Flame Combustion Stability Research Articles

A Fuzzy Synthetic Evaluation Method of Flame Stability Based on Time–Frequency Analysis and Higher-Order Statistics

The flame combustion processes involves chemical reactions and therefore flame stability is difficult to accurately assess. Based on flame radiation measuring parameters, a new synthetic evaluation system of flame combustion stability is established. A series of combustion conditions with various fuel/air ratios is investigated. Flame radiation luminance fluctuating information is acquired on a low-cost flame detection device. Power spectrum and bi-spectral information of the phase domain are derived from time domain signals based on Fourier transform and higher order statistics based upon a de-noising algorithm. The time–frequency characteristics and the features of the bi-spectrum under various combustion conditions are qualitatively analyzed, and the simultaneous descriptive parameters from time, frequency, and phase domain are extracted. A theoretical model for comprehensive fuzzy evaluation has been constructed, and also an index system has been established. It is demonstrated that this judgment system is reasonable and effective. The results can be used as an analyzing tool for process engineers for improving combustion conditions.

Industrial Flame Edge Detection Algorithm Based on Gray Dominant Filter

Industrial furnace flame detection is directly related to the safety and economic operation of boiler. The flame edge detection is one of the most key parts of the flame detection. At present, there are many edge detection algorithms for normal object but few for industrial flame edge detection which cannot provide fully support for furnace flame combustion stability, flame 3D reconstruction and flame temperature field reconstruction. One new industrial flame image edge detection algorithm based on industrial flame image characteristics is proposed in this paper. This algorithm can effectively remove noise level in flame image and well maintain flame image edge features, compute the gradients from multiple-directions for entirely detecting flame edge information. Moreover, this algorithm is verified by experiment in this paper to present high performance in resisting noise, and clearly see continuous flame edge curve, which reveals better effect in industrial flame edge detection than other edge detection algorithms.

Research on Flame Combustion Stability Based on Neural Network

For the use of boiler flame image analysis to detect the boiler flame combustion stability, when the combustion affected by coal, peaking , improper operation or other effects, the flame appeared short pulsation. In general, the traditional detection methods based on gray scale variance can not avoid the impact of flame pulsation on account of the inaccuracy of the boiler combustion stability detection. This paper presents a flame combustion instability detection method based on neural network and selects multiple features which are directly related to the flame stability as neural network input vector. Experiments show that this method can fight off the tiny ripple influence caused by the impurities combustion or peak and simultaneously, greatly improve the detection accuracy and stability.

The Identification of the Flame Combustion Stability Based on the Mahalanobis Distance

The identification of stability about flame combustion plays a vital role in the industrial production of plants. Paper integrates the phase information into identification system extracted by higher order spectral, and stresses on identifying the stability of flame combustion with the application of the Mahalanobis Distance, combined with the other two characteristic quantities: the flame intensity variance and flicker frequency. The Mahalanobis Distance can overcome the interference correlation of the flame characteristic quantities and the impact of different dimensions, so the identification rate reaches to 93%, which effectively verifies the correctness of the Mahalanobis Distance.