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In this article, the effects of mixture dilution using EGR or excessive air on adiabatic flame temperature, laminar flame speed, and minimum ignition energy are studied to illustrate the fundamental benefits of lean combustion. An ignition system developing a new active pre-chamber (APC) design was assessed, aimed at improving the indicated thermal efficiency (ITE) of a 1.5 L four-cylinder gasoline direct injection (GDI) engine. The engine combustion process was simulated with the SAGE detailed chemistry model within the CONVERGE CFD tool, assuming the primary reference fuel (PRF) to be a volumetric mixture of 93% iso-octane and 7% n-heptane. The effects of design parameters, such as APC volume, nozzle diameter, and nozzle orientations, on ITE were studied. It was found that the ignition jet velocity from the pre-chamber to the main chamber had a significant impact on the boundary heat losses and combustion phasing. The simulation showed that, under 16.46 compression ratio (CR) and 8.93 bar indicated mean effective pressure (IMEP) condition, it is possible to achieve the peak ITE of 49.85% with λ = 2.23.

The flame temperature distribution and stability of the oil burner are analysed from three aspects: without considering the influence of the sample, considering the influence of the sample, and the back temperature of the sample. Common metal and non-metal materials were used in the tests. Common problems in the tests were solved.

Integrating hydrogen with CNG is crucial for carbon neutrality and environmental goals, as it enhances flame temperature, reduces emissions, and combats global warming. This study employs the CHEMKIN tool to examine combustion characteristics, including adiabatic flame temperature, mole fraction, normalization, and production rate, in H 2 -CNG mixtures under various atmospheric and operating conditions. Blending 50% hydrogen with CNG results in significant changes, including a temperature increase from 2322 to 2344 K when the hydrogen content is at 50%. The introduction of hydrogen causes a notable 30–35% reduction in CH 4 mole fraction and a simultaneous 26.6% increase in C-normalized CH 4 production. Free radicals play a role in affecting CO 2 production, with the normalization of CO species increasing from 0.068 to 0.087. Through NSGA-II multi-objective optimization methods, the study identifies a 50% H 2 -50% CNG blend as the optimal choice for thermal and environmental performance. The study explores the energy and environmental impacts of incorporating hydrogen into CNG-air combustion, with a specific focus on the effects of 50% H 2 blending with CNG. Hydrogen blending benefits from elevated adiabatic flame temperature and increased free radical formation, ultimately leading to emission reduction. These findings firmly establish H 2 -CNG mixtures as promising environmentally friendly alternatives with superior combustion characteristics. Their potential paves the way for significant progress towards achieving carbon neutrality and combating climate change through cleaner, more efficient fuel options.

Flame temperature field measurement has always been a key topic in combustion research, which is of great significance for combustion state diagnosis and fuel combustion optimization. Deep learning technology exploits its superior nonlinear ability to rapidly reconstruct the three-dimensional (3D) temperature field of flames from flame light-field images. However, existing algorithms have problems with complex networks, poor noise resistance and low reconstruction accuracy. This paper proposes a lightweight 3D flame temperature field reconstruction algorithm based on an improved MobileNet that combines residuals and spatial attention mechanisms. This method basically achieves a balance between low complexity and high accuracy and has good noise resistance performance. Simulation results show that the average relative error of temperature field reconstruction on the noise-free unimodal flame dataset is only 0.022%, and its computational complexity is only one-tenth of the existing CNN. The noise simulation experiment shows that this method has good noise resistance performance. The maximum relative error and the average relative error at Gaussian white noise standard deviation σ = 0.15 are only 2.91% and 0.27%, respectively.

The steel industry is an important foundation of the national economy and the livelihood of the people, producing a large amount of carbon dioxide gas, accounting for about 70% of the carbon dioxide gas generated in the steel industry, which occurs during the ironmaking process. Therefore, the key technology to reduce the pollution and improve competitiveness is to increase the stability of blast furnace production and the quality of hot metal. Since the operation requirements for temperature control in the vanadium-titanium blast furnace are dramatically different compared to the traditional ones due to the low fluidity of vanadium-titanium slag, maintaining the required hot metal temperature within a narrow range with smaller fluctuations is essential. In addition, the adjustment parameters of the lower part have a significant influence on the tuyere combustion flame temperature during the daily operation of blast furnaces. At present, there is no relevant research on the online detection and analysis of vanadium-titanium blast furnace tuyere combustion flame temperature. In this study, the temperature of four tuyeres in a 500 m3 vanadium and titanium blast furnace at Jianlong Steel was detected by an online detection system. The tuyere combustion flame temperature was then calculated using colorimetric temperature measuring methodology at various times and at four distinct locations. After that, the calibration analyses, imaging parameter and the temperature tendencies in different directions of the blast furnace were investigated. This study not only offers new methods for understanding the regularity of operation and increasing the degree of visualization in vanadium and titanium smelting blast furnaces but also provides technical support for intelligent and low-carbon operation in blast furnaces.

Flame temperature and spectral emissivity were the important parameters characterizing the sufficient degree of fuel combustion and the particle radiative characteristics in the Rocket Based Combined Cycle (RBCC) combustor. To investigate the combustion characteristics of the complex supersonic flame in the RBCC combustor, a new radiation thermometry combined with Levenberg-Marquardt (LM) algorithm and the least squares method was proposed to measure the temperature, emissivity and spectral radiative properties based on the flame emission spectrum. In-situ measurements of the flame temperature, emissivity and spectral radiative properties were carried out in the RBCC direct-connected test bench with laser-induced plasma combustion enhancement (LIPCE) and without LIPCE. The flame average temperatures at fuel global equivalence ratio (α) of 1.0b and 0.6 with LIPCE were 4.51% and 2.08% higher than those without LIPCE. The flame combustion oscillation of kerosene tended to be stable in the recirculation zone of cavity with the thermal and chemical effects of laser induced plasma. The differences of flame temperature at α = 1.0b and 0.6 were 503 K and 523 K with LIPCE, which were 20.07% and 42.64% lower than those without LIPCE. The flame emissivity with methane assisted ignition was 80.46% lower than that without methane assisted ignition, due to the carbon-hydrogen ratio of kerosene was higher than that of methane. The spectral emissivities at 600 nm with LIPCE were 1.25%, 22.2%, and 4.22% lower than those without LIPCE at α = 1.0a (with methane assisted ignition), 1.0b (without methane assisted ignition) and 0.6. The effect of concentration in the emissivity was removed by normalization to analyze the flame radiative properties in the RBCC combustor chamber. The maximum differences of flame normalized emissivity were 50.91% without LIPCE and 27.53% with LIPCE. The flame radiative properties were stabilized under the thermal and chemical effects of laser induced plasma at α = 0.6.

Numerical analysis using CFD facilitates the problem of investigating lean methane-air mixtures on the Pt-coated catalytic walls, involving homogeneous gaseous species and heterogeneous catalytic surface reactions. Parametric studies on the inlet velocity and temperature alter the location of the commencement of surface reactions, and as a result, the prudent selection of appropriate flow configurations in the catalytic combustion process can be determined. The results show that the temperature is regulated by the stable combustion process attaining an adiabatic flame temperature, while on the other hand, immediately after the occurrence of flame ignition, sharp rises of gaseous species are observed, proving that the gas reactions are induced by the catalytic surface activity. In addition, this work analogized the Navier-Stokes model with the 1-D plug-flow reactor model to reveal the range of validity of various approximations.

The aim of this study is to perform the effect of coffee waste blended in bio-briquette as an alternative fuel for domestic coffee stove. The sample of bio-briquette were blended with a variation of composition coffee waste, coal and polyvinyl acetate (PVAc) glue as a binder. The sample of bio-briquette was introduced in electric furnace with combustion atmosphere by using of 20% excess air condition. In the experiment, the mass reduction fraction, combustion rate, flame temperature of burning bio-briquettes was elucidated. The results obtained shows there are influenced of mass decreasing with an increasing of coffee waste blends, where the more of composition coffee wastes was added causes the faster of mass decreasing fraction of bio-briquette. The rate of combustion (K) increases with increasing percentage of coffee waste, where the largest K is obtained 6.05 (1 / second) in the composition of 90% coffee waste, while the smallest K of 4.55 (1 / second) is obtained in the composition of 30% coffee waste. The highest flame temperature occurs in the mixture of coffee waste 90% which is equal to 580 ° C and the lowest fire temperature occurs in the mixture of coffee waste 30% that is equal to 410 ° C.

The effects of glycine-to-nitrate molar ratio ( G / N ) and sintering temperature of 600 °C on the solution combustion synthesis of nanocrystalline Co 0.8 Mg 0.2 Fe 2 O 4 (CMFO) are reported. The structural, morphological and magnetic properties of CMFO could be controlled by using different combinations of glycine fuel and metal nitrates and also sintering temperature. Thermodynamic considerations of the combustion processes show that the exothermicity, adiabatic flame temperature and the amount of gases released increase with increase in G / N . The auto-combusted and sintered powders obtained were characterized by X-ray diffraction, Raman spectroscopy, scanning electron microscopy (SEM), thermo-gravimetric analysis–differential scanning calorimetry and vibrating scanning magnetometer measurements. SEM images of CMFO showed that the G / N ratio and sintering temperature had pronounced effect on the microstructure regarding density and porosity. The magnetization, crystallite sizes and crystallinity of the CMFO spinel phase increased with increase in G / N ratio and sintering temperature. Raman spectroscopic analysis showed that only the fuel-rich sample gave the five Raman active modes characteristic of a spinel structure. The obtained results were also discussed in comparison with ferrite system formed by different synthesis processes. Graphical Abstract XRD of CMFO auto-combustion powders prepared with different G/N ratios: (a) G/N = 2.22, (b) G/N = 1.48 and (c) G/N = 0.74.

The raceway adiabatic flame temperature (RAFT) is the basis for judging the thermal state of the hearth and an important parameter for the blast furnace (BF) operation. However, the traditional model fails to accurately characterize the actual RAFT suitable for H 2 -rich gas injection BF. In this study, a RAFT heat balance model suitable for BF with injection of H 2 -rich gas (shale gas, coke oven gas and H 2 ) was optimized. The influences of the H 2 concentrations in tuyere gases, O 2 enrichment ratio, pulverized coal injection (PCI) quantity and blast humidity on RAFT were calculated and the mathematical formula was set up through multiple linear regression. The results show that with the injection rate of coke oven gas, H 2 and shale gas, the RAFT decreases at a rate of 10.4 ℃ per kg, 14.7 ℃ per kg and 5.92 ℃ per kg, respectively. In addition, RAFT increases with the increase of oxygen enrichment ratio, while decreases with the increase of PCI quantity and blast humidity. Changing the oxygen enrichment ratio, PCI quantity and blast humidity can modulate RAFT when the H 2 -rich gas is injected into BF. This work provides a reference for the H 2 -rich gas injection BF. Graphical Abstract