Combustion stability analysis for non-standard low-calorific gases: Blast furnace gas and coke oven gas

Zhang, Long; Xie, Wenwen; Ren, Zhuyin

doi:10.1016/j.fuel.2020.118216

Cited by 25 publications

(17 citation statements)

References 34 publications

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“…Following the work of [4,16], we employ a non-adiabatic unsteady PSR to reveal the complex dynamics exhibited in industrial hot air heaters. The chemical kinetic model employed is DRM22, a reduced version of GRIMECH 1.2 obtained by Kazakov et al [17], which consists of 22 species and 104 reactions.…”

Section: Governing Equations For the Model Combustormentioning

confidence: 99%

“…The hot air heater in this study operates under constant atmospheric pressure. The heater chamber is cylindrical with a constant volume of 13.82 m 3 as detailed in [4]. The mass flow rate of the inlet mixture m a is 2.75 kg/s, and the residence time τ res is calculated according to Eq.…”

Section: Governing Equations For the Model Combustormentioning

confidence: 99%

“…3(a), the volume fraction of COG2 at the inlet transitions from 0 to 1 to represent the fuel switching process from COG1 to COG2 in an industrial combustor. As detailed in [4], the burning of COG fuels will show instability from mixing-chemistry interaction, with an oscillation frequency of around 20 Hz. Therefore, the simulation time range here is chosen to be [0, 0.5s] with 100 data points in between, and the fuel switch process occurs between 0.15 s and 0.35 s. Such time interval in the dataset is sufficiently small to resolve the oscillation inside the combustor.…”

Section: Inverse Modeling For Fuel Switchingmentioning

confidence: 99%

“…Industrial combustion devices such as hot air heaters are widely used for burning non-standard low-calorific value (NLCV) gases, such as blast furnace gas and coke oven gas, which are prone to combustion instability. Zhang et al [4] studied the oscillating combustion in reactors resulting from periodic fuel burnt out, fuel replenishment, and ignition governed by the subtle mixingchemical kinetics interaction or due to the inlet flow rate fluctuation under industrial conditions. Zhang et al utilized forward modeling to investigate the oscillation of combustion via unsteady PSR simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Typical fuel compositions of coke oven gas (COG) and blast furnace gas (BFG) used (volume fraction)[4] …”

mentioning

confidence: 99%

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Neural Differential Equations for Inverse Modeling in Model Combustors

Zhang

et al. 2021

Preprint

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Monitoring the dynamics processes in combustors is crucial for safe and efficient operations. However, in practice, only limited data can be obtained due to limitations in the measurable quantities, visualization window, and temporal resolution. This work proposes an approach based on neural differential equations to approximate the unknown quantities from available sparse measurements. The approach tackles the challenges of nonlinearity and the curse of dimensionality in inverse modeling by representing the dynamic signal using neural network models. In addition, we augment physical models for combustion with neural differential equations to enable learning from sparse measurements. We demonstrated the inverse modeling approach in a model combustor system by simulating the oscillation of an industrial combustor with a perfectly stirred reactor. Given the sparse measurements of the temperature inside the combustor, upstream fluctuations in compositions and/or flow rates can be inferred. Various types of fluctuations in the upstream, as well as the responses in the combustor, were synthesized to train and validate the algorithm. The results demonstrated that the approach can efficiently and accurately infer the dynamics of the unknown

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Section: Governing Equations For the Model Combustormentioning

confidence: 99%

Section: Governing Equations For the Model Combustormentioning

confidence: 99%

Section: Inverse Modeling For Fuel Switchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Typical fuel compositions of coke oven gas (COG) and blast furnace gas (BFG) used (volume fraction)[4] …”

mentioning

confidence: 99%

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Neural Differential Equations for Inverse Modeling in Model Combustors

Zhang

et al. 2021

Preprint

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Colourless distributed combustion effects on a pre‐mixed coke oven gas flame

Tasdemir,

Karyeyen

2023

Can J Chem Eng

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Fuels consisting of high hydrogen concentration can be consumed under colourless distributed combustion (CDC) to suppress flashback tendency in premixed conditions. Higher NOX challenges can also be overcome through CDC. For those purposes, coke oven gas was consumed under CDC within the scope of this study. To achieve CDC, or diluents were introduced into the oxidizer so that oxygen concentration in the oxidizer would be decreased from 21% O2 to its lean blow‐off limits. Excess air ratios were determined as λ = 1.2 and λ = 1.5 along with a thermal power of 10 kW under premixed conditions. A commercial computational fluid dynamics code was used to predict temperature distribution and , CO, and emissions. 162‐step reactions created with GRI‐Mech 3.0 chemical kinetics was integrated to the eddy dissipation concept combustion model. The temperature and profiles predicted were compared with the experimental results. Consistency was achieved at ~95% for temperature profiles, and almost 100% for profiles between the measured and the predicted ones. According to the results, it is concluded that a more homogeneous temperature distribution with the ~21% decrease in the maximum temperature was observed, and there was about 96% decrease in level. It was also demonstrated that using as a diluent is more effective in terms of temperature distribution over the combustor and reduction, while dilution with is more effective in terms about 70% in decrease on CO.

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A novel modeling strategy for the prediction on the concentration of H2 and CH4 in raw coke oven gas

Lei

Chen

et al. 2023

Energy

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Combustion stability analysis for non-standard low-calorific gases: Blast furnace gas and coke oven gas

Cited by 25 publications

References 34 publications

Neural Differential Equations for Inverse Modeling in Model Combustors

Neural Differential Equations for Inverse Modeling in Model Combustors

Colourless distributed combustion effects on a pre‐mixed coke oven gas flame

A novel modeling strategy for the prediction on the concentration of H2 and CH4 in raw coke oven gas

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