Portrait of Dr Gang Lu

Dr Gang Lu

Senior Lecturer in Electronic Instrumentation

About

Dr Gang Lu received a BEng degree from Central South University, Changsha, China in 1982, and a PhD degree from the University of Greenwich, UK in 2000. He started his career as a mechanical engineer, serving for the iron- and steel-making industry in China, and had been a Post-Doctoral Research Fellow with the University of Greenwich and the University of Kent. He is now a Senior Lecturer in Electronic Instrumentation with the School of Engineering and Digital Arts, University of Kent. His research interests include sensors, instrumentation, measurement, condition monitoring, digital signal processing, digital image processing and applications of artificial intelligence. He has been involved in a range of projects on advanced monitoring and characterization of flames in both lab- and industrial-scale fossil-fuel combustion systems.

Dr Gang Lu is a Chartered Engineer, a senior member of IEEE, and a member of the Energy Institute. He was awarded the Engineering Innovation Prize (Energy) by the IET in 2006.

Research interests


Teaching

  • EL875 Advanced Sensors and Instrumentation Systems
  • EL600 Project (Final Year Project)
  • EL565 Electronic Instrumentation and Measurement Systems
  • EL515 Physiological Measurement
  • EL562 Project Planning and Project Management
  • EL318 Engineering Mathematics
  • EL027 Digital Electronics

Publications

Article

  • Guo, M. et al. (2018). Temperature Measurement of Stored Biomass Using Low-frequency Acoustic Waves and Correlation Signal Processing Techniques. Fuel [Online] 227:89-98. Available at: https://doi.org/10.1016/j.fuel.2018.04.062.
    As a substitute of traditional fossil fuels, biomass is widely used to generate electricity and heat. The temperature of stored biomass needs to be monitored continuously to prevent the biomass from self-ignition. This paper proposes a non-intrusive method for the temperature measurement of stored biomass based on acoustic sensing techniques. A characteristic factor is introduced to obtain the sound speed in free space from the measured time of flight of acoustic waves in stored biomass. After analysing the relationship between the defined characteristic factor and air temperature, an updating procedure on the characteristic factor is proposed to reduce the influence of air temperature. By measuring the sound speed in free space air temperature is determined which is the same as biomass temperature. The proposed methodology is examined using a single path acoustic system which consists of a source and two sensors. A linear chirp signal with a duration of 0.1 s and frequencies of 200-500 Hz is generated and transmitted through stored biomass pellets. The time of flight of sound waves between the two acoustic sensors is measured through correlation signal processing. The relative error of measurement results using the proposed method is no more than 4.5% over the temperature range from 22℃ to 48.9℃. Factors that affect the temperature measurement are investigated and quantified. The experimental results indicate that the proposed technique is effective for the temperature measurement of stored biomass with a maximum error of 1.5℃ under all test conditions.
  • Daood, S. et al. (2017). Additive technology for pollutant control and efficient coal combustion. Energy and Fuels [Online] 31:5581-5596. Available at: http://dx.doi.org/10.1021/acs.energyfuels.7b00017.
    High efficiency and low emissions from pf coal power stations has been the drive behind the development of present and future efficient coal combustion technologies. Upgrading coal, capturing CO2, reducing emission of NOx, SO2 and particulate matter, mitigating slagging, fouling and corrosion are the key initiatives behind these efficient coal technologies. This study focuses on a newly developed fuel additive (Silanite™) based efficient coal combustion technology, which addresses most of the aforementioned key points. Silanite™ a finely milled multi-oxide additive when mixed with the coal without the need to change the boiler installation has proven to increase the boiler efficiency, flame temperature with reduction in corrosion, NOx and particulate matter (dust) emissions. The process has been developed through bench, pilot (100kW) and full scale (233 MWth). The process has been found to have a number of beneficial effects that add up to a viable retrofit to existing power plant as demonstrated on the 233MWth boiler tests (under BS EN 12952-15:2003 standard)
  • Bai, X., Lu, G. and Yan, Y. (2017). Flame image segmentation using multiscale color and wavelet-based texture features. Computer Engineering and Applications (Chinese) [Online] 53:213-219. Available at: http://dx.doi.org/10.3778/j.issn.1002-8331.1610-0083.
    Accurate and reliable segmentation of flame images are crucial in vision based monitoring and characterization of flames. It is, however, difficult to maintain the segmentation accuracy while achieving fast processing time due to the impact of the background noise in the images and the variation of operation conditions. To improve the quality of the image segmentation, a flame image segmentation method is proposed based on Multiscale Color and Wavelet-based Textures(MCWT) of the images. By combining the color and texture features, a characteristic matrix is built and then compressed using a local mean method. The outer contour of the flame image under the compressed scale is detected by a cluster technique. Subsequently, the flame edge region under the original scale is determined, following that, the characteristic matrix of the edge region is constructed and classified, and finally, the flame image segmentation is achieved. Flame images captured from an industrial-scale coal-firedtest rig under different operation conditions are segmented to evaluate the proposed method. The test results demonstrate that the performance of segmenting flame images of the proposed method is superior to other traditional methods. It also has been found that the proposed method performs more effectively in segmenting the flame images with Gaussian and pepper and salt noise.
  • Bai, X. et al. (2017). Combustion behavior profiling of single pulverized coal particles in a drop tube furnace through high-speed imaging and image analysis. Experimental Thermal and Fluid Science [Online]:322-330. Available at: https://doi.org/10.1016/j.expthermflusci.2017.03.018.
    Experimental investigations into the combustion behaviors of single pulverized coal particles are carried out based on high-speed imaging and image processing techniques. A high-speed video camera is employed to acquire the images of coal particles during their residence time in a visual drop tube furnace. Computer algorithms are developed to determine the characteristic parameters of the particles from the images extracted from the videos obtained. The parameters are used to quantify the combustion behaviors of the burning particle in terms of its size, shape, surface roughness, rotation frequency and luminosity. Two sets of samples of the same coal with different particle sizes are studied using the techniques developed. Experimental results show that the coal with different particle sizes exhibits distinctly different combustion behaviors. In particular, for the large coal particle (150-212 m), the combustion of volatiles and char takes place sequentially with clear fragmentation at the early stage of the char combustion. For the small coal particle (106-150 m), however, the combustion of volatiles and char occurs simultaneously with no clear fragmentation. The size of the two burning particles shows a decreasing trend with periodic variation attributed to the rapid rotations of the particles. The small particle rotates at a frequency of around 30 Hz, in comparison to 20 Hz for the large particle due to a greater combustion rate. The luminous intensity of the large particle shows two peaks, which is attributed to the sequential combustion of volatiles and char. The luminous intensity of the small particle illustrates a monotonously decreasing trend, suggesting again a simultaneous devolatilization/volatile and char combustion.
  • Bai, X., Lu, G. and Yan, Y. (2017). Fractal Characteristics of Thin Thermal Mixing Layers in Coal-Fired Flame. Journal of Combustion Science and Technology [Online] 3:225-230. Available at: http://journals.tju.edu.cn/rs/oa/DArticle.aspx?type=view&id=R201606021.
    The images of turbulent flame were acquired by using a digital imaging system on an industry-scale pulverized coal-fired test rig.The fractal dimensions of thin thermal mixing layers in flame were computed through morphology-based flame image processing techniques.The effects of the ratios of primary air and secondary/tertiary air on fractal dimensions were characterized.The results presented in this work show that the variations of fractal dimension are closely related to the ratio changes of primary air and secondary/tertiary air. Therefore,the fractal dimensions of flame thin thermal mixing layers can be used as an important indicator for the control and optimization of a combustion process.
  • Shan, L. et al. (2017). Studies on Combustion Behaviours of Single Biomass Particles Using a Visualization Method. BIOMASS & BIOENERGY [Online] 109. Available at: https://doi.org/10.1016/j.biombioe.2017.12.008.
    Combustion behaviours of single particles (125-150m) of eucalyptus, pine and olive residue were investigated by means of a transparent drop-tube furnace, electrically heated to 1073 K, and a high-speed camera coupling with a long distance microscope. All three types of biomass samples were found to have two evident combustion phases, i.e., volatile combustion in an envelope flame and subsequent char combustion with high luminance. Yet, due to differences in chemical compositions and properties, their combustion behaviours - were also seen somewhat discrepant. The volatile flame of the olive residue was fainter than that of pine and eucalyptus due to its high ash content. During the char combustion phase, fragmentation took place for most pine particles but only for a few particles of olive residue and eucalyptus. For all three types of biomass samples, the flame size and the average luminous intensity profiles were deduced from the captured combustion video images whilst the combustion burnout times of the volatile matter and char were also calculated and estimated. There were two peak values clearly shown on the profiles of both the flame size and the average luminous intensity during the volatile combustion process of pine and eucalyptus particles, which, according to literature, could not be observed by optical pyrometry. The observed peaks correspond to the devolatilisation of hemicellulose and cellulose. The ratio between the estimated char burnout time and volatile combustion time increases quadratically with the fixed carbon to volatile matter mass ratio, confirming char combustion is much slower than volatile combustion.
  • Zhou, H. et al. (2017). Combining flame monitoring techniques and support vector machine for the online identification of coal blends. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering) [Online] 18:677-689. Available at: http://dx.doi.org/10.1631/jzus.A1600454.
    The combustion behavior of two single coals and three coal blends in a 300 kW coal-fired furnace under variable operating conditions was monitored by a flame monitoring system based on image processing and spectral analysis. A similarity coefficient was defined to analyze the similarity of combustion behavior between two different coal types. A total of 20 flame features, extracted by the flame monitoring system, were ranked by weights of their importance estimated using ReliefF, a feature selection algorithm. The mean of the infrared signal was found to have by far the highest importance weight among the flame features. Support vector machine (SVM) was used to identify the coal types. The number of flame features used to build the SVM model was reduced from 20 to 12 by combining the methods of ReliefF and SVM, and computational precision was guaranteed simultaneously. A threshold was found for the relationship between the error rate and similarity coefficient, which were positively correlated. The success rate decreased with increasing similarity coefficient. The results obtained demonstrate that the system can achieve the online identification of coal blends in industry.
  • Bai, X. et al. (2017). Multi-mode Combustion Process Monitoring on a Pulverised Fuel Combustion Test Facility based on Flame Imaging and Random Weight Network Techniques. Fuel [Online]. Available at: https://doi.org/10.1016/j.fuel.2017.03.091.
    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.
  • Li, N. et al. (2016). Prediction of NOx Emissions from a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques. Combustion Science and Technology [Online] 188:233-246. Available at: http://doi.org/10.1080/00102202.2015.1102905.
    This article presents a methodology for predicting NOx emissions from a biomass combustion process through flame radical imaging and deep learning (DL). The dataset was established experimentally from flame radical images captured on a biomass-gas fired test rig. Morphological component analysis is undertaken to improve the quality of the dataset, and the region-of-interest extraction is introduced to extract the flame radical part and rescale the image size. The developed DL-based prediction model contains three successive stages for implementing the feature extraction, feature fusion, and emission prediction. The fine-tuning based on the prediction is introduced to adjust the process of the feature fusion. The effects of the feature fusion and fine-tuning are discussed in detail. A comparison between various image- and machine-learning-based prediction models show that the proposed DL prediction model outperforms other models in terms of root mean square error criteria. The predicted NOx emissions are in good agreement with the measurement results.
  • Bai, X. et al. (2016). Multimode Monitoring of Oxy-gas Combustion through Flame Imaging, Principal Component Analysis and Kernel Support Vector Machine. Combustion Science and Technology [Online] 189:776-792. Available at: http://dx.doi.org/10.1080/00102202.2016.1250749.
    This paper presents a method for the multimode monitoring of combustion stability under different oxy-gas fired conditions based on flame imaging, principal component analysis and kernel support vector machine (PCA-KSVM) techniques. The images of oxy-gas flames are segmented into premixed and diffused regions through Watershed Transform method. The weighted color and texture features of the diffused and premixed regions are extracted and projected into two subspaces using the PCA to reduce the data dimensions and noises. The multi-class KSVM model is finally built based on the flame features in the principal component subspace to identify the operation condition. Two classic multivariate statistic indices, i.e. Hotelling’s T2 and squared prediction error (SPE), are used to assess the normal and abnormal states for the corresponding operation condition. The experimental results obtained on a lab-scale oxy-gas rig show that the weighted color and texture features of the defined diffused and premixed regions are effective for detecting the combustion state and that the proposed PCA-KSVM model is feasible and effective to monitor a combustion process under variable operation conditions.
  • Li, X. et al. (2015). NOx emission prediction based on flame radical profiling and support vector machine. Proceedings of the CSEE [Online] 35:1413-1419. Available at: http://www.dx.doi.org/10.13334/j.0258-8013.pcsee.2015.06.016.
    Characteristics of reacting radicals in a flame are crucial for an in-depth understanding of the formation process of combustion emissions. An algorithm for the prediction of NOx( NO and NO2) Emissions in flue gas was presented through flame radical imaging, flame temperature monitoring and application of soft computing techniques, support vector machine. Radiation images of flame radicals OH *, CN *, CH *and C2* Were captured using an intensified multi-wavelength imaging system. Flame temperature was determined using a spectrometer and two-color pyrometry. Based on these images, the characteristic values ​​of the flame radicals were extracted. These characteristic values ​​(contours and ratios of radical intensities), together with the flame temperature, were then used to predict NOx emissions. Experimental results from a laboratory-scale gas-fired combustion rig show the effectiveness of the proposed method for the prediction of NOx emissions.
  • Li, X. et al. (2015). On-line identification of biomass fuels based on flame radical imaging and application of radical basis function neural network techniques. IET Renewable Power Generation [Online] 9:323-330. Available at: http://doi.org/10.1049/iet-rpg.2013.0392.
    In biomass fired power plants a range of biomass fuels are used to generate electric power. It is desirable to identify the type of biomass fuels on-line continuously in order to achieve an improved combustion efficiency, and reduced pollutant emissions. This paper presents the recent investigations into the on-line identification of biomass fuels based on the combination of flame radical imaging and radical basis function (RBF) neural network (NN) techniques. The characteristic values of flame radicals (OH*, CN*, CH* and C2*), including the intensity ratio, intensity contour, mean intensity, area and eccentricity, are computed to reconstruct two types of RBF NN, that is, accurate and probabilistic RBF networks. Experimental results obtained for three types of biomass fuels (flour, willow sawdust and palm kernel shell) firing on a laboratory-scale combustion test rig are presented to demonstrate the effectiveness of the proposed method.
  • Li, N. et al. (2015). Prediction of Pollutant Emissions of Biomass Flames Through Digital Imaging, Contourlet Transform, and Support Vector Regression Modeling. IEEE Transactions on Instrumentation and Measurement [Online] 64:2409-2416. Available at: http://doi.org/10.1109/TIM.2015.2411999.
    This paper presents a method for the prediction of NOx emissions in a biomass combustion process through the combination of flame radical imaging, contourlet transform and Zernike moment (CTZM), and least squares support vector regression (LS-SVR) modeling. A novel feature extraction technique based on the CTZM algorithm is developed. The contourlet transform provides the multiscale decomposition for flame radical images and the selected operator based on Zernike moments is designed to provide the well-defined structure for the images. The resulted image features are a variable structure, which is originated from the CTZM. Finally, the variable features of the images of four flame radicals (OH*, CN*, CH*, and C*2) are defined. The relationship between the variable features of radical images and NOx emissions is established through radial basis function network modeling, SVR modeling, and the LS-SVR modeling. A comparison between the three modeling approaches shows that the LS-SVR model outperforms the other two methods in terms of root-mean-square error and mean relative error criteria. In addition, the structure of the image features has a significant impact on the performance of the prediction models. The test results obtained on a biomass-gas fired test rig show the effectiveness of the proposed technical approach for the prediction of NOx emissions.
  • Sun, D. et al. (2015). Quantitative Assessment of Flame Stability Through Image Processing and Spectral Analysis. IEEE Transactions on Instrumentation and Measurement [Online] 64:3323-3333. Available at: http://doi.org/10.1109/TIM.2015.2444262.
    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.
  • Zhou, H. et al. (2014). Support vector machine based online coal identification through advanced flame monitoring. Fuel [Online] 117:944-951. Available at: http://dx.doi.org/10.1016/j.fuel.2013.10.041.
    This paper presents a new on-line coal identification system based on support vector machine (SVM) to achieve on-line coal identification under variable combustion conditions. Four different coals were burnt in a 0.3 MW coal combustion furnace with different coal feed rates, total air flow rates and flow rate ratios of primary air and secondary air. The flame monitoring system was installed at the exit of the burner to acquire the coal flame images and light intensity signals. Spatial and temporal flame features were extracted for coal identification. The averaged prediction accuracy is 99.1%. The mean value of the infrared signal has the most significant influence on prediction accuracy. For “unstudied” operation cases, the prediction accuracy is 94.7%.
  • Chalmers, H. et al. (2014). OxyCAP UK: Oxyfuel Combustion - academic Programme for the UK. Energy Procedia [Online] 63:504-510. Available at: http://dx.doi.org/10.1016/j.egypro.2014.11.055.
    The OxyCAP-UK (Oxyfuel Combustion - Academic Programme for the UK) programme was a £2 M collaboration involving researchers from seven UK universities, supported by E.On and the Engineering and Physical Sciences Research Council. The programme, which ran from November 2009 to July 2014, has successfully completed a broad range of activities related to development of oxyfuel power plants. This paper provides an overview of key findings arising from the programme. It covers development of UK research pilot test facilities for oxyfuel applications; 2-D and 3-D flame imaging systems for monitoring, analysis and diagnostics; fuel characterisation of biomass and coal for oxyfuel combustion applications; ash transformation/deposition in oxyfuel combustion systems; materials and corrosion in oxyfuel combustion systems; and development of advanced simulation based on CFD modelling.
  • Sun, D. et al. (2014). On-Line Nonintrusive Detection of Wood Pellets in Pneumatic Conveying Pipelines Using Vibration and Acoustic Sensors. IEEE Transactions on Instrumentation and Measurement [Online] 63:993-1001. Available at: http://dx.doi.org/10.1109/TIM.2013.2292284.
    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.
  • Hossain, M. et al. (2013). Three-dimensional reconstruction of flame temperature and emissivity distribution using optical tomographic and two-colour pyrometric techniques. Measurement Science and Technology [Online] 24:74010. Available at: https://doi.org/10.1088/0957-0233/24/7/074010.
    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.
  • Sun, D. et al. (2013). Condition Monitoring of Combustion Processes Through Flame Imaging and Kernel Principal Component Analysis. Combustion Science and Technology [Online] 185:1400-1413. Available at: http://dx.doi.org/10.1080/00102202.2013.798316.
    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 Hotelling's 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.
  • Yan, Y. et al. (2012). Recent Advances in Flame Tomography. Chinese Journal of Chemical Engineering [Online] 20:389-399. Available at: http://dx.doi.org/10.1016/S1004-9541(12)60402-9.
    To reduce greenhouse gas emissions from fossil fuel fired power plants, a range of new combustion technologies are being developed or refined, including oxy-fuel combustion, co-firing biomass with coal and fluidized bed combustion. Flame characteristics under such combustion conditions are expected to be different from those in normal air fired combustion processes. Quantified flame characteristics such as temperature distribution, oscillation frequency, and ignition volume play an important part in the optimized design and operation of the environmentally friendly power generation systems. However, it is challenging to obtain such flame characteristics particularly through a three-dimensional and non-intrusive means. Various tomography methods have been proposed to visualize and characterize flames, including passive optical tomography, laser based tomography, and electrical tomography. This paper identifies the challenges in flame tomography and reviews existing techniques for the quantitative characterization of flames. Future trends in flame tomography for industrial applications are discussed.
  • Qiu, T., Yan, Y. and Lu, G. (2012). An Autoadaptive Edge-Detection Algorithm for Flame and Fire Image Processing. IEEE Transactions on Instrumentation and Measurement [Online] 61:1486-1493. Available at: http://dx.doi.org/10.1109/TIM.2011.2175833.
    The determination of flame or fire edges is the
    process of identifying a boundary between the area where there
    is thermochemical reaction and those without. It is a precursor
    to image-based flame monitoring, early fire detection, fire evaluation,
    and the determination of flame and fire parameters. Several
    traditional edge-detection methods have been tested to identify
    flame edges, but the results achieved have been disappointing.
    Some research works related to flame and fire edge detection were
    reported for different applications; however, the methods do not
    emphasize the continuity and clarity of the flame and fire edges.
    A computing algorithm is thus proposed to define flame and fire
    edges clearly and continuously. The algorithm detects the coarse
    and superfluous edges in a flame/fire image first and then identifies
    the edges of the flame/fire and removes the irrelevant artifacts. The
    autoadaptive feature of the algorithm ensures that the primary
    symbolic flame/fire edges are identified for different scenarios.
    Experimental results for different flame images and video frames
    proved the effectiveness and robustness of the algorithm.

Conference or workshop item

  • Lu, G. (2018). Experimental Investigation of Oxy-combustion Behaviour of Single Biomass Pellets using High-speed Imaging and Colour Processing Techniques. in: 12th ECCRIA - The European Conference on Fuel and Energy Research and its Applications. TFERF- The Fuel and Energy Research Forum.
    Despite increasing use of renewable energy worldwide, coal remains to be the primary energy resource to meet the increasing demand for electric power in many countries. However, coal-fired Biomass fuel has been widely accepted as renewable energy in conventional power generation plants. Biomass fuels, however, can vary widely in properties, composition and structure, leading to drastically different 'fuel performance', particularly under oxy combustion conditions. Whilst considerable research has been carried out on the experimental studies and modelling of single biomass particle's ignition and combustion under conventional air combustion conditions, limited work has been undertaken in this area under oxy combustion conditions. This is largely due to the lack of a quantitative means to measure and characterise the combustion behaviours of biomass particles/pellets. In this study, a combination of high-speed and spectroscopic imaging, and image processing techniques is employed to investigate the combustion behaviours of single biomass pellets in a V-DTF (Visual Drop Tube Furnace) under oxy-fuel combustion conditions. Five different biomass pellets (i.e., wood, straw, peanut shell, miscanthus and terrified wood) combust under air and three oxy conditions (i.e., 21%O2/79%CO2, 25%O2/75%CO2, and 30%O2/70%CO2) for the pre-set furnace temperatures of 800 ˚C and 900 ˚C. Images of burning pellets are recorded using a high-speed camera (up to 900 fps) and an EMCCD camera for each test condition. Characteristic parameters of the burning pellet, such as flame size, temperature, are colour features, defined and computed based the images obtained, which are then used to study the impact of the oxy conditions on the combustion behaviour of the tested biomass fuels. The experimental results provide a useful reference for improved understanding in the fundamental aspects of physical and chemical behaviours of biomass fuels under oxy-firing conditions. Fig. 1 illustrates typical images of a miscanthus pellet under air and oxy conditions. Detailed experimental results will be presented at the conference.
  • Lu, G. (2018). Visualisation and measurement of flames in a gas-fired multi-burner boiler. in: IMEKO 2018- The XXII World Congress of the International Measurement Confederation. USA: Institute of Measurement and Control. Available at: http://dx.doi.org/10.1088/1742-6596/1065/20/202009.
    The paper presents the development of an instrumentation system for the visualisation and measurement of flames in a gas-fired multi-burner boiler based on digital imaging and spectrometric techniques. The system consists of a rigid optical probe and an optical fibre, a digital camera, a spectrometer and an embedded computer with associated application software. The characteristic parameters of the flame, including size, temperature and oscillation frequency are quantitatively determined based on flame images obtained. The spectral characteristics of the flame are analysed over the spectral range from the ultraviolet to near infrared. The system was evaluated on a gas-fired heat recovery boiler under different operation conditions. Results obtained suggest the promising correlation between computed flame parameters and operation conditions.
  • Lu, G. (2018). Investigations into the Impact of Coal Moisture on Burner Performance through Flame Imaging and Spectroscopic Analysis. in: 12th ECCRIA - The European Conference on Fuel and Energy Research and its Applications. TFERF- The Fuel and Energy Research Forum.
    Despite increasing use of renewable energy worldwide, coal remains to be the primary energy resource to meet the increasing demand for electric power in many countries. However, coal-fired power plants have to cope with coals with different properties, including those with high moisture content. It is known that moisture content in coal does not only affect coal handling but also burner performance, and thus combustion efficiency and emission formation process. A study is recently carried out to investigate the impact of moisture content in coal on the burner performance through flame imaging and spectroscopic analysis. Experimental tests were conducted on a 40MWth coal-fired combustion test facility (CTF). A typical pulverised coal was fired in the study. The variation in evaporated coal moisture was replicated by injecting steam into the primary coal flow in the range of 7%-55% (PFM, primary flow moisture) under different operation conditions including variations in furnace load and fuel-to-air ratio. A flame imaging system and a miniature spectrometer were employed to acquire concurrently flame images and spetroscopic data (Fig. 1). The characteristic parameters of the flame such as spreading angle, temperature, oscillation frequency and spectral intensity are computed and their relationship with the operation conditions including PFM and emissions (NOx, CO) are quantified. Fig. 2 illustrates typical flame images under different steam injections. Detailed experimental results and analysis will be presented at the conference.
  • Lu, G. (2018). Burner Condition Monitoring based on Flame Imaging and Data Fusion Techniques. in: 12th ECCRIA - The European Conference on Fuel and Energy Research and its Applications. TFERF- The Fuel and Energy Research Forum.
    Rapid growth in electricity generation from intermittent renewables has resulted in increasing demand in conventional fossil-fuel power stations for plant flexibility, load balancing and fuel flexibility. This has led to new challenges in plant monitoring and control, particularly securing combustion stability for optimizing combustion process in terms of furnace safety, fuel efficiency and pollutant emissions. An unstable combustion process can cause many problems including furnace vibration, non-uniform thermal distribution in the furnace, high pollutant emissions and unburnt carbon in the flue gas. The stability of burners should therefore be continuously monitored and maintained for the improved overall performance of the furnace. A study is carried out to investigate the burner stability based on flame imaging and data fusion techniques. Experiments were carried out on a 915 MWth coal-fired power station. A bespoke flame imaging system (Fig. 1) was employed to acquire flame images from 16 individual burners (4 mills each with 4 burners) with a frame rate up to 200 frames per second. The characteristic parameters of the flame, including temperature, non-uniformity, entropy, oscillation frequency and colour characteristics (hue, saturation and intensity), are computed. The relationship between the flame characteristics and burner inputs and flue gas emissions (e.g., NOx) is quantified. Stability index is then introduced as an indicator of the stability of individual burner. Fig. 2 illustrates typical flame images for different burners. Detailed test results and analysis will be presented at the conference.
  • Lu, G. (2018). Flame monitoring and characterisation through digital imaging and spectrometry. in: IFRF 2018 Conference- Clean, efficient and safe industrial combustion. Institute of Measurement and Control.
    Fossil fuel fired boilers are often required to work under variable operation conditions. The variability in fuel diet and load conditions result in various problems in boiler performances. A methodology based on digital imaging and spectrometric techniques is proposed for flame monitoring and characterisation on utility boilers. The system developed consists of an optical probe/water jacket, a digital camera, a spectrometer covering a spectral range from 200nm to 900nm and an embedded computer with associated application software. Computer algorithms are established to determine flame characteristic parameters, including size, shape, temperature and spectral distributions. The spontaneous emissions of flame radicals (e.g., CH*and C2*) and alkali elements such as Sodium (Na) and Potassium (K) are characterised and their relationships with the combustion inputs (e.g., fuel, air-to-fuel ratio) and pollutant emissions (e.g., NOx) are studied. The methodology proposed are examined on a gas-fired heat recovery boiler under different operation conditions. The results obtained suggest there exist close correlations between flame parameters computed and boiler operation conditions. In particular, flame radicals (CH* and C2*) and their ratio show a close relationship with the air-to-fuel ratio. The spectral intensities of Na (589nm) and K (767nm) also illustrate a strong link to the type of fuel. Current work focuses on quantifying the relationship between the flame parameters and the boiler operation conditions and establishing a computational model for online prediction of emissions from flame characteristic parameters.
  • Xiaojing, B. et al. (2016). Multi-mode Combustion Process Monitoring through Flame Imaging and Soft-computing. in: 11th European Conference on Coal Research and its Applications. UK: Coal Research Forum. Available at: http://www.coalresearchforum.org/conference.html.
    Reliable monitoring and diagnosis of combustion stability in combustion systems such as fossil-fuel fired boilers, gas turbines and combustion engines are crucial to maintain the system safety, combustion efficiency and low emissions, particularly under variable operation conditions. Considerable efforts have thus been made in developing techniques for online monitoring and diagnosis of the stability of a combustion process. Among those, flame imaging conjoined with image processing and soft computing techniques has been paid much attention for both laboratorial and industrial applications. Some imaging and soft computing techniques have been proposed for combustion state monitoring, but most of them can only detect a single-mode condition. However, modern combustion systems often operate under variable conditions (i.e., multi-mode process). Due to the dynamic nature of the combustion process, single-mode monitoring methods often mistakenly determine some normal combustion behaviours as abnormal ones. The recent trend of using a variety of fuels, including low quality coals, coal blends, and co-firing biomass and coal, has further deteriorated this issue.
    In this study, a method based on flame imaging and soft-computing techniques for multi-mode combustion process monitoring is proposed. Flame images are acquired using a flame imaging system. Mean intensity values of RGB image components and texture descriptors are extracted and computed from the grey-level co-occurrence matrix. Such features are then used as inputs to a combined PCA-KSVM (principle component analysis-kernel support vector machine) model for multi-mode process monitoring. In this method, the PCA serves for eliminating the impact of noise and instabilities on the mode recognition. The KSVM identifies the combustion mode by using the scores of the features in the principle component subspace. Finally, two multivariate statistic indices, T2 and SPE, are computed and used to assess the stabilities of the combustion process. The proposed approach has been examined by using flame images obtained on the UKCCSRC PACT 250kW PF (pulverised fuel) test rig under different operation conditions (e.g., variations in the primary air and secondary-territory air split). Test results have shown that the computed image features represent well the dynamic behaviours of the flame, and that the PCA-KSVM model has outperformed conventional methods in monitoring the multi-mode combustion process.
  • He, Y., Lu, G. and Yan, Y. (2016). 3-D reconstruction of an axisymmetric flame based on cone-beam tomographic algorithms. in: 10th International Conference on Sensing Technology (ICST). IEEE Xplore. Available at: http://dx.doi.org/10.1109/ICSensT.2016.7796314.
    This paper presents a method of 3-D (three-dimensional) reconstruction of an axisymmetric flame based on cone-beam tomographic algorithms. A FDK-based analytic tomographic algorithm is developed. Computer simulations are undertaken to evaluate the structural similarity between the template and the reconstructed volume so as to evaluate the effectiveness of the algorithm developed. Experimental tests are also conducted using a CCD camera to capture images of a candle flame. The 3-D reconstruction of the flame is then performed. The simulation and experimental results demonstrate the feasibility of the proposed cone-beam based tomographic algorithm for 3-D flame image reconstruction
  • Bai, X. et al. (2016). Measurement of Coal Particle Combustion Behaviors in A Drop Tube Furnace Through High-speed Imaging and Image Processing. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2016). IEEE, pp. 1445-1450. Available at: https://doi.org/10.1109/I2MTC.2016.7520582.
    This paper presents the measurement and characterization of single coal particles in a drop tube furnace through high speed imaging and image processing. A high speed camera coupling with a long distance microscope is employed to acquire the images of the particle during its residence time in the furnace. A set of physical quantities of the particle, including size, shape and boundary roughness, are defined and computed based on the images obtained, which are then used describe the combustion behaviors of the particle. Experimental results show that the combined high speed imaging and image processing technique has provided an effective means for measuring and quantifying the characteristics of single coal particles during combustion.
  • Farias Moguel, O. et al. (2016). Large eddy simulation of a coal flame: estimation of the flicker frequency under air and oxy-fuel conditions. in: 11th European Conference on Coal Research and its Applications.. Available at: http://www.coalresearchforum.org/conference.html.
    Fossil fuel combustion, such as coal combustion, currently meets the majority of the global energy demand; however, the process also produces a significant proportion of the worldwide CO2 greenhouse gas emissions. Further improvement in the efficiency and control of the combustion process is needed, as well as the implementation of novel technologies such as carbon capture and storage (CCS). Oxy-fuel combustion is a very promising CCS technology, where the air in the combustion process is replaced with a mixture of recycled flue gas and oxygen producing a high CO2 outflow that can effectively be processed or stored. The adjustment of the combustion environment within the boiler resulting from the high CO2 concentration will modify the flame characteristics. It is therefore important to evaluate properly the changes of the flame that occur with different flue gas recycle schemes.
    A coal flame is often characterised by its physical parameters, such as the flame size, shape, brightness and temperature, and it can be considered as a stable flame by the presence of ignition and the propagation of the flame. The oscillatory behaviour of a flame can be quantified by the flicker frequency obtained after the instantaneous variations of the flame parameters, and is used as a reference for flame stability.
    Computational Fluid Dynamics (CFD) is widely used to model coal combustion. This work compares the estimated flicker frequency taken from CFD calculations against measurements undertaken at the experimental facilities of the UKCCSRC Pilot-scale Advanced Capture Technology (PACT) located in South Yorkshire, UK. The 250 kW combustion test facility consists of a down-fired, refractory lined cylindrical furnace, which is 4 m in height with a 0.9 m internal diameter. The furnace is fitted with a scaled version of a commercially available Doosan Babcock low-NOx burner.
    The flame physical parameters are approximated from performing a Large Eddy Simulation (LES) using the CFD code ANSYS FLUENT v15. The flicker frequency obtained from the CFD approach is compared against the experimentally measured value from a 2D flame imaging system. A series of oxy-fuel cases are then examined in the same fashion in order to assess their flame stability and the boiler operational limit. The flicker frequency trend obtained from the computations and measurements helps to determine the dynamic response of the flame for different combustion environments, and the results will be applicable in determining the optimal recycle ratio applied in future oxy-fuel systems.
  • Cugley, J. et al. (2016). Flame Characterisation in a Multi-burner Heat Recovery Boiler through Digital Imaging and Spectrometry. in: 11th European Conference on Coal Research and its Applications. Coal Research Forum. Available at: http://www.coalresearchforum.org/conference.html.
    Fossil fuel fired utility boilers fire a range of fuels under variable operation conditions. This variability in fuel diet and load conditions is linked to various problems in boiler performances, particularly the flame quality which is closely associated with furnace safety, combustion efficiency and pollutant emissions. Reliable flame monitoring is thus critical as the flame can fluctuate significantly in terms of size, shape, location, colour and temperature distribution. For instance, heat recovery water tube boilers are commonly used in industry to recover the energy in the exhaust gas from gas turbines. The boiler is fitted with multiple burners which allow flexibility with tuning of the boiler firing rates depending on process steam demand. It was reported that flame properties in such boilers had a direct impact on the flame stability and pollutant emissions (i.e., NOx and CO). There is, however, no technique available for online monitoring and quantifying the flame properties of individual burners. This has resulted in a lack of understanding in how each burner operates with regard to the overall performance of the boiler, particularly the emissions.
    Under the support of the BF2RA and EPSRC, an imaging and spectrometry based instrumentation system is being developed for flame monitoring and emission. Fig 1 shows the block diagram of the system. An optical probe, protected by the air-cooled jacket, transmits the light of flame to the camera house. The light of flame is then split into two beams. The first beam is captured by a camera to provide images for determining the physical parameters of the flame. The second beam is received by a miniature spectrometer for flame spectral analysis. Intelligent computing algorithms are developed for flame monitoring and emission prediction. The system, once fully developed, will be assessed under a range of operation conditions on a heat recovery water tube boiler at a British Sugar’s factory. More test results will be presented at the conference.
  • Li, N. et al. (2015). Prediction of NOx emissions from a biomass fired combustion process through digital imaging, nonnegative matrix factorization and fast sparse. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2015),. pp. 176-180.
  • Hossain, M. et al. (2015). Temperature Measurement of Gas Turbine Swirling Flames using Tomographic Imaging Techniques’. in: IEEE International Conference on Imaging Systems and Techniques 2015 (IST2’015).. Available at: http://dx.doi.org/10.1109/IST.2015.7294541.
  • Hossain, M., Lu, G. and Yan, Y. (2014). Tomographic imaging based measurement of three-dimensional geometric parameters of a burner flame. in: Proceedings of IEEE International Instrumentation and Measurement Technology Conference 2014 (I2MTC 2014). pp. 1111-1114. Available at: http://dx.doi.org/10.1109/I2MTC.2014.6860915.
    This paper presents the measurement of 3-D (three-dimensional) flame geometric parameters based on optical fiber imaging and tomographic techniques. Two identical CCD (Charge-coupled Device) cameras coupled with eight imaging fiber bundles are used to capture the 2-D (two-dimensional) images of a burner flame concurrently from eight different directions around the burner. An optical tomographic algorithm LFBP-SART is utilized to reconstruct the cross-sections and generate a complete 3-D model of the flame. A set of geometric parameters, including length, volume, surface area and circularity, are then determined from the model generated and used for characterizing the flame. The proposed technical approach is firstly evaluated using an LED (light emitting diode) tube with known dimensions, and then on a gas-fired combustion rig. The results obtained demonstrate that the proposed algorithms are effective for measuring the 3-D geometric parameters of a burner flame over a range of combustion conditions.
  • Hossain, M. et al. (2014). 3-D reconstruction and characterisation of oxy-coal flames on a 250kW combustion test facility’. in: 10th European Conference on Coal Research and Its Applications.
  • Li, N. et al. (2014). A multiple linear regression approach to NOx emission prediction based on flame radical imaging and contourlet transform. in: 10th European Conference on Coal Research and Its Applications.
  • Li, N. et al. (2014). Prediction of pollutant emissions of biomass flames using digital imaging, contourlet transform and radial basis function network techniques. in: IEEE International Instrumentation and Measurement Technology Conference. pp. 697-700.
  • Hossain, M., Lu, G. and Yan, Y. (2014). Soot volume fraction profiling of asymmetric diffusion flames through tomographic imaging. in: IEEE International Conference on Imaging Systems and Techniques 2014. pp. 427-431.
  • Li, G., Lu, G. and Yan, Y. (2014). Fire detection using stereoscopic imaging and image processing techniques’. in: IEEE International Conference on Imaging Systems and Techniques 2014 (IST2014). pp. 28-32.
  • Zhang, Y., Lu, G. and Yan, X. (2013). Modeling and control for a class of singular forest resource system. in: Control Conference (CCC), 2013 32nd Chinese. pp. 2126-2131.
    Forest resources is not only an important global ecological system component but also the material basis of human life. The sustainable development of forest resources has become the key to maintain and improve the whole human environment. How to find the scientific and reasonable ways to manage the forest resources and ensure the profit of the manager and keep sustainable development of forest resources have become principal subjects in the research fields of forest development. The dynamic control models of forest resources are established in this paper, FTS controller and optimal performance index of the singular discrete systems will be obtained on the basis of the calculation for the models.
  • Sun, D. et al. (2013). A simple index based quantitative assessment of flame stability. in: IEEE International Conference on Imaging Systems and Techniques. pp. 190-193. Available at: http://dx.doi.org/10.1109/IST.2013.6729689.
    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.
  • Qiu, T., Yan, Y. and Lu, G. (2013). Flame stability monitoring through statistical analysis of the medial axis. in: Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International. pp. 1658-1661. Available at: http://dx.doi.org/10.1109/I2MTC.2013.6555695.
    On-line monitoring of flame stability is crucial as it is closely related to plant safety, combustion efficiency, and pollutant emissions. This paper presents a new method for the stability monitoring of a burner flame using its medial axis. Flame video clips are acquired on a combustion test rig under different combustion conditions. Each frame of the flame video clips is processed and the medial axis of the flame is extracted. The processed medial axes are then superimposed for statistical analysis. The test results show that, although the shape of the flame is randomly fluctuated even under the same combustion condition, some of the statistical shape parameters are relatively stable for the given burner condition. These parameters can be used to indicate the stability of the flame.
  • Sun, D. et al. (2013). Detecting the presence of large biomass particles in pneumatic conveying pipelines using an acoustic sensor. in: Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International. pp. 1487-1490. Available at: http://dx.doi.org/10.1109/I2MTC.2013.6555661.
    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.
  • Hossain, M. et al. (2013). Three-dimensional temperature profiling of oxy-gas burner flames. in: IEEE International Conference on Imaging Systems and Techniques. pp. 91-94. Available at: http://dx.doi.org/10.1109/IST.2013.6729669.
    This paper presents the three-dimensional temperature measurement of an oxy-gas burner flame based on optical tomographic and two-color techniques. Eight two-dimensional (2-D) image projections of the flame are obtained concurrently by multiple imaging fiber bundles and imaging sensors. The LFBP (Logical Filtered Back-Projection) algorithm combined the SART (Simultaneous Algebraic Reconstruction Technique) is utilized to reconstruct the gray-level intensity of the flame based on its 2-D images. The flame temperature distribution is then determined using the reconstructed gray-levels through the two-color pyrometry. Experiments were undertaken under different oxy-fuel conditions. The results show that the flame temperature increases with the O2 concentration and that the temperature distribution of the oxy-fuel flame is more uniform at the root region than that at other regions.
  • Szuhánszki, J. et al. (2013). Experimental investigation of oxy-coal combustion at a 250 kW Combustion Test Facility. in: The 3rd Oxyfuel Combustion Conference.
    ABSTRACT Carbon Capture and Storage (CCS) technology has considerable potential to reduce CO2 emissions of the energy sector to near zero. Therefore it promises to make a major contribution in mitigating climate change, whilst enabling the continued use of fossil fuels over the coming decades. In addition, it will enhance the energy security of nations with significant fossil fuel reserves, and enable those relying on energy imports to maintain a more diverse range of supply (DECC, 2012). Oxy-fuel combustion is one of the most developed CCS technologies and is suitable for near-term deployment (Wall, 2011). However, in order to ensure the success of the first large scale plants, and thereby demonstrate the technical and economic feasibility of the technology, the fundamentals of the oxy-fuel combustion process have to be fully understood.
    In oxy firing atmospheric N2 is substituted with CO2 from the recycled flue gas, in order to increase the exit CO2 concentration and to moderate the flame temperatures within the process. This changes the fundamentals of the combustion process and, as a result, oxy-coal combustion differs from conventional air fired combustion in a number of ways, including coal reactivity, flame characteristics, heat transfer and emissions performance. This paper, which explores the combustion of coal under oxy-fuel conditions in a state of the art 250 kW Combustion Test Facility (CTF), focuses on flame characterisation and heat transfer performance.
  • Sun, D. et al. (2013). On-line automatic detection of wood pellets in pneumatically conveyed wood dust flow. in: The 8th International Symposium on Measurement Techniques for Multiphase Flows (ISMTMF 2013). pp. 71-75. Available at: http://dx.doi.org/10.1063/1.4872088.
    This paper presents a piezoelectric transducer based system for on-line automatic detection of wood pellets in wood dust flow in pneumatic conveying pipelines. The piezoelectric transducer senses non-intrusively the collisions between wood pellets and the pipe wall. Wavelet-based denoising is adopted to eliminate environmental noise and recover the collision events. Then the wood pellets are identified by sliding a time window through the denoised signal with a suitable threshold. Experiments were carried out on a laboratory test rig and on an industrial pneumatic conveying pipeline to assess the effectiveness and operability of the system.
  • Li, X. et al. (2013). On-line identification of biomass fuels based on flame radical and application of support vector machine techniques. in: IET Renewable Power Generation Conference 2013.. Available at: http://dx.doi.org/10.1049/cp.2013.1737.
    In biomass fired power plants, a range of biomass fuels are used to generate electric power. It is vitally important to identify the type of biomass on-line in order to improve combustion efficiency, reduce emissions and ensure the boiler safety. Present research focuses on the on-line identification of biomass fuels using flame radical imaging and SVM (Support Vector Machine) techniques. The characteristic values of flame radicals, including OH*, CN*, CH* and C2*, are extracted and used to reconstruct the SVM for on-line fuel identification. Experimental results obtained on a laboratory-scale biomass-gas-fired combustion test rig demonstrate the effectiveness of the proposed method.
  • Hossain, M., Lu, G. and Yan, Y. (2012). Measurement of flame temperature distribution using optical tomographic and two-color pyrometric techniques. in: Instrumentation and Measurement Technology Conference. IEEE, pp. 1856-1860. Available at: http://dx.doi.org/10.1109/I2MTC.2012.6229354.
    This paper presents the technique which combines optical tomographic and two-color parametric techniques for the three-dimensional (3-D) reconstruction of temperature distribution of a burner flame. Flame images are acquired using eight optical fiber bundles and two RGB CCD (charge-coupled device) cameras from eight different directions on one side of the burner. The new tomographic algorithm which combines the LFBP (Logical Filtered Back-Projection) and SART (Simultaneous Algebraic Reconstruction Technique) is proposed for the 3-D reconstruction of the gray-levels for two primary color (Red and Green) images. The temperature distribution of the flame is then determined from the ratios of the reconstructed gray-levels of the two primary color images based on the two-color principle. Experimental results on a gas fired combustion test rig are also presented and discussed. The results demonstrate that the proposed technique is effective in measuring the 3-D temperature distribution of the flame.
  • Li, X. et al. (2012). Prediction of NOx emissions throughflame radical imaging and neural network based soft computing. in: Imaging Systems and Techniques (IST), 2012 IEEE International Conference. pp. 502-505. Available at: http://dx.doi.org/10.1109/IST.2012.6295594.
    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.
  • Hossain, M. et al. (2012). Three-dimensional reconstruction of flame temperature and emissivity distribution using optical tomographic and two-colour pyrometric techniques. in: Imaging Systems and Techniques (IST), 2012 IEEE International Conference. pp. 13-17. Available at: http://dx.doi.org/10.1109/IST.2012.6295577.
    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.