Flame stability measurement through image moments and texture analysis

Abstract

In this article, the first two moments of the image, mean and standard deviation, uniform Local Binary Pattern (LBP) texture analysis methods were experimentally investigated in coal-fired boilers to measure flame stability. The first two moments of the flame image were used to evaluate the flame stability in terms of color and brightness (average gray value). Although the radiation signal of the flame is widely obtained by the spectral analysis method, the radiation signal of the flame was obtained by the LBP texture analysis method in this study. The flame stability measurement technique proposed in this study does not require prior knowledge about CCD camera features. Therefore, it can be easily applied to measure flame stability without expensive and complicated adaptation processes. Flame stability was measured with R = 0.9868 accuracy with the proposed method. The experimental results show that the proposed texture analysis method is more effective than current spectral analysis methods. The results obtained within the scope of this study also show that it can be easily applied to existing closed-loop control systems to monitor flame stability.

A boiler oxygen content and furnace temperature prediction model based on honey badger algorithm optimized neural network

“…x new is the updated individual location of the honey badger; x prey is the prey location; and r 7 is a random number between (0, 1) as determined by the above equation, respectively. It can be observed from Eq (9). that based on the distance information, the honey badger searches in the vicinity of the prey location.…”

“…In recent years, neural networks have been widely used in a variety of fields due to their ability to fit arbitrary continuous functions and their strong nonlinear mapping capabilities [7][8][9]. Moreover, Golgiyaz et al also proposed an artificial neural network prediction model for predicting flue gas temperature using flame images spectra, which is able to better analyze the correlation between the flame image and the reference temperature and emissions of the flue gas content through the features of the flame image and analyze the reference temperature and emissions of the flue gas content [10].…”