence, this study suggests that more advanced high-fidelity turbulence models (e.g., hybrid RANS-LES, LES, DNS) are needed to accurately predict the confined swirling flow in combustors.

Passive Control of Vortices in the Wake of a Bluff Body

Vortices belong to the most important phenomena in fluid dynamics and play an essential role in many engineering applications. They can act detrimentally by harnessing the flow energy and reducing the efficiency of an aerodynamic device, whereas in other cases, their presence can be exploited to achieve targeted flow conditions. The control of the vortex parameters is desirable in both cases. In this paper, we introduce an optimization strategy for the control of vortices in the wake of a bluff body. Flow modelling is based on RANS and DES computations, validated by experimental data. The algorithm for vortex identification and characterization is based on the triple decomposition of motion. It produces a quantitative measure of vortex strength which is used to define the objective function in the optimization procedure. It is shown how the shape of an aerodynamic device can be altered to achieve the desired characteristics of vortices in its wake. The studied case is closely related to flame holders for combustion applications, but the conceptual approach has a general applicability to vortex control.

Graph and convolutional neural network coupling with a high-performance large-eddy simulation solver

Computational Fluid Dynamics (CFD) traditionally relies on long-standing numerical simulation strategies for the Navier–Stokes equations. Recently, interest in data-driven hybrid CFD solvers has spiked, leveraging pre-computed datasets to enhance various weak links inside existing solvers, such as closure models, under-resolved physics, or even to guide numerical resolution strategies. Running these hybrid solvers, notably in High Performance Computing (HPC) environments, presents specific challenges. In particular, context-aware deep learning (e.g. Convolutional (CNN) and Graph (GNN) Neural Networks) is promising for this task, but requires passing data representations between the physics solver and the neural network. In relevant industrial configurations, CFD meshes can be Cartesian but highly irregular, or unstructured, both of which do not match the pixel/voxel structure needed to run CNNs. In addition, discrepancies in programming language and libraries are common between CFD and machine learning applications. This work explores the many challenges of running a parallel hybrid solver in an HPC context, through the coupling of the AVBP CFD solver with neural networks in turbulent combustion and wall friction modeling applications. The knowledge gained is showcased in this article, as well as assembled in an actionable open-source library.

New method for the preparation of hierarchical nanotube K0.3xMnxCe1-xOδ catalysts and their excellent catalytic performance for soot combustion

A series of nanofibrous K-Mn-Ce composite oxide catalysts were prepared via a centrifugal spinning method for the first time, and their physicochemical properties were studied. The prepared K0.3xMnxCe1-xOδ metal oxide catalyst, which is a composite of CeO2 and cryptopotassium manganese-type K2-xMn8O16, exhibits a hierarchical nanotube structure with uniform nanoneedles on its surface. During the process of soot oxidation, CeO2 utilizes its unique oxygen storage and release capacity to supply oxygen species. Simultaneously, the excellent redox properties of K2-xMn8O16 effectively promote valence cycling of the active components and produce more active oxygen species. This synergistic effect effectively enhances the intrinsic activity of the catalysts. Among the prepared catalysts, K0.15Mn0.5Ce0.5Oδ exhibits the highest catalytic performance, with 10% (T10), 50% (T50), and 90% (T90) soot conversion temperatures of 267 °C, 309 °C, and 338 °C, respectively, and shows good stability and water and sulfur resistance. Based on their simple preparation, low cost, and good catalytic performance, K0.3xMnxCe1-xOδ composite metal oxide catalysts have good potential in catalytic soot combustion applications.

Detonation properties and nitrogen oxide production in ammonia–hydrogen–air mixtures

Ammonia is a promising compound for chemical storage of renewable energy produced from non-continuous sources. However, the low reactivity of ammonia requires to use ammonia–hydrogen blends as a fuel for combustion applications. The present study corresponds to a first numerical assessment of the potential of ammonia–hydrogen–air mixtures as reactive mixtures for detonation engine applications. Both ideal and curved detonation models were employed to calculate the detonation properties, entropy production, and NOx production for mixtures with varying amounts of ammonia and hydrogen under a wide range of initial thermodynamics conditions. Interestingly, our calculations show that the entropy production and the amount of nitrogen oxides produced at the Chapman–Jouguet state respectively decreases and increases with the proportion of hydrogen in the ammonia–hydrogen blend. These aspects could have a great impact on engine efficiency and air pollution and should be considered with care. Our results also demonstrate that only mixtures with relatively low amounts of ammonia, i.e., XNH3 lower than 0.25 of the fuel blend, can be employed for detonation engine applications.

A co-doped strategy for simultaneously improving oxygen permeability and stability of BaCoO3-δ ceramic membrane

Perovskite-type oxygen transport membrane (OTM) has considerable potential in the application of oxygen-enriched combustion, which can prepare low-cost, low-energy, and high-purity oxygen. BaCoO3-δ-based OTM, as a promising candidate, possesses high oxygen permeability, but poor stability limits its application. In this study, the strategy of Y3+ and W6+ co-doping BaCo0·8Y0.2-xWxO3-δ (BCYW-x) was proposed firstly to control the trade-off effect between oxygen permeability and stability by considering the radius and valence of ions. Y3+ and W6+ co-doping stabilize the perovskite cubic structure to room temperature of the BCYW-x, and the average bond energy of metal-oxygen increases with W6+ doping, enhancing the structure stability. Although oxygen vacancy concentration and critical radius decrease, the migration of oxygen ions is accelerated with the increase of W6+ content at high temperatures. The BCYW-0.1 shows the exceptional oxygen permeation flux of 1.90 mL･cm−2･min−1 at 900 °C and stably operates at 900 °C and 800 °C. Furthermore, the kinetic model calculation indicates that the permeation resistance decreases because of the acceleration of the oxygen surface exchange reaction as W6+ doping, contributing to the oxygen permeation process. This study provides a feasible and effective co-doping strategy to improve the oxygen permeability and stability of perovskite OTMs simultaneously.