Soot volume fraction profiling of asymmetric diffusion flames through tomographic imaging

This paper presents the 3-D (three-dimensional) reconstruction of soot volume fraction of diffusion flames based on tomographic imaging and image processing techniques. Eight flexible imaging fiber bundles and two RGB (Red, Green and Blue) CCD (Charge-coupled Device) cameras are used to obtain concurrently the 2-D (two-dimensional) image projections of the flame from eight different angles of view around the burner. Algorithms which combine the tomographic and two-color pyrometric techniques are utilized to reconstruct the soot volume fraction distributions on both cross- and longitudinal-sections of the flame. A series of experiments were carried out on a gas-fired combustion rig for the determination of soot volume fraction using the algorithms proposed. Test results demonstrate the effectiveness of the developed algorithms

Three-dimensional temperature field measurement of flame using a single light field camera

Compared with conventional camera, the light field camera takes the advantage of being capable of recording the direction and intensity information of each ray projected onto the CCD (charge couple device) sensor simultaneously. In this paper, a novel method is proposed for reconstructing three-dimensional (3-D) temperature field of a flame based on a single light field camera. A radiative imaging of a single light field camera is also modeled for the flame. In this model, the principal ray represents the beam projected onto the pixel of the CCD sensor. The radiation direction of the ray from the flame outside the camera is obtained according to thin lens equation based on geometrical optics. The intensities of the principal rays recorded by the pixels on the CCD sensor are mathematically modeled based on radiative transfer equation. The temperature distribution of the flame is then reconstructed by solving the mathematical model through the use of least square QR-factorization algorithm (LSQR). The numerical simulations and experiments are carried out to investigate the validity of the proposed method. The results presented in this study show that the proposed method is capable of reconstructing the 3-D temperature field of a flame

Simulation of flame temperature reconstruction through multi-plenoptic camera techniques

Due to the variety of burner structure and fuel mixing, the flame temperature distribution is not only manifold but also complex. Therefore, it is necessary to develop an advanced temperature measurement technique, which can provide not only the adequate flame radiative information but also reconstruct the complex temperature accurately. This paper presents a comprehensive simulation of flame temperature reconstruction through multi-plenoptic camera techniques. A novel multi-plenoptic camera imaging technique is proposed which is able to provide adequate flame radiative information only from two different directions and to reconstruct the three dimensional (3D) temperature of a flame. An inverse algorithm i.e., Non-negative Least Squares is used to reconstruct the flame temperature. To verify the reconstruction algorithm, two different temperature distributions such as unimodal axisymmetric and bimodal asymmetric are used. Numerical simulations are carried out to evaluate the performance of the technique. It has been observed that the reconstruction accuracy decreases with the increasing of signal-to-noise ratios. However, compared with the single plenoptic and conventional multi-camera techniques, the proposed method has the advantages of lower relative error and better reconstruction quality and stability even with the higher SNRs for both temperature distributions. Therefore, the proposed multi-plenoptic camera imaging technique is capable of reconstructing the complex 3-D temperature fields more accurately

Temperature Distribution Measurement Using the Gaussian Process Regression Method

The temperature distribution in real-world industrial environments is often in a three-dimensional space, and developing a reliable method to predict such volumetric information is beneficial for the combustion diagnosis, the understandings of the complicated physical and chemical mechanisms behind the combustion process, the increase of the system efficiency, and the reduction of the pollutant emission. In accordance with the machine learning theory, in this paper, a new methodology is proposed to predict three-dimensional temperature distribution from the limited number of the scattered measurement data. The proposed prediction method includes two key phases. In the first phase, traditional technologies are employed to measure the scattered temperature data in a large-scale three-dimensional area. In the second phase, the Gaussian process regression method, with obvious superiorities, including satisfactory generalization ability, high robustness, and low computational complexity, is developed to predict three-dimensional temperature distributions. Numerical simulations and experimental results from a real-world three-dimensional combustion process indicate that the proposed prediction method is effective and robust, holds a good adaptability to cope with complicated, nonlinear, and high-dimensional problems, and can accurately predict three-dimensional temperature distributions under a relatively low sampling ratio. As a result, a practicable and effective method is introduced for three-dimensional temperature distribution

Approach to reduce light field sampling redundancy for flame temperature reconstruction

Flame temperature measurement through a light field camera shows an attractive research interest due to its capabilities of obtaining spatial and angular rays' information by a single exposure. However, the sampling information collected by the light field camera is vast and most of them are redundant. The reconstruction process occupies a larger computing memory and time-consuming. We propose a novel approach i.e., feature rays under-sampling (FRUS) to reduce the light field sampling redundancy and thus improve the reconstruction efficiency. The proposed approach is evaluated through numerical and experimental studies. Effects of under-sampling methods, flame dividing voxels, noise levels and light field camera parameters are investigated. It has been observed that the proposed approach provides better anti-noise ability and reconstruction efficiency. It can be valuable not only for the flame temperature reconstruction but also for other applications such as particle image velocimetry and light field microscope

Flame temperature reconstruction through multi-plenoptic camera technique

Due to the variety of burner structure and fuel mixing, the flame temperature distribution is not only irregular but also complex. Therefore, it is necessary to develop an advanced temperature measurement technique, which can provide not only adequate flame radiative information but also reconstruct complex flame temperature accurately. In this paper, a novel multi-plenoptic camera imaging technique is proposed which is not only provide adequate flame radiative information from two different directions but also reconstruct the complex flame temperature distribution accurately. An inverse algorithm i.e., Non-Negative Least Squares is used to reconstruct the flame temperature. The bimodal asymmetric temperature distribution is considered to verify the feasibility of the proposed system. Numerical simulations and experiments were carried out to evaluate the performance of the proposed technique. Simulation results demonstrate that the proposed system is able to provide higher reconstruction accuracy although the reconstruction accuracy decreases with the increase of noise levels. Meanwhile, compared with the single plenoptic and conventional multi-camera techniques, the proposed method has the advantages of lower relative error and better reconstruction quality even with higher noise levels. The proposed technique is further verified by experimental studies. The experimental results also demonstrate that the proposed technique is effective and feasible for the reconstruction of flame temperature. Therefore, the proposed multi-plenoptic camera imaging technique is capable of reconstructing the complex flame temperature fields more precisely

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

Spatial resolution of light field sectioning pyrometry for flame temperature measurement

The light field sectioning pyrometry (LFSP) has proven a significant advancement for in-situ measurement of flame temperature through a single light field camera. However, the spatial resolution of LFSP is limited, which severely inhibits the measurement accuracy. This paper aims to evaluate the spatial resolution of LFSP for flame temperature measurement quantitatively. A theoretical model of the spatial resolution is established based on optical parameters and point spread function of the light field camera. The spatial resolution is then numerically analyzed with different parameters of light field cameras. Based on the theoretical model, a novel cage-typed light field camera with a higher spatial resolution of LFSP is developed and experimentally evaluated. A significant improvement of spatial resolution about 17% and 50% in lateral and depth directions, respectively, is achieved. Results show that the spatial resolution is in good agreement with the theoretical model. The LFSP is then evaluated under different combustion cases and their temperatures are reconstructed

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