Prediction of equivalence ratio in pulse combustor from ion current amplitude spectrum

Li, Fangyan; Cao, Zhang; Xu, Lijun; Xie, Yixin

doi:10.1016/j.fuel.2018.01.004

Cited by 17 publications

(6 citation statements)

References 28 publications

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“…6 (a-1)), other peaks at 135 Hz, 405 Hz, and 540 Hz appeared. These were the so called sub-and super-harmonics of the signal, which were not observed in the passively valved RPC of Li et al [43,44]. However, no outstanding harmonic components were seen in the pressure signal results (Fig.…”

Section: Accepted Manuscript N O T C O P Y E D I T E Dmentioning

confidence: 82%

“…The stability of resonant pulse combustion has also been investigated. Li et al [43,44] studied the stability properties of a passively valved RPC with gaseous fuel by measuring the ion current and the dynamic pressure signal produced by combustion heat release. They observed that low frequency oscillations appeared, and dominated the combustion driven resonance, when the blowout limit was approached.…”

Section: Accepted Manuscript N O T C O P Y E D I T E Dmentioning

confidence: 99%

“…The homemade ion sensor is deemed to have the similar uncertainty with that in Ref. [43,44]. A detailed description of the instrumentation system is found in [45,46].…”

Section: Apparatus and Instrumentationmentioning

confidence: 99%

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Stability Characteristics of an Actively Valved Resonant Pulse Combustor

et al. 2021

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Section: Accepted Manuscript N O T C O P Y E D I T E Dmentioning

confidence: 82%

Section: Accepted Manuscript N O T C O P Y E D I T E Dmentioning

confidence: 99%

See 1 more Smart Citation

Stability Characteristics of an Actively Valved Resonant Pulse Combustor

et al. 2021

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“…These conditions are appropriate fuel air ratio, suitable turbulence, suitable temperature and sufficient time period. The combustion process is extremely complicated, because combustion condition is very changeable [5]. When it changes, the fuel air ratio will deviate from the optimal value; correspondingly, the combustion system will deviate from the optimal state.…”

Section: Introductionmentioning

confidence: 99%

Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network

Golgiyaz

Talu

Daskin

et al. 2022

Alexandria Engineering Journal

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It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient (k) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, k data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of featureÀk.

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“…In recent years, engineers have a motivation for improving combustion efficiency due to higher fuel costs and occasionally limited fuel availability. The combustion process is extremely complicated, because combustion condition is very changeable [4]. When it changes, the excess air will deviate from the optimal value; correspondingly, the combustion system will deviate from the optimal state.…”

Section: Introductionmentioning

confidence: 99%

A Basic ANN System for Prediction of Excess Air Coefficient on Coal Burners Equipped with a CCD Camera

Onat¹,

Daskin²

2019

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Excess air coefficient (λ) is the most important parameter characterizing the combustion efficiency. Conventional measurement of λ is practiced by way of the flue analyze device with high market priced. Estimating of the λ from flame images is crucial in perspective of the combustion control because of decreasing structural dead time of the combustion process. Beside, estimation systems can be used continuously in a closed loop control system, unlike conventional analyzers. This paper represents a basic λ prediction system with a neural network for small scale nut coal burner equipped with a CCD camera. The proposed estimation system has two inputs. First input is stack gas temperature simply measuring from the flue. To choose the second input, eleven different matrix parameters have been evaluated together with flue gas temperature values and performed by matrix-based multiple linear regression analysis. As a result of these analyses, it has been seen that the trace of image matrix obtained from the flame image provides higher accuracy than the other matrix parameters. This instantaneous trace value of image source matrix is then filtered from high frequency dynamics by means of a low pass filter. Experimental data of the inputs and λ are synchronously matched by a neural network. Trained algorithm has reached R=0.984 in terms of accuracy. It is seen from the result that proposed estimating system using flame image with assistance of the stack gas temperature can be preferred in combustion control systems.

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Prediction of equivalence ratio in pulse combustor from ion current amplitude spectrum

Cited by 17 publications

References 28 publications

Stability Characteristics of an Actively Valved Resonant Pulse Combustor

Stability Characteristics of an Actively Valved Resonant Pulse Combustor

Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network

A Basic ANN System for Prediction of Excess Air Coefficient on Coal Burners Equipped with a CCD Camera

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