About

Md Moinul Hossain received his BSc degree in Computer Science and Engineering from Bangladesh and MSc. degree in Wireless Communications and Systems Engineering from the University of Greenwich in 2005 and 2009, respectively, and his PhD degree in Electronic Engineering in the field of Instrumentation and Measurement from the University of Kent, UK, in 2014. In October 2014 he joined the University of Strathclyde as a Research Associate in the Department of Chemical & Process Engineering. He worked as a Research and Development Engineer in the GreenTech Automation Ltd from 2016 to 2017 and during that period he was a Visiting Research Fellow in the ICES Research Group at the University of the Kent. In May 2017 he joined as a Research Associate in the Department of Electronic And Electrical Engineering at the University of Strathclyde. He was an invited Lecturer of the School of Energy and Environment, Southeast University, China in 2015 and 2017, respectively. Also, he was awarded the International Teachers Exchange Scheme Funds in 2015 and 2017, respectively from Southeast University China.

Md Moinul Hossain is a Member of the Institute of Electrical and Electronic Engineers (IEEE) and since January 2018 he is an Editorial Member of IEEE Access Journal.

His main areas of expertise are in Combustion Diagnostics, Sensors, Instrumentation, Measurement, Condition Monitoring, Digital Image Processing, Deep Learning and Solid Oxide Fuel Cells.

Research interests

Combustion Diagnostics, Sensors, Instrumentation, Measurement, Condition Process Monitoring, Digital Image Processing, Deep Learning, Solid Oxide Fuel Cells and Light Field Imaging

Teaching

Teaching responsibilities include: 

  • First Year Engineering Applications Project (First year)
  • Embedded Computer Systems (Third year)
  • Digital Systems Design (Third year)
  • Embedded Real-Time Operating Systems (MSc)

Supervision

PhD supervision topics

  • Flame Volumetric Reconstruction from 2-D Projections through Deep Learning
  • Light Field Imaging for Flame Temperature Measurement
  • Minimising Operational Risks of Power Plant via Data Analytics and Deep Learning
  • Measurement of Flame Radical Emissions through Hyperspectral Imaging
  • Combustion Stability Monitoring through Deep Learning
  • Object Detection through Complex Media (fog/smoke)
  • Three-dimensional Emission Tomography of Flame Chemiluminescence

Professional

Associate Editor of the IEEE Access Journal 

Prizes and Awards

  • International Teachers Exchange Scheme Awards, School of Energy and Environment, Southeast University, Nanjing, China, 05/2015 & 11/2017.
  • Student Travel and Subsistence Bursary by Coal Research Forum (CRF), The 10th ECCRIA (European Conference on Coal Research and its Applications), University of Hull, 09/2014.
  • IEEE Student Travel Grant, IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Graz, Austria, 05/2012.
  • Student Travel and Subsistence Bursary, The 8th Postgraduate Advanced Measurement & Instrumentation Forum, Beihang University, Beijing, China, 10/2011.
  • PhD Research Studentship, Engineering, and Physical Sciences Research Council (EPSRC), University of Kent, UK, 09/2009.
  • Best Project Award on Master of Science in Wireless Communications & Systems Engineering, University of Greenwich, UK, 10/2008.

Publications

Article

  • Stamoulakatos, A., Cardona, J., McCaig, C., Murray, D., Filius, H., Atkinson, R., Bellekens, X., Michie, C., Andonovic, I., Lazaridis, P., Hamilton, A., Hossain, M., Caterina, G. and Tachtatzis, C. (2020). Automatic Annotation of Subsea Pipelines using Deep Learning. Sensors [Online] 20. Available at: http://dx.doi.org/10.3390/s20030674.
    Regulatory requirements for sub-sea oil and gas operators mandates the frequent inspection of pipeline assets to ensure that their degradation and damage are maintained at acceptable levels. The inspection process is usually sub-contracted to surveyors who utilize sub-sea Remotely Operated Vehicles (ROVs), launched from a surface vessel and piloted over the pipeline. ROVs capture data from various sensors/instruments which are subsequently reviewed and interpreted by human operators, creating a log of event annotations; a slow, labor-intensive and costly process. The paper presents an automatic image annotation framework that identifies/classifies key events of interest in the video footage viz. exposure, burial, field joints, anodes, and free spans. The reported methodology utilizes transfer learning with a Deep Convolutional Neural Network (ResNet-50), fine-tuned on real-life, representative data from challenging sub-sea environments with low lighting conditions, sand agitation, sea-life and vegetation. The network outputs are configured to perform multi-label image classifications for critical events. The annotation performance varies between 95.1 and 99.7 in terms of accuracy and 90.4 and 99.4 in terms of F1-Score depending on event type. The performance results are on a per-frame basis and corroborate the potential of the algorithm to be the foundation for an intelligent decision support framework that automates the annotation process. The solution can execute annotations in real-time and is significantly more cost-effective than human-only approaches.
  • Han, Z., Hossain, M., Wang, Y., Li, J. and Xu, C. (2019). Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network. Applied Energy [Online] 259:114159. Available at: http://www.sciencedirect.com/science/article/pii/S030626191931846X.
    Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions. Therefore, it is important to monitor combustion stability for optimizing efficiency and maintaining furnace safety. However, it is difficult to establish a robust monitoring model with high precision through traditional data-driven methods, where prior knowledge of labeled data is required. This study proposes a novel approach for combustion stability monitoring through stacked sparse autoencoder based deep neural network. The proposed stacked sparse autoencoder is firstly utilized to extract flame representative features from the unlabeled images, and an improved loss function is used to enhance the training efficiency. The extracted features are then used to identify the classification label and stability index through clustering and statistical analysis. Classification and regression models incorporating the stacked sparse autoencoder are established for the qualitative and quantitative characterization of combustion stability. Experiments were carried out on a gas combustor to establish and evaluate the proposed models. It has been found that the classification model provides an F1-score of 0.99, whilst the R-squared of 0.98 is achieved through the regression model. Results obtained from the experiments demonstrated that the stacked sparse autoencoder model is capable of extracting flame representative features automatically without having manual interference. The results also show that the proposed model provides a higher prediction accuracy in comparison to the traditional data-driven methods and also demonstrates as a promising tool for monitoring the combustion stability accurately.
  • Qi, Q., Hossain, M., Zhang, B., Ling, T. and Xu, C. (2019). Flame temperature reconstruction through multi-plenoptic camera technique. Measurement Science and Technology [Online] 30:124002 (12pp). Available at: https://doi.org/10.1088/1361-6501/ab2e98.
    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.
  • Liu, J., Hossain, M., Sun, J., Liu, Y., Zhang, B., Tachtatzis, C. and Xu, C. (2019). Simultaneous measurement of flame temperature and absorption coefficient through LMBC-NNLS and plenoptic imaging techniques. Applied Thermal Engineering [Online] 154:711 - 725. Available at: https://dx.doi.org/10.1016/j.applthermaleng.2019.03.130.
    It is important to identify boundary constraints in the inverse algorithm for the reconstruction of flame temperature because a negative temperature can be reconstructed with improper boundary constraints. In this study, a hybrid algorithm, a combination of Levenberg-Marquardt with boundary constraint (LMBC) and non-negative least squares (NNLS), was proposed to reconstruct the flame temperature and absorption coefficient simultaneously by sampling the multi-wavelength flame radiation with a colored plenoptic camera. To validate the proposed algorithm, numerical simulations were carried out for both the symmetric and asymmetric distributions of the flame temperature and absorption coefficient. The plenoptic flame images were modeled to investigate the characteristics of flame radiation sampling. Different Gaussian noises were added into the radiation samplings to investigate the noise effects on the reconstruction accuracy. Simulation results showed that the relative errors of the reconstructed temperature and absorption coefficient are less than 10, indicating that accurate and reliable reconstruction can be obtained by the proposed algorithm. The algorithm was further verified by experimental studies, where the reconstructed results were compared with the thermocouple measurements. The simulation and experimental results demonstrated that the proposed algorithm is effective for the simultaneous reconstruction of the flame temperature and absorption coefficient.
  • Hossain, M. (2019). Investigation and optimization of sampling characteristics of light field camera for flame temperature measurement. Journal of Chinese Physics B [Online] 28:034207-1. Available at: http://cpb.iphy.ac.cn/EN/10.1088/1674-1056/28/3/034207.
    It is essential to investigate the light field camera parameters for the accurate flame temperature measurement because the sampling characteristics of the flame radiation can be varied with the light field camera parameters. In this study, novel indices of the light field camera were proposed to investigate the directional and spatial sampling characteristics of the flame radiation. Effects of light field camera parameters such as focal length and magnification of the main lens, focal length and magnification of the microlens were investigated. It has been observed that the sampling characteristics of the flame are varied with the different parameters of the light field camera. The optimized parameters of the light field camera were then proposed for the flame radiation sampling. The larger sampling angle (23 times larger) is achieved by the optimized parameters compared to the commercial light field camera parameters. A non-negative least square (NNLS) algorithm was used to reconstruct the flame temperature. The reconstruction accuracy was also evaluated by the optimized parameters. The results suggested that the optimized parameters can provide higher reconstruction accuracy for axisymmetric and non-symmetric flame conditions in comparison to the commercial light field camera.
  • Cugley, J., Lu, G., Hossain, M., Yan, Y. and Searle, I. (2018). Visualisation and measurement of flames in a gas-fired multi-burner boiler. Journal of Physics: Conference Series [Online] 1065. Available at: https://dx.doi.org/10.1088%2F1742-6596%2F1065%2F20%2F202009.
    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.
  • Sun, J., Hossain, M., Xu, C. and Zhang, B. (2018). Investigation of flame radiation sampling and temperature measurement through light field camera. International Journal of Heat and Mass Transfer [Online] 121:1281-1296. Available at: https://doi.org/10.1016/j.ijheatmasstransfer.2018.01.083.
    Different light field cameras (i.e., traditional and focused) can be used for the flame temperature measurement. But it is crucial to investigate which light field camera can provide better reconstruction accuracy for the flame temperature. In this study, numerical simulations were carried out to investigate the reconstruction accuracy of the flame temperature for the different light field cameras. The effects of flame radiation sampling of the light field cameras were described and evaluated. A novel concept of sampling region and sampling angle of the light field camera was proposed to assess the directional accuracy of the sampled rays of each pixel on the photosensor. It has been observed that the traditional light field camera sampled more rays for each pixel, hence the sampled rays of each pixel are approached less accurately from a single direction. The representative sampled ray was defined to obtain the direction of flame radiation. The radiation intensity of each pixel was calculated and indicated that the traditional light field camera sampled less radiation information than the focused light field camera. A non-negative least square (NNLS) algorithm was used to reconstruct the flame temperature. The reconstruction accuracy was also evaluated for the different distances from microlens array (MLA) to the photosensor. The results obtained from the simulations suggested that the focused light field camera performed better in comparison to the traditional light field camera. Experiments were also carried out to reconstruct the temperature distribution of ethylene diffusion flames based on the light field imaging, and to validate the proposed model.
  • Sun, J., Hossain, M., Xu, C., Zhang, B. and Wang, S. (2017). A novel calibration method of focused light field camera for 3-D reconstruction of flame temperature. Optics Communications [Online] 390:7 - 15. Available at: https://doi.org/10.1016/j.optcom.2016.12.056.
    This paper presents a novel geometric calibration method for focused light field camera to trace the rays of flame radiance and to reconstruct the three-dimensional (3-D) temperature distribution of a flame. A calibration model is developed to calculate the corner points and their projections of the focused light field camera. The characteristics of matching main lens and microlens f-numbers are used as an additional constrains for the calibration. Geometric parameters of the focused light field camera are then achieved using Levenberg-Marquardt algorithm. Total focused images in which all the points are in focus, are utilized to validate the proposed calibration method. Calibration results are presented and discussed in details. The maximum mean relative error of the calibration is found less than 0.13, indicating that the proposed method is capable of calibrating the focused light field camera successfully. The parameters obtained by the calibration are then utilized to trace the rays of flame radiance. A least square QR-factorization algorithm with Plank's radiation law is used to reconstruct the 3-D temperature distribution of a flame. Experiments were carried out on an ethylene air fired combustion test rig to reconstruct the temperature distribution of flames. The flame temperature obtained by the proposed method is then compared with that obtained by using high-precision thermocouple. The difference between the two measurements was found no greater than 6.7. Experimental results demonstrated that the proposed calibration method and the applied measurement technique perform well in the reconstruction of the flame temperature.
  • Daood, S., Ottolini, M., Taylor, S., Ogunyinka, O., Hossain, M., Lu, G., Yan, Y. and Nimmo, W. (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., Hossain, M., Szuhánszki, J., Daood, S., Nimmo, W., Yan, Y. and Pourkashanian, M. (2017). Multi-mode Combustion Process Monitoring on a Pulverised Fuel Combustion Test Facility based on Flame Imaging and Random Weight Network Techniques. Fuel [Online] 202:656-664. Available at: https://dx.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.
  • Bai, X., Hossain, M., Lu, G., Yan, Y. and Liu, S. (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.
  • Sun, J., Xu, C., Zhang, B., Hossain, M., Wang, S., Qi, H. and Tan, H. (2016). Three-dimensional temperature field measurement of flame using a single light field camera. Optics Express [Online] 24:1118-1132. Available at: https://dx.doi.org/10.1364/OE.24.001118.
    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.
  • Humphries, G., Dunn, J., Lengden, M., Hossain, M., Burns, I. and Black, J. (2015). A simple photoacoustic method for the in situ study of soot distribution in flames. Applied Physics B [Online] 119:709-715. Available at: https://doi.org/10.1007/s00340-015-6132-y.
    This paper presents a simple photoacoustic technique capable of quantifying soot volume fraction across a range of flame conditions. The output of a high-power (30 W) 808-nm cw-diode laser was modulated in order to generate an acoustic pressure wave via laser heating of soot within the flame. The generated pressure wave was detected using a micro-electro-mechanical microphone mounted close to a porous-plug flat-flame burner. Measurements were taken using the photoacoustic technique in flames of three different equivalence ratios and were compared to laser-induced incandescence. The results presented here show good agreement between the two techniques and show the potential of the photoacoustic method as a way to measure soot volume fraction profiles in this type of flame. We discuss the potential to implement this technique with much lower laser power than was used in the experiments presented here.
  • Hossain, M., Myung, J., Lan, R., Cassidy, M., Burns, I., Tao, S. and Irvine, J. (2015). Study on Direct Flame Solid Oxide Fuel Cell Using Flat Burner and Ethylene Flame. ECS Transactions [Online] 68:1989-1999. Available at: http://dx.doi.org/10.1149/06801.1989ecst.
    This paper presents an experimental investigation of direct flame solid-oxide fuel cell (SOFC) by using a flat-flame burner and fuel-rich ethylene/air premixed flames. A direct flame fuel cell (DFFC) setup is designed and implemented to measure electrochemical characteristics of electrolyte supported (i.e., single cell consisting of Ce0.9Ni0.1O2-? anode/GDC electrolyte/LSCF-GDC cathode) fuel cell. The fuel cell temperature and cell performance were investigated by operating various fuel/air equivalence ratios and varying distance between burner surface and the fuel cell. A maximum power density of 41 mW/cm2 and current density of 121 mA/cm2 were achieved. Experimental results suggest that the fuel cell performance was greatly influenced by the flame operating conditions and cell position in the flame. The uniformity of the flame temperature and the fuel cell stability were also investigated and calculations of equilibrium gas species composition were performed.
  • Chalmers, H., Al-Jeboori, M., Anthony, B., Balusamy, S., Black, S., Marincola, F., Clements, A., Darabkhani, H., Dennis, J., Farrow, T., Fennell, P., Franchetti, B., Gao, L., Gibbins, J., Hochgreb, S., Hossain, M., Jurado, N., Kempf, A., Liu, H., Lu, G., Ma, L., Navarro-Martinez, S., Nimmo, W., Oakey, J., Pranzitelli, A., Scott, S., Snape, C., Sun, C., Sun, D., Szuhánszki, J., Trabadela, I., Wigley, F., Yan, Y. and Pourkashanian, M. (2014). OxyCAP UK: Oxyfuel Combustion - academic Programme for the UK. Energy Procedia [Online] 63:504 - 510. Available at: http://www.sciencedirect.com/science/article/pii/S1876610214018700.
    The OxyCAP-UK (Oxyfuel Combustion - Academic Programme for the UK) programme was a £2M 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.
  • Chalmers, H., Al-Jeboori, M., Anthony, B., Balusamy, S., Black, S., Marincola, F., Clements, A., Darabkhani, H., Dennis, J., Farrow, T., Fennell, P., Franchetti, B., Gao, L., Gibbins, J., Hochgreb, S., Hossain, M., Jurado, N., Kempf, A., Liu, H., Lu, G., Ma, L., Navarro-Martinez, S., Nimmo, W., Oakey, J., Pranzitelli, A., Scott, S., Snape, C., Sun, C., Sun, D., Szuhánszki, J., Trabadela, I., Wigley, F., Yan, Y. and Pourkashanian, M. (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.
  • Hossain, M., Lu, G., Sun, D. and Yan, Y. (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.
  • YAN, Y., QIU, T., LU, G., Hossain, M., Gilabert, G. and LIU, S. (2012). Recent Advances in Flame Tomography. Chinese Journal of Chemical Engineering [Online] 20:389 - 399. Available at: http://www.sciencedirect.com/science/article/pii/S1004954112604029.
    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.
  • Hossain, M., Lu, G. and Yan, Y. (2012). Optical Fiber Imaging Based Tomographic Reconstruction of Burner Flames. IEEE Transactions on Instrumentation and Measurement [Online] 61:1417-1425. Available at: http://dx.doi.org/10.1109/TIM.2012.2186477.
    This paper presents the design, implementation, and
    evaluation of an optical fiber imaging based tomographic system
    for the 3-D visualization and characterization of a burner
    flame. Eight imaging fiber bundles coupled with two RGB chargecoupled
    device cameras are used to acquire flame images simultaneously
    from eight different directions around the burner. The
    fiber bundle has 30k picture elements and an objective lens with
    a 92? angle of view. The characteristic evaluation of the imaging
    fiber bundles and the calibration of the system were conducted
    to ensure the accuracy of the system. A new tomographic algorithm
    that combines the logical filtered back-projection and the
    simultaneous algebraic reconstruction technique is proposed to
    reconstruct the flame sections from the images. A direct comparison
    between the proposed algorithm and other tomographic approaches
    is conducted through computer simulation for different
    test templates and numbers of projections. The 3-D reconstruction
    of the cross- and longitudinal-sections of a burner flame from
    image projections obtained from the imaging system was also
    performed. The effectiveness of the imaging system and computer
    algorithm is assessed through experimental tests.

Conference or workshop item

  • Hossain, M. (2019). Combustion Condition Monitoring Through Deep Learning Networks. In: 11th International Conference on Applied Energy 2019. USA: Elsevier, pp. 1-5. Available at: https://www.journals.elsevier.com/applied-energy/call-for-papers/icae2019-the-11th-international-conference-on-applied-energy.
    Combustion condition monitoring is essential in a power plant for maintaining stable operations and operational safety. Therefore it is crucial to develop an intelligent combustion monitoring system. Existing traditional methods not only need a large quantity of labeled data but also require rebuilding monitoring model for new conditions. Aiming these problems, the present study proposes a novel approach combining denoising auto-encoder (DAE) and generative adversarial network (GAN) to monitor combustion condition. By using the learning mechanism of the GAN, the robust feature extraction ability of DAE as a generator is improved. These features are then fed into the Gaussian process classifier (GPC) for condition identification. Especially, newly occurring conditions can be correctly classified by simply training the GPC, rather than training from scratch. Experiments performed on a gaseous combustor indicate that the proposed approach can extract representative features accurately and achieve high performance in combustion condition monitoring with the accuracy of 98.5% for original conditions and 97.8% for the new conditions.
  • Hossain, M. (2019). Improving Global Neighborhood Structure Map Denoising Approach for Digital Images. In: 13th International Conference on Interfaces and Human Computer Interaction. iadis digital library, pp. 207-214. Available at: http://www.iadisportal.org/digital-library/improving-global-neighborhood-structure-map-denoising-approach-for-digital-images#.
    This paper proposes a new noise reduction model for digital images. In the proposed model, the intensity similarity between the center pixel and its neighboring pixels within a certain window for constructing a Global Neighborhood Structure (GNS) using Dominant Neighborhood Structure (DNS) maps of central pixels has been measured. The intensity similarity was calculated by using the Canberra Distance measurement equation; where the conventional GNS map approach used the Euclidean distance principle. To evaluate the performance of the proposed model, several noise attacks were imposed on two public image datasets and experimental results demonstrated that the proposed model outperforms the conventional GNS map based denoising technique by exhibiting higher PSNR and SNR values.
  • Hossain, M. (2018). Simulation of flame temperature reconstruction through multi-plenoptic camera techniques. In: 9th World Congress on INDUSTRIAL PROCESS TOMOGRAPHY. Bath, UK.
    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.
  • Hossain, M., Cugley, J., Lu, G., Smith, D., Kim, I. and Yan, Y. (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.
  • Hossain, M., Cugley, J., Lu, G., Caesar, S., Cornwell, S., Riley, G. and Yan, Y. (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). 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.
  • Farias Moguel, O., Clements, A., Szuhánszki, J., Ingham, D., Ma, L., Hossain, M., Lu, G., Yan, Y. and Pourkashanian, M. (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.
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