ANALIZA SYGNAŁÓW PULSACJI PŁOMIENIA Z WYKORZYSTANIEM KRÓTKOCZASOWEJ TRANSFORMATY FOURIERA

The main aim of the diagnostics of combustion process is ensuring its stability and efficiency. The most important aspect related to the monitoring of the combustion process is a non-invasive acquisition of information from flame and subsequently subjecting it for further processing. Such method of research allows to  evaluate the course of the process and determine the characteristic conditions under which the combustion process is stable or not. The article presents the application of short-time Fourier transform for the analysis of flame pulsation signals. The aim of the research was to find an area especially sensitive to the change of combustion process conditions.Głównym celem stawianym diagnostyce procesu spalania jest zapewnienie stabilności i efektywności przebiegu procesu. Najważniejszym aspektem monitorowania procesu spalania jest pozyskiwanie w sposób bezinwazyjny informacji z płomienia, a następnie poddanie jej dalszemu przetwarzaniu. Taki sposób badań pozwala na ocenę przebiegu procesu i daje możliwość wyznaczania charakterystycznych stanów, w których proces przebiega stabilnie lub nie. W artykule przedstawiono wykorzystanie krótkoczasowej transformaty Fouriera do analizy sygnałów pulsacji płomienia. Celem badań było znalezienie obszaru szczególnie wrażliwego na zmianę warunków w procesie spalania

Multi-mode Combustion Process Monitoring on a Pulverised Fuel Combustion Test Facility based on Flame Imaging and Random Weight Network Techniques

Combustion systems need to be operated under a range of different conditions to meet fluctuating energy demands. Reliable monitoring of the combustion process is crucial for combustion control and optimisation under such variable conditions. In this paper, a monitoring method for variable combustion conditions is proposed by combining digital imaging, PCA-RWN (Principal Component Analysis and Random Weight Network) techniques. Based on flame images acquired using a digital imaging system, the mean intensity values of RGB (Red, Green, and Blue) image components and texture descriptors computed based on the grey-level co-occurrence matrix are used as the colour and texture features of flame images. These features are treated as the input variables of the proposed PCA-RWN model for multi-mode process monitoring. In the proposed model, the PCA is used to extract the principal component features of input vectors. By establishing the RWN model for an appropriate principal component subspace, the computing load of recognising combustion operation conditions is significantly reduced. In addition, Hotelling’s T2 and SPE (Squared Prediction Error) statistics of the corresponding operation conditions are calculated to identify the abnormalities of the combustion. The proposed approach is evaluated using flame image datasets obtained on a 250 kWth air- and oxy-fuel Combustion Test Facility. Variable operation conditions were achieved by changing the primary air and SA/TA (Secondary Air to Territory Air) splits. The results demonstrate that, for the operation conditions examined, the condition recognition success rate of the proposed PCA-RWN model is over 91%, which outperforms other machine learning classifiers with a reduced training time. The results also show that the abnormal conditions exhibit different oscillation frequencies from the normal conditions, and the T2 and SPE statistics are capable of detecting such abnormalities

Flame stability and burner condition monitoring through optical sensing and digital imaging

This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for flame stability and burner condition monitoring on fossil-fuel-fired furnaces. A review of methodologies and technologies for the monitoring of flame stability and burner condition is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating optical sensing, digital imaging, digital signal/image processing and soft computing techniques, is proposed. Based on this strategy, a prototype flame imaging system is developed. The system consists of a rigid optical probe, an optical-bearn-splitting unit, an embedded photodetector and signal-processing board, a digital camera, and a mini-motherboard with associated application software. Detailed system design, implementation, calibration and evaluation are reported. A number of flame characteristic parameters are extracted from flame images and radiation signals. Power spectral density, oscillation frequency, and a proposed universal flame stability index are used for the assessment of flame stability. Kernel-based soft computing techniques are employed for burner condition monitoring. Specifically, kernel principal components analysis is used for the detection of abnormal conditions in a combustion process, whilst support vector machines are used for the prediction of NO x emission and the identification of flame state. Extensive experimental work was conducted on a 9MW th heavy-oil-fired combustion test facility to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 660MWth heavy-oil-fired boiler to investigate the cause of the boiler vibration from a flame stability point of view. Results Obtained from the tests are presented and discussed

An Improved Algorithm, for the Measurement of Flame Oscillation Frequency

The oscillation frequency of a combustion flame measured from the spectrum of a flame signal often depends on the level of white noise in the signal as well as the signal sampling rate. In order to solve this problem, a new computing algorithm has been developed by making two improvements to the original direct calculation method. One of the improvements is the wavelet-based pre-filtering of the original flame signal. The other is an adaptive truncation of the spectrum of the filtered signal. It is found that the white noise can be largely cancelled by wavelet filtering. To decrease the smearing effect of the remaining noise a spectrum truncation scheme is utilised. The effectiveness of the improved algorithm is evaluated by conducting a series of experimental tests on an industrial scale combustion test facility. Results obtained are reported and discussed