Duo Sun

Three-dimensional reconstruction of flame temperature and emissivity distribution using optical tomographic and two-colour pyrometric techniques

This paper presents an experimental investigation, visualization and validation in the three-dimensional (3D) reconstruction of flame temperature and emissivity distributions by using optical tomographic and two-colour pyrometric techniques. A multi-camera digital imaging system comprising eight optical imaging fibres and two RGB charged-couple device (CCD) cameras are used to acquire two-dimensional (2D) images of the flame simultaneously from eight equiangular directions. A combined logical filtered back-projection (LFBP) and simultaneous iterative reconstruction and algebraic reconstruction technique (SART) algorithm is utilized to reconstruct the grey-level intensity of the flame for the two primary colour (red and green) images. The temperature distribution of the flame is then determined from the ratio of the reconstructed grey-level intensities and the emissivity is estimated from the ratio of the grey level of a primary colour image to that of a blackbody source at the same temperature. The temperature measurement of the system was calibrated using a blackbody furnace as a standard temperature source. Experimental work was undertaken to validate the flame temperature obtained by the imaging system against that obtained using high-precision thermocouples. The difference between the two measurements is found no greater than ±9%. Experimental results obtained on a laboratory-scale propane fired combustion test rig demonstrate that the imaging system and applied technical approach perform well in the reconstruction of the 3D temperature and emissivity distributions of the sooty flame.

Flame stability monitoring and characterization through digital imaging and spectral analysis

This paper presents the design, implementation and evaluation of an instrumentation system for the stability monitoring and characterization of combustion flames. The system, incorporating optical sensing, image processing and spectral analysis techniques, is designed to monitor a range of flame characteristic parameters. The stability of the flame is assessed through statistical analysis of the flame parameters obtained. Embedded computer techniques are employed to ensure the compactness and robustness of the system. Experiments were conducted on a gas-fired combustion test rig to evaluate the system. The impact of equivalence ratio on the stability of the gaseous flame is investigated. Further trials were carried out on a 9 MWth heavy-oil-fired combustion test facility. The impact of the swirl vane angle of tertiary air on the oil-fired flames is studied. The results demonstrate the effectiveness of the system for the monitoring and characterization of the flame stability.

Condition Monitoring of Combustion Processes Through Flame Imaging and Kernel Principal Component Analysis

This article presents a methodology for the diagnosis of abnormal conditions in a combustion process through flame imaging and kernel principal component analysis (KPCA). A digital imaging system is used to capture real-time flame images and radiation signals, from which flame characteristics such as flame area, brightness, non-uniformity, and oscillation frequency are quantified. These characteristics are used as the variables to establish the KPCA model of the combustion process. With the use of Hotellings T2 and Q statistics, the monitoring of abnormal conditions of the combustion process is achieved. Unlike the traditional principal component analysis (PCA) method, the KPCA method is capable of dealing with nonlinear data via nonlinear mapping, which projects the original nonlinear input space into a high-dimensional linear feature space. The effectiveness of the methodology is demonstrated by applying the approach to processing the data obtained on a 9MWth heavy oil fired combustion test facility. Experimental results obtained show that the KPCA method outperforms the traditional PCA in discriminating between the normal and abnormal combustion conditions, even in cases where the number of training samples is limited.

On-Line Nonintrusive Detection of Wood Pellets in Pneumatic Conveying Pipelines Using Vibration and Acoustic Sensors

This paper presents a novel instrumentation system for on-line nonintrusive detection of wood pellets in pneumatic conveying pipelines using vibration and acoustic sensors. The system captures the vibration and sound generated by the collisions between biomass particles and the pipe wall. Time-frequency analysis technique is used to eliminate environmental noise from the signal, extract information about the collisions, and identify the presence of wood pellets. Experiments were carried out on an industrial pneumatic conveying pipeline to assess effectiveness and operability. The impacts of various factors on the performance of the detection system are compared and discussed, including different sensing (vibration sensor versus acoustic sensor), different time-frequency analysis methods (wavelet-based denoising versus classic filtering), and different system installation locations.

Quantitative Assessment of Flame Stability Through Image Processing and Spectral Analysis

This paper experimentally investigates two generalized methods, i.e., a simple universal index and oscillation frequency, for the quantitative assessment of flame stability at fossil-fuel-fired furnaces. The index is proposed to assess the stability of flame in terms of its color, geometry, and luminance. It is designed by combining up to seven characteristic parameters extracted from flame images. The oscillation frequency is derived from the spectral analysis of flame radiation signals. The measurements involved in these two methods do not require prior knowledge about fuel property, burner type, and other operation conditions. They can therefore be easily applied to flame stability assessment without costly and complex adaption. Experiments were carried out on a 9-MW heavy-oil-fired combustion test rig over a wide range of combustion conditions including variations in swirl vane position of the tertiary air, swirl vane position of the secondary air, and the ratio of the primary air to the total air. The impact of these burner parameters on the stability of heavy oil flames is investigated by using the index and oscillation frequency proposed. The experimental results obtained demonstrate the effectiveness of the methods and the importance of maintaining a stable flame for reduced NOx emissions. It is envisaged that such methods can be easily transferred to existing flame closed-circuit television systems and flame failure detectors in power stations for flame stability monitoring.

Prediction of NOx emissions throughflame radical imaging and neural network based soft computing

The characteristics of reacting radicals in a flame are crucial for an in-depth understanding of the formation process of combustion emissions. This paper presents an algorithm for the prediction of NOx (NO and NO2) emissions in flue gas through flame radical imaging, flame temperature monitoring and application of Neural Network techniques. Radiation images of flame radicals OH*, CN*, CH* and C2* are captured using an intensified multi-wavelength imaging system. Flame temperature is determined using a spectrometer and two-color pyrometry. Based on these images, the characteristic values of the flame radicals are extracted. These characteristic values, together with the flame temperature, are then used to predict NOx emissions. Experimental results from a laboratory-scale gas-fired combustion rig have shown the effectiveness of the proposed method for the prediction of NOx emissions.

Measurement of soot temperature, emissivity and concentration of a heavy-oil flame through pyrometric imaging

This paper presents the monitoring and characterization of emissive properties of soot particles in heavy oil flames based on pyrometric imaging techniques. The soot temperature is derived from the relationship between the primary colors of flame images captured by a RGB camera. The emissivity of soot particles is then estimated by using the gray-level ratio of a primary color of the image to that of a blackbody source at the same temperature. The soot concentration is represented and estimated by KL factor, which is derived from the Hottel and Broughtons model once the emissivity is determined. The imaging system is calibrated using a blackbody furnace as a standard temperature source. The measurement accuracy is verified by applying the system to measure the true temperature of a tungsten lamp. The maximum relative error is about 0.9%. Experiments were conducted on a 9MWth industrial-scale combustion test facility to investigate the impact of the ratio of overfire air to total air, and the location of overfire air ports on the soot temperature, emissivity and concentration of a heavy oil flame.

An embedded imaging and signal processing system for flame stability monitoring and characterisation

This paper presents the design, implementation and evaluation of an instrumentation system for flame stability monitoring and characterisation on industrial furnaces. The system, incorporating digital imaging and spectral analysis techniques, is designed to monitor a range of flame characteristic parameters. The stability of the flame is then assessed through statistical analysis of the flame parameters obtained. Embedded computer techniques are employed to ensure the system is compact and robust. Experiments were conducted on a laboratory-scale combustion test rig to evaluate the system. The impact of the air-to-fuel ratios on the stability of a gaseous flame is investigated. The results demonstrate that the system is capable of monitoring flame stability in a statistical way.

A simple index based quantitative assessment of flame stability

This paper proposes a simple universal index for on-line quantitative assessment of flame stability. The proposed index has a dynamic range of [0, 1] and is designed by combining the dynamic characteristics of seven parameters extracted from flame images in HSI color space. It assesses the flame stability in terms of color, geometry and luminance. Experiments were carried out on a 9MWth heavy-oil-fired combustion test facility. The impact of the swirl vanes on the stability of a heavy oil flame is investigated. The results obtained demonstrate the effectiveness of the proposed approach to quantitative flame stability assessment.

Detecting the presence of large biomass particles in pneumatic conveying pipelines using an acoustic sensor

This paper proposes a novel approach to online automatic detection of the presence of large biomass particles in a pneumatic conveying pipeline using an acoustic emission sensor and time-frequency analysis techniques. The acoustic sensor is used to capture the sound emitted from the collisions between biomass particles and pipe wall. Time-frequency analysis technique is used to eliminate environmental noise from the acoustic signal, extract the revealing information about the collisions, and identify the large particles. The acoustic sensor together with its signal conditioning unit is integrated into a compact enclosure, which can be easily attached to the outer face of a pneumatic pipeline. Experimental results obtained from an industrial pneumatic conveyor demonstrate the method works well and results are promising.