Abstract
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.
Highlights
Burner systems have been widely used in industries and domestic areas for steam production and hot water supply [1,2,3]
The flame images can immediately reflect the current status of the combustors and so they are crucial in perspective of the combustion control because of decreasing structural dead time of the combustion process inasmuch as the dead time affects adversely disturbance rejection performance of closed loop control systems [5,6,7,8]
The temperature value (Tflue) measuring from the flue and trace value of image source matrix which is filtered from high frequency dynamics by means of a low pass filter are simultaneously used in an artificial neural network (ANN)
Summary
Burner systems have been widely used in industries and domestic areas for steam production and hot water supply [1,2,3]. This paper represents a basic and effective estimation system It is analyzed the correlation between air excess coefficient (λ) and the filtered characteristic parameters of flame image captured from the nut coal burning chamber by means of a CCD camera. The regression analyses have performed on eleven different flame characteristic features by means of the matrix approach These parameters have evaluated together with flue temperature. The temperature value (Tflue) measuring from the flue and trace value of image source matrix which is filtered from high frequency dynamics by means of a low pass filter are simultaneously used in an artificial neural network (ANN).
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