An experimental study on high temperature and low oxygen air combustion

Kim, W. B.; Chung, Dae Hun; Yang, Je Bok; Noh, Dong Soon

doi:10.1007/bf02698273

Cited by 7 publications

(4 citation statements)

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“…The visibility of the flames decreases when the oxygen concentration in the combustion air is decreased. A unique flame structure is displayed when the air preheat temperature increases and the oxygen concentration in the combustion air is decreased from 21% to 2%; for instance, the color changes from bright white/yellow to blue/green/yellow . The visibility of the flames depends on the diluents, and the intermediate species does not only depend on fuel type .…”

Section: Reduction In Thermal Nox During Flameless Combustionmentioning

confidence: 99%

Flameless combustion role in the mitigation of NO_Xemission: a review

Abuelnuor

Wahid

Mohammed

et al. 2014

Int. J. Energy Res.

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SUMMARYNitrogen oxide formation is one source of pollution emission in fossil fuel combustion. Numerous technologies, such as flameless combustion, have been introduced to prevent such formation. This paper presents a panorama of flameless combustion and its principles. The influence of this technology on air preheats and gas diluents is discussed, as well as the effect of the firing mode on thermal NO X . The benefits of flameless combustion, including its energy conservation capability and capability to reduce pollutant emissions such as NO X emission to overcome environmental dilemmas in combustion, are discussed in this paper.

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Section: Reduction In Thermal Nox During Flameless Combustionmentioning

confidence: 99%

Flameless combustion role in the mitigation of NO_Xemission: a review

Abuelnuor

Wahid

Mohammed

et al. 2014

Int. J. Energy Res.

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“…Experiments are carried out at a 360 MW pulverized coal-fired boiler with W-type flame [23,24]. Fig.…”

Section: Methodsmentioning

confidence: 99%

Application of gain scheduling for modeling the nonlinear dynamic characteristics of NO x emissions from utility boilers

Kong

Ding

Zhang

et al. 2009

Korean J. Chem. Eng.

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A hierarchical gain scheduling (HGS) approach is proposed to model the nonlinear dynamics of NO x emissions of a utility boiler. At the lower level of HGS, a nonlinear static model is used to schedule the static parameters of local linear dynamic models (LDMs), such as static gains and static operating conditions. According to upper level scheduling variables, a multi-model method is used to calculate the predictive output based on lower-level LDMs. Both static and dynamic experiments are carried out at a 360 MW pulverized coal-fired boiler. Based on these data, a nonlinear static model using artificial neural network (ANN) and a series of linear dynamic models are obtained. Then, the performance of the HGS model is compared to the common multi-model in predicting NO x emissions, and experimental results indicate that the proposed HGS model is much better than the multi-model in predicting NO x emissions in the dynamic process.

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“…We applied an orthogonal experiment design method to carry out the experiments on a 360 MW coal fired boiler which adopted a W-flame furnace [16][17][18]. There are 36 direct burners installed in the front and rear arch which eject to the centre of the furnace.…”

Section: Experiments and Data Collectionmentioning

confidence: 99%

Modeling and coordinative optimization of NO x emission and efficiency of utility boilers with neural network

Zhang

Ding

et al. 2007

Korean J. Chem. Eng.

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An empirical model to predict the boiler efficiency and pollutant emissions was developed with artificial neural networks based on the experimental data on a 360 MW W-flame coal fired boiler. The temperature of the furnace was selected as an intermediate variable in the hybrid model so that the predictive precision of NO x emissions was enhanced. The predictive precision of the hybrid model was improved compared with the direct model. Three optimal operational objects were proposed in order to minimize the fuel and environmental costs. Based on the neural network model and optimal objects, a genetic algorithm was employed to seek real-time solution every 30 seconds. Optimum manipulated variables such as excess air, primary air and secondary air were obtained under different optimal objects. The above algorithm interconnected with a distributed control system (DCS) formed the supervisory control and achieved real-time coordinated optimization control of utility boilers.

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An experimental study on high temperature and low oxygen air combustion

Cited by 7 publications

References 0 publications

Flameless combustion role in the mitigation of NO_Xemission: a review

Flameless combustion role in the mitigation of NO_Xemission: a review

Application of gain scheduling for modeling the nonlinear dynamic characteristics of NO x emissions from utility boilers

Modeling and coordinative optimization of NO x emission and efficiency of utility boilers with neural network

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An experimental study on high temperature and low oxygen air combustion

Cited by 7 publications

References 0 publications

Flameless combustion role in the mitigation of NOXemission: a review

Flameless combustion role in the mitigation of NOXemission: a review

Application of gain scheduling for modeling the nonlinear dynamic characteristics of NO x emissions from utility boilers

Modeling and coordinative optimization of NO x emission and efficiency of utility boilers with neural network

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Flameless combustion role in the mitigation of NO_Xemission: a review

Flameless combustion role in the mitigation of NO_Xemission: a review