Principles of optimization of combustion by radiant energy signal and its application in a 660 MWe down- and coal-fired boiler

Luo, Zixue; Wang, Fei; Zhou, Huaichun; Liu, Rutie; Li, Wenchang; Chang, Gengzhou

doi:10.1007/s11814-011-0098-1

Cited by 34 publications

(17 citation statements)

References 20 publications

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“…NO x is one of the main pollutants from coal-fired power plants, and is also a contributor to global warming [24]. Combustion optimization technology has been demonstrated as an effective and economic method to the reduction of NO x emissions from coal-fired boilers by tuning adjustable parameters of a boiler, such as coal feed quantity, excess air ratio, and secondary air etc [25]. Such optimization of operational parameters relies heavily on an accurate correlation between NO x emissions and operational parameters.…”

Section: Application To No X Emissionsmentioning

confidence: 99%

A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

Dong

2019

IEEE Access

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This paper proposes a novel nonlinear partial least square (PLS) approach for dealing with the modeling problem of industrial processes with input variables in collinearity. The new method combines the external linear PLS framework with the internal extreme learning machine (ELM) function. First, PLS is used as the outer framework to extract the input and output latent variables as well as eliminating the collinearity of the original variables, and then ELM is employed to describe the nonlinear relation between pairs of latent variables. Besides, the weight updating strategy based on errors minimization is also involved to improve the prediction accuracy. Then, the pH-neutralization process is taken as a benchmark to verify the validity of the new model. Finally, this method is applied to model the NO x emission of a 1000-MW coal-fired boiler, and root-mean-square error (RMSE) values are 5.9541 for the training dataset and 6.8323 for the testing dataset. Compared with linear PLS and another two nonlinear PLS methods, smaller prediction errors is obtained. The results indicate new nonlinear PLS model can be a better choice for establishing the model of NO x emission for coal-fired boilers. INDEX TERMS Nonlinear partial least square, extreme learn machine, weight update, NO x emission, coal-fired boilers.

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Section: Application To No X Emissionsmentioning

confidence: 99%

A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

Dong

2019

IEEE Access

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“…In studies of flame image processing, radiant energy signal (RES) is used frequently [15][16][17][18][19][20][21][22]. RES is one of the important exhaustive parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers explained the flame temperature distribution and the RES are sensitive to change in the combustion of the boiler [19]. Also RES and combustion rate can be associated [20]. The relationship between fuel consumption rate and heat absorption rate can be achieved with RES [21].…”

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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“…There are four main types of down‐fired combustion technologies, as developed by Mitsui Babcock Energy, Ltd., Babcock & Wilcox, Foster Wheeler, and Stein. However, the introduced down‐fired boilers tend to function poorly when burning this type of coal in China, resulting in asymmetric combustion, late ignition, severe slagging, unstable combustion, insufficient burnout (typically with the unburnt carbon in fly ash of approximately 10%) and high NO x emission levels (around 1500 mg/m 3 at 6% O 2 ). Various solutions were proposed to solve these problems, such as adjusting the burner location and furnace parameters to avoid asymmetric combustion, changing burner operation models to mitigate slagging, retrofitting the combustion configuration to enhance the ignition and burnout, and adjusting air distribution to reduce NO x emissions.…”

Section: Introductionmentioning

confidence: 99%

Experimental‐ and numerical‐simulation research on the inner–secondary‐air ratio in a 600‐MW _e down‐fired boiler

et al. 2019

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Summary Using a phase Doppler‐anemometer measurement system, the cold gas/particle‐airflow behavior in a 1:40 scale‐model furnace was assessed to study the influences of adjusting the inner–secondary‐air ratio in a 600‐MWe multi‐injection and multistaging down‐fired boiler. Numerical simulations were also conducted to verify the results of the modeling trials and to provide heat‐state information. The results demonstrate that reducing the inner–secondary‐air ratio from 19.66% to 7.66% gradually enhances the downward velocity decay of the gas/particle airflow, while the inner secondary‐air downward‐entraining effect on the fuel‐rich flow is weakened. Lowering the inner–secondary‐air ratio greatly inhibits the decay of the near burner–particle volume flux. In addition, the fuel rich–flow ignition distance is reduced, from 1.02 to 0.87 m. A lower inner–secondary‐air ratio is harmful to restrain early NOx formation. Reducing the ratio also causes the fuel‐rich flow to turn upwards ahead, while the penetration depth of this flow gradually decreases and the maximum temperature in the hopper region falls from 1900 to 1800 K. On the basis of these data, an optimal inner–secondary‐air ratio of 13.66% is recommended.

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Principles of optimization of combustion by radiant energy signal and its application in a 660 MWe down- and coal-fired boiler

Cited by 34 publications

References 20 publications

A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

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

Experimental‐ and numerical‐simulation research on the inner–secondary‐air ratio in a 600‐MW _e down‐fired boiler

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Principles of optimization of combustion by radiant energy signal and its application in a 660 MWe down- and coal-fired boiler

Cited by 34 publications

References 20 publications

A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

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

Experimental‐ and numerical‐simulation research on the inner–secondary‐air ratio in a 600‐MW e down‐fired boiler

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Experimental‐ and numerical‐simulation research on the inner–secondary‐air ratio in a 600‐MW _e down‐fired boiler