Dr Md Moinul Hossain
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.
His research interests include mainly 2D/3D Combustion Diagnostics, Tomographic Techniques, Optical Instrumentation Systems Design and Fabrication, 3D Visualisation, Simulation and Modelling, Digital Signal/Image Processing and Direct Solid-oxide Fuel Cells.
- Automatic Annotation of Subsea Survey Video Using Deep Learning
- Advanced Burner Flame Monitoring through Digital Imaging
- Application of Optical Measurement Techniques to Improve Gas Furnace Efficiency
- Flame Solid Oxide Fuel Cells, Simple Devices to Extract Electricity Directly from Natural Gas and Liquid Petroleum Gas Flames
- In-depth Studies of Oxy-coal Combustion Processes through Numerical Modelling, Optical Tomographic (OT) Measurements, and 3D Flame Imaging Techniques
- Three-Dimensional Visualization and Quantitative Characterisation of Oxyfuel Flames using Optical Tomography (OT) and Digital Imaging Processing Techniques
Sun, J. et al. (2018). Investigation of flame radiation sampling and temperature measurement through light field camera. International Journal of Mass and Heat 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. et al. (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. 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. 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.
Sun, J. et al. (2016). Three-dimensional temperature field measurement of flame using a single light field camera. Optics Express [Online] 24:1118-1132. Available at: https://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.
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.
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.