The potential of on-line optical flow measurement in the control and monitoring of pilot-scale oxy-coal flames

pre-printDigital image processing techniques oer a wide array of tools capable of extracting apparent displacement or velocity information from sequences of images of moving objects. Optical flow algorithms have been widely used in areas such as traffic monitoring and surveillance. The knowledge of instantaneous apparent flame velocities (however they are defined) may prove to be valuable during the operation and control of industrial-scale burners. Optical diagnostics techniques, coupled with on-line image processing have been applied in the optimization of coal-red power plants; however, regardless of the available technology, the current methods do not apply optical flow measurement. Some optical flow algorithms have the potential of real-time applicability and are thus possible candidates for on-line apparent flame velocity extraction. In this paper, the potential of optical ow measurement in on-line flame monitoring and control is explored

The spatiotemporal coherence as an indicator of the stability in swirling flows

Combustion has played a key role in the development of human society; it has driven the evolution in the manufacturing processes, transportation, and it is used to produce the vast majority of the global energy consumed. The emission of pollutants from the combustion of fossil fuels in power plants lead to the development of advanced clean energy technologies, such as carbon capture and storage. Oxyfuel combustion is part of the carbon capture and storage techniques, and consists in the replacement of the air as oxidiser in the reaction with a mixture of oxygen and recycled flue gas, thus allowing a rich CO2 out-flow stream that can subsequently be compressed, transported and safely stored. The number of phenomena in combustion that are inherently dynamic impede the convention of a unique conception of flame stability. However, the quantification of the flow repeatability can produce insights on the efficiency of the process. This thesis presents the assessment of the stability in swirling flows through the calculation of their spatiotemporal coherence. The experimental data obtained from a 250 kWth combustor allows the assessment of the flame by means of spectral and oscillation severity analyses. A similar methodology is developed to analyse the data from large eddy simulations. The spectral analysis, the proper orthogonal decomposition and the dynamic mode decomposition have been employed to account for the temporal, spatial and spatiotemporal coherence of the flow, respectively. The spatiotemporal coherence is employed as a comprehensive term for the characterisation of the dynamic behaviour in the swirling flows and as a measurable indicator of the stability. This concept can be incorporated into the design of novel combustion technologies that will lead into a sustained reduction in pollutants and to the mitigation of the noxious effects associated to them

Doctor of Philosophy

dissertationDigital image processing has wide ranging applications in combustion research. The analysis of digital images is used in practically every scale of studying combustion phenomena from the scale of individual atoms to diagnosing and controlling large-scale combustors. Digital image processing is one of the fastest-growing scientific areas in the world today. From being able to reconstruct low-resolution grayscale images from transmitted signals, the capabilities have grown to enabling machines carrying out tasks that would normally require human vision, perception, and reasoning. Certain applications in combustion science benefit greatly from recent advances in image processing. Unfortunately, since the two fields - combustion and image processing research - stand relatively far from each other, the most recent results are often not known well enough in the areas where they may be applied with great benefits. This work aims to improve the accuracy and reliability of certain measurements in combustion science by selecting, adapting, and implementing the appropriate techniques originally developed in the image processing area. A number of specific applications were chosen that cover a wide range of physical scales of combustion phenomena, and specific image processing methodologies were proposed to improve or enable measurements in studying such phenomena. The selected applications include the description and quantification of combustion-derived carbon nanostructure, the three-dimensional optical diagnostics of combusting pulverized-coal particles and the optical flow velocimetry and quantitative radiation imaging of a pilot-scale oxy-coal flame. In the field of the structural analysis of soot, new structural parameters were derived and the extraction and fidelity of existing ones were improved. In the field of pulverized-coal combustion, the developed methodologies allow for studying the detailed mechanisms of particle combustion in three dimensions. At larger scales, the simultaneous measurement of flame velocity, spectral radiation, and pyrometric properties were realized

KLASYFIKACJA STANU PROCESU SPALANIA NA PODSTAWIE ANALIZY OBRAZU PŁOMIENIA

This paper presents comparison image classification method of cofiring biomass and pulverized coal. Defined two class of combustion: stable and unstable for nine variants with different power value parameters and fixed amount biomass. Experimental results show that achieved correct classification of images for the assumed variants. The best results were obtained with K-NN classifier (parameter K = 7).W pracy przedstawiono porównanie wybranych metod klasyfikacji obrazów dla współspalania pyłu węglowego i biomasy. Zdefiniowano dwie klasy spalania: stabilne i niestabilne dla dziewięciu wariantów z różnymi parametrami mocy oraz stałą ilością biomasy. Wyniki badań pokazują, poprawną klasyfikację obrazów dla założonych wariantów. Najlepsze wyniki uzyskano dla klasyfikatora K-NN z parametrem K = 7

Oscillating coal and biomass flames: A spectral and digital imaging approach for air and oxyfuel conditions

The transient nature of a flame can be quantified by performing spectral and oscillatory analysis of its parameters, such as the flame's luminance and temperature. This paper presents an assessment of the effect of an oxyfuel environment on the combustion of two different solid fuels, a high volatile bituminous coal and a white wood biomass, in a 250 kWth pilot-scale combustion test facility. A digital flame monitoring system was fitted to the experimental furnace, and was used to record high speed videos of the flame. Transient signals for both digital luminance and temperature were obtained after the instantaneous frames were extracted from the original videos. Spectral analysis was performed over the transient signal in order to analyse the temporal coherence of the flame through a weighted oscillation frequency value. An additional parameter, the oscillation index, which accounts for the amplitude of the oscillation of the flame, was computed to complement the information recovered from the flame. The oscillation trends obtained from these experiments assess the dynamic response of the flame to different combustion environments within the furnace. In general, it was found that oxyfuel flames showed a discernible temporal repeatability and a lower magnitude of the oscillation of their flame parameters, and therefore are registered as being more stable than their counterpart under air combustion conditions. In addition, the biomass flames exhibit less sensitivity to the oxyfuel combustion environment than what was found with coal, which may allow future oxy-biomass regimes to operate under a much wider envelop of firing conditions

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

Flame stability detection method for co-firing of biomass fuels based on digital image processing

Combustion of low-quality fuels or fuel blends will lead to flame instability, resulting in low combustion efficiency and high NOx emissions. Due to the inherent complexity of burner flames and the lack of an effective means for flame monitoring and characterization, it is difficult to evaluate the flame stability in a combustion process quantitatively. To solve this problem, a method based on digital image processing for co-firing biomass fuels is proposed in this paper to monitor various characteristic parameters of a burner flame and evaluate its stability. In this method, a general flame stability index with continuous values in the range of [0, 1] is defined, and by using a digital CCD camera, the flame image information is collected. After the collected image is analyzed, the characteristic parameters like the flame length/height, brightness, temperature, flicker frequency and others are extracted. Then, statistical analysis and data fusion are carried out for theses characteristic parameters, and the flame stability index is obtained. Thus, the quantitative detection and evaluation of flame stability is realized. Moreover, this method was verified on a laboratory-scale combustion test rig. The combustion behaviours of different biomass blends(corncob-wheat straw blend, willow-peanut shell blend and peanut shell-wheat straw blend) were compared. The results show that, the defined flame stability index could effectively characterize the flame combustion state

Advanced Flame Monitoring and Emission Prediction through Digital Imaging and Spectrometry

This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for burner condition monitoring and NOx emissions prediction on fossil-fuel-fired furnaces. A review of methodologies and technologies for burner condition monitoring and NOx emissions prediction is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating digital imaging, UV-visible spectrum analysis and soft computing techniques, is proposed. Based on these techniques, a prototype flame imaging system is developed. The system consists mainly of an optical and fibre probe protected by water-air cooling jacket, a digital camera, a miniature spectrometer 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 spectral signals. Luminous and geometric parameters, temperature and oscillation frequency are collected through imaging, while flame radical information is collected by the spectrometer. These parameters are then used to construct a neural network model for the burner condition monitoring and NOx emission prediction. Extensive experimental work was conducted on a 120 MWth gas-fired heat recovery boiler to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 40 MWth coal-fired combustion test facility to investigate the production of NOx emissions and the burner performance. The results obtained demonstrate that an Artificial Neural Network using the above inputs has produced relative errors of around 3%, and maximum relative errors of 8% under real industrial conditions, even when predicting flame data from test conditions not disclosed to the network during the training procedure. This demonstrates that this off the shelf hardware with machine learning can be used as an online prediction method for NOx

Thermal image analysis using calibrated video imaging

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on April 23, 2009)Thesis (Ph.D.) University of Missouri-Columbia 2006.This research work introduces new techniques for temperature measurement that utilizes visual spectrum of light. A periscope with a CCD camera is used to capture the image from a high temperature gas fired furnace and calculate the temperature distribution of flame and furnace wall. The proposed visual thermal imaging methods for calculating the flame or wall temperature profiling are applied in several experiments in the laboratory and commercial furnaces. These novel approaches include reference technique which utilizes a known temperature measured either by an infrared optical device known as IR-Gun or thermocouples; and camera response curve method which utilizes two-color blackbody technique and flame adiabatic temperature condition. The camera calibration technology is also utilized and integrated with these temperature calculation methods to enable the temperature measurement in a specified region and produce a three dimensional temperature profiling. Various experiments are performed and temperature data are collected both with IR-Gun and thermocouples. These data are then compared with the results of CCD camera images using the proposed methods. Calculated temperatures from various experiments indicate that these methods yield excellent results that are closely comparable with both the IR Gun readings and thermocouples records.Includes bibliographical reference