Portrait of Professor Yong Yan

Professor Yong Yan

Professor of Electronic Instrumentation
Director of Innovation

About

Yong Yan joined the University of Kent in 2004 as a Professor of Electronic Instrumentation and the Head of Instrumentation and Control Research Group. He was School Director of Research from 2008 to 2018 and has been School Director of Innovation since 2018. He has published more than 430 papers in peer reviewed journals and conference proceedings. His h-index is 42 with over 5600 citations. In recognition of his contributions to pulverised fuel flow metering and burner flame imaging he was named an IEEE Fellow in 2011. He was awarded the Achievement Medal by the IEE in 2003, the Engineering Innovation Prize by the IET in 2006, Alec Hough-Grassby Award by the Institute of Measurement and Control in 2011, and the Best Paper Award in 2016 and the Best Application Award in 2017 both by the IEEE Instrumentation and Measurement Society. He has been a Distinguished Lecturer of the IEEE Instrumentation and Measurement Society since 2012. He is a Council Member of International Flame Research Foundation (IFRF) and a Member of the Executive Committee of the Fuel and Energy Research Forum (FERF). He has been teaching electronic instrumentation and related modules at both undergraduate and postgraduate levels for 26 years and has supervised 28 PhD students to successful completion. 

His main areas of expertise are in Sensors, Instrumentation, Measurement, Condition Monitoring, Digital Signal Processing, Digital Image Processing and Applications of Artificial Intelligence.

Research interests

Conference News - Call For Papers

2019

2018

2017

2016

2015

2014


Teaching

Current Teaching Commitments

  • EL875 Advanced Sensors and Instrumentation Systems 
  • EL849 Research Methods 
  • EL565 Electronic Instrumentation and Measurement Systems 
  • EL562 Computer Interfacing Group Project (Project Management) 
  • EL305 Introduction to Electronics (Analogue Electronics)

Previous Teaching Commitments

  • Research Methodology 
  • Digital Signal Processing 
  • Computer Simulation 
  • Systems Modelling 
  • Data Acquisition with Microcomputers 
  • Electrical Principles and Measurement

Supervision

Professor Yong Yan has supervised 28 PhD students and 3 MPhil/MSc-R students to successful completion. 

PhD research topics

  • CO2 Flow Metering under CCS Conditions through Multi-Modal Sensing and Intelligent Data Fusion
  • Dynamic Signal Processing for Complex Industrial Processes
  • Vibration Monitoring of Rotating Machinery Using Electrostatic Sensors 
  • Measurement of Rotational Speed Using Electrostatic Sensors 
  • Particle Flow Measurement Using Integrated Piezoelectric and Electrostatic Sensor Arrays Incorporating FPGAs
  • Digital Imaging Based Characterisation of Biomass Particles in Power Generation
  • On-line Detection of Large Particles in Fine Dust In Pneumatic Conveying Pipelines Using Acoustic Sensors
  • Advanced Flame Monitoring through Digital Imaging
  • Monitoring of Particulate Emissions through Digital Imaging and Light Scattering
  • Contactless Temperature Measurement of Stored Biomass
  • Monitoring and Characterisation of Large Scale Industrial Fires

Student Prizes and Awards

  • Jiali Wu - The IEEE Instrumentation and Measurement Society 2018 Graduate Fellowship Award for a project entitled “On-line measurement and characterisation of burner flames using electrostatic sensor arrays”.
  • Shuai Zhang - The IEEE Instrumentation and Measurement Society 2016 Graduate Fellowship Award for a project entitled “Characterization of Pneumatically Conveyed Pulverised Fuel in Square-Shaped Pipes Using Electrostatic Sensor Arrays”.
  • Lijuan Wang - The best presentation prize for her paper entitled “Gas-liquid two-phase flow measurement using Coriolis flowmeters incorporating neural networks” at the 9th International Symposium on Measurement Techniques for Multiphase Flows, Hokkaido, Japan, 23-25 September 2015.
  • Lijuan Wang - The IEEE Instrumentation and Measurement Society 2015 Graduate Fellowship Award for a project entitled “Condition monitoring of rotating machinery using electrostatic sensor arrays”.
  • Lijuan Wang - The best graduate poster (First Prize) for her paper entitled “Performance assessment of the rotational speed measurement system based on a single electrostatic sensor” at 2014 IEEE International Instrumentation and Measurement Technology Conference (the flagship conference of the IEEE Instrumentation and Measurement Society).
  • Lingjun (Sally) Gao - The best graduate poster (Second Prize) for her paper entitled “Contour-based image segmentation for on-line size distribution measurement of pneumatically conveyed particles” at 2011 IEEE International Instrumentation and Measurement Technology Conference.

Professional

External Roles

  • Distinguished Lecturer, IEEE Instrumentation and Measurement Society
  • Associate Editor of the IEEE Transactions on Instrumentation and Measurement
  • Editor of Measurement (Journal of the International Measurement Confederation, IMEKO)
  • Member of Editorial Advisory Board of Flow Measurement and Instrumentation
  • Member of Editorial Board of Industrial Combustion Journal
  • Council Member of International Flame Research Foundation (IFRF)
  • Member of the Executive Committee of the Fuel and Energy Research Forum (FERF)
  • Member of the EPSRC (Engineering and Physical Sciences Research Council) Peer Review College
  • Assessor for British Council’s Newton Fund Researcher Links and Institutional Links
  • Assessor for Changjiang (Yangtze River) Scholars Programme of the Ministry of Education of P.R. China  
  • External examiner and assessor for research grant proposals, professorial candidates, PhD and MSc-R theses at institutions in China, India, Saudi Arabia, South Africa and Sweden 
  • External examiner for PhD/MPhil degrees at Cardiff, City, Cranfield, Edinburgh, Glasgow Caledonian, Greenwich, Imperial College, Leeds, Manchester, Nottingham, Sheffield, South Wales, Surrey, Teesside and Westminster Universities
  • Visiting professor at North China Electric Power University, P.R. China

Prizes and Awards

  • 2017 Best Application Award, the IEEE Instrumentation and Measurement Society (I2MTC2017), Torino, Italy.
  • 2016  Best Paper Award (1st Place), IEEE International Instrumentation and Measurement Technology Conference (I2MTC2016), Taipei, Taiwan.
  • 2012 Industrial Award (in the category of protecting the environment), the IEEE Instrumentation and Measurement Society, I2MTC2012, Graz, Austria.
  • 2011 Alec Hough-Grassby Award, the Institute of Measurement and Control, UK.
  • 2009 Rushlight Commendation Award (in the category of fossil fuels), UK.
  • 2007 National Award, Engineering Education Scheme, the Royal Academy of Engineering, UK.
  • 2006 Global Engineering Innovation Award (in the category of Power/Energy), the Institution of Engineering Technology, UK.
  • 2005 Finalist for the National Measurement Award (in the Innovative Measurement Category), DTI/TSB, UK.
  • 2003 Achievement Medal, the Institution of Electrical Engineers, UK.

Research Team News

  • The Kent Instrumentation Team has developed a CO2 flow test facility for CO2 flowmeter calibration and evaluation under CCS (Carbon Capture and Storage) conditions. 气-液两相CO2质量流量测量精度达到1.5%
  • Professor Yong Yan won the "Best Paper Award” at the 2016 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) in Taipei, Taiwan during 23-26 May 2016. 华北电力大学论文在2016年IEEE国际仪表与测量大会上获一等奖
  • Research Student Miss Lijuan Wang wins 2015 IEEE Instrumentation and Measurement Society Graduate Fellowship Award.
  • Newton International Fellow joins Kent Instrumentation Team for two years.


Publications

Article

  • Wang, T. et al. (2018). Rotational Speed Measurement through Image Similarity Evaluation and Spectral Analysis. IEEE Access [Online]:1-12. Available at: https://doi.org/10.1109/ACCESS.2018.2866479.
    Accurate and reliable measurement of rotational speed is desirable in a variety of industries.
    This paper presents a rotational speed measurement system based on a low-cost imaging device with a
    simple marker on the rotor. Sequential images are pre-processed through denoising, histogram equalization
    and circle Hough transform, and then processed by similarity evaluation methods to obtain the similarity
    level of images. Finally, the rotational speed is obtained through Chirp-Z transform on the restructured
    signals. The measurement principle, structure design and performance assessment of the proposed system
    are presented. The effects of different influence factors, including frame rate, marker shape and size,
    algorithm for image similarity evaluation, illumination conditions, shooting angle and photographic
    distance, on the performance of the measurement system are quantified and discussed through a series of
    experimental tests on a laboratory test rig. Experimental results suggest that the system is capable of
    providing constant rotational speed measurement with a maximum relative error of ±0.6% and a
    repeatability of less than 0.6% over a speed range from 100 to 900 RPM. Under varying speed conditions
    the proposed system can achieve valid measurement with a relative error within ±1% over the speed range
    of 300 to 900 RPM.
  • Sun, S. et al. (2018). Real-time Imaging and Holdup Measurement of Carbon Dioxide under CCS Conditions Using Electrical Capacitance Tomography. IEEE Sensors Journal [Online] 18:1-1. Available at: https://doi.org/10.1109/JSEN.2018.2858448.
    This paper presented a method for real-time
    cross-sectional imaging and holdup measurement of gas-liquid
    two-phase carbon dioxide (CO2) flow using electrical
    capacitance tomography (ECT). A high-pressure ECT sensor
    with 12 electrodes was constructed and a dedicated digital ECT
    system with a data acquisition rate of 757 frames per second was
    developed for capacitance measurement. Three widely-used
    image reconstruction algorithms were compared for
    tomographic imaging and phase holdup measurement.
    Experiments were carried out on a DN25 laboratorial scale CO2
    two-phase flow rig at a pressure of 6 MPa for the gaseous mass
    flowrates from 0 to 430 kg/h and liquid mass flowrates at 515,
    1100 and 1900 kg/h. The experimental results show that the
    cross-sectional distribution of two-phase CO2 flow can be
    monitored using the ECT system, which matches well with the
    images captured by a high-speed imaging system. Compared
    with the reference gas holdup obtained by the flowmeters in the
    single phase gaseous and liquid loops, the absolute accuracy of
    the gas holdup measurement can reach 6%, indicating that the
    developed system is promising for real-time monitoring of
    carbon dioxide in CCS transportation pipelines.
  • Li, X. et al. (2018). Biomass Fuel Identification Based on Flame Spectroscopy and Feature Engineering. Proceeding of the CSEE [Online] 38:4474-4481. Available at: http://dx.doi.org/10.13334/j.0258-8013.pcsee.171984.
    Flame spectra contain useful information about combustion and hence the spectral features of flame radicals may be used to identify different biomass fuels. A technique for biomass fuel identification is proposed based on the spectral features of flame radicals, feature engineering and improved support vector machine. The spectral intensity signals of biomass flames and flame radicals (OH*(310.85nm), CN*(390.00nm), CH*(430.57nm) and C2*(515.23nm, 545.59nm)) were acquired using a spectrometer. Feature engineering was built, which can accurately reflect the characteristics of sample category, through feature extraction, feature selection based on Filter and feature learning based on dictionary learning. The support vector machine is used to build the identification model, where radial basis kernel parameter γ and error penalty factor C are optimized using an improved grid search algorithm. Experimental results from a laboratory-scale combustion rig show the effectiveness of the proposed method for the identification of biomass fuel.
  • 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.
  • Yan, Y. et al. (2018). Localization of Multiple Leak Sources Using Acoustic Emission Sensors Based on MUSIC Algorithm and Wavelet Packet Analysis. IEEE Sensors Journal [Online]. Available at: http://dx.doi.org/10.1109/JSEN.2018.2871720.
    Multiple leak sources may occur in a large pressure
    vessel that contains corrosive materials or has been in use for a
    long period of time. Although a variety of leak localization
    methods have been proposed in previous studies, they are capable
    of locating only a single leak source. Methods for simultaneous
    localization of multiple leak sources are desirable in practical
    applications. To address this issue, a novel method using acoustic
    emission (AE) sensors in conjunction with MUltiple SIgnal
    Classification (MUSIC) algorithm and wavelet packet analysis is
    proposed and experimentally assessed. High-frequency AE
    sensors are assembled into a linear array to acquire signals from
    multiple leak sources. Characteristics of the leak signals are
    analyzed in the frequency domain. Wavelet packet analysis is
    deployed to extract useful information about the signals from the
    frequency band of 50 kHz - 400 kHz. The MUSIC algorithm is
    applied to identify the directions of the leak sources through a
    space spectrum function. Leak sources are located based on the
    directions identified by the AE sensor array placed at different
    locations. The performance of the proposed method is evaluated
    through experimental tests on a stainless steel flat plate of 100 cm
    ×100 cm×0.4 cm. The results demonstrate that the method is
    capable of locating two leak holes. In addition, the localization
    accuracy depends on the leaking pressure. It is demonstrated that
    the two leak holes are located within two small areas, respectively,
    which are 25.12 cm2 for leak hole 1 and 1.96 cm2 for leak hole 2.
  • Yan, Y. et al. (2018). Application of Soft Computing Techniques to Multiphase Flow Measurement: A Review. Flow Measurement and Instrumentation [Online] 60:30-43. Available at: https://doi.org/10.1016/j.flowmeasinst.2018.02.017.
    After extensive research and development over the past three decades, a range of techniques have been proposed and developed for online continuous measurement of multiphase flow. In recent years, with the rapid development of computer hardware and machine learning, soft computing techniques have been applied in many engineering disciplines, including indirect measurement of multiphase flow. This paper presents a comprehensive review of the soft computing techniques for multiphase flow metering with a particular focus on the measurement of individual phase flowrates and phase fractions. The paper describes the sensors used and the working principle, modelling and example applications of various soft computing techniques in addition to their merits and limitations. Trends and future developments of soft computing techniques in the field of multiphase flow measurement are also discussed.
  • Adefila, K. et al. (2017). Flow Measurement of Wet CO2 Using an Averaging Pitot Tube and Coriolis Mass Flowmeters. International Journal of Greenhouse Gas Control [Online] 63:289-295. Available at: https://doi.org/10.1016/j.ijggc.2017.06.005.
    The flow measurement of wet-gas is an active field with extensive research background that remains a modern-day challenge. The implication of wet-gas flow conditions is no different in Carbon Capture and Storage (CCS) pipelines. The associated complex flow regime with wet-gas flow makes it difficult to accurately meter the flow rate of the gas phase. Some conventional single-phase flowmeters like the Coriolis, Orifice plate, Ultrasonic, V-Cone, Venturi and Vortex have been tested for this application, usually accompanied with special recommendations. Often, a correlation equation valid within a certain range of specific conditions is required to correct the response of the flowmeter. This paper presents investigations into the suitability and performance of one of the most advanced averaging pitot tubes for the flow measurement of wet CO2 gas. The averaging pitot tube with flow conditioning wing geometry (APT-FCW) was studied and experimentally assessed in earlier work for the flow measurement of pure and dry CO2 within an error of ±1%. Under wet-gas conditions, however, the APT-FCW sensor is found to give an error of up to ±25% and within ±1.5% after appropriate correcting solutions are applied for a liquid fraction of up to 20%.
  • 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. 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.
  • Qian, X. et al. (2017). Effects of moisture content on electrostatic sensing based mass flow measurement of pneumatically conveyed particles. Powder Technology [Online] 311:579-588. Available at: http://dx.doi.org/10.1016/j.powtec.2016.12.061.
    Mass flow rate measurement of pneumatically conveyed particles is desirable for the optimal control of many industrial processes. The unpredicted variation of moisture content in particles affects the accuracy of mass flow measurement of particles in enclosed pipelines using electrostatic electrodes. In this study, the characteristics of measured electrostatic signals from particle flow under different flow conditions are analysed to study the effect of moisture content on the mass flow rate measurement. The measurement principle of ring-shaped electrostatic electrodes, the effects of moisture content on electrification of solid particles, and the experimental setup used in the study are presented. Two types of electrostatic electrodes with different axial widths and structure are adopted to measure the electrostatic signals of nonporous glass beads and porous activated carbon powder on the vertical pipeline of a 74 mm bore gas–solid two-phase flow test rig under various moisture content, mass flow rate and conveying velocity conditions. The experimental results indicate that the amplitude and frequency characteristics of the electrostatic signals change with the moisture content. The deviation of mass flow measurement that caused by the variation of moisture content is analysed, and a recalibration process is demonstrated to be effective for the improvement of measurement accuracy.
  • Sun, J. and Yan, Y. (2017). Non-intrusive Characterisation of Particle Cluster Behaviours in a Riser through Electrostatic and Vibration Sensing. Chemical Engineering Journal [Online] 323:381-395. Available at: http://doi.org/10.1016/j.cej.2017.04.082.
    Particle clusters are important mesoscale flow structures in gas-solid circulating fluidised beds (CFBs). An electrostatic sensing system and two accelerometers are installed on the riser of a CFB test rig to collect signals simultaneously. Cross correlation, Hilbert-Huang transform (HHT), V-statistic analysis, and wavelet transform are applied for signal identification and cluster characterisation near the wall. Solids velocities are obtained through cross correlation. Non-stationary and non-linear characteristics are distinctly exhibited in the Hilbert spectra of the electrostatic and vibration signals, and the cluster dynamic behaviours are represented by the energy distributions of the signal intrinsic mode functions (IMFs). The cycle feature and main cycle frequency of cluster motion are characterised through V-statistic analysis of the vibration signals. Consistent characteristic information about particle clusters is extracted from the electrostatic and vibration signals. Furthermore, a cluster identification criterion for electrostatic signals is proposed, including a fixed and a wavelet dynamic thresholds, based on which the cluster time fraction, average cluster duration time, cluster frequency, and average cluster vertical size are quantified. Especially, the cluster frequency obtained from this criterion agrees well with that from the aforementioned V-statistic analysis. Results from this 3 work provide a new non-intrusive approach to the characterisation of cluster dynamic behaviours and their effects on the flow field.
  • Hu, Y. et al. (2017). A Smart Electrostatic Sensor for On-line Condition Monitoring of Power Transmission Belts. IEEE Transactions on Industrial Electronics [Online] 64:7313-7322. Available at: http://dx.doi.org/10.1109/TIE.2017.2696507.
    On-line condition monitoring of power transmission belts is essential to keep industrial belt-driven equipment functioning smoothly and reliably. This paper presents a smart electrostatic sensor that monitors belt motion through detection of static charge on the belt. A theoretical model is established using the method of moments for calculation of induced charge on strip-shaped electrodes placed in the vicinity of a belt moving both axially and transversely. The sensor unit converts the induced charge into proportional voltage signals using charge amplifiers and measures belt speed and vibration through cross correlation and spectral analysis, respectively. The performance of the smart electrostatic sensor is validated against a photoelectric rotary encoder and a laser displacement sensor. Comparative experimental results show that the belt speed can be measured with a relative error within ±2% over the range 1.7-15.5 m/s. The electrostatic sensor is capable of measuring the frequencies of transverse vibration accurately. Although absolute displacement cannot be measured due to the uncertain level of charge on the belt, the measurement results of relative vibration magnitudes for different modes and at different belt speeds are reasonably accurate.
  • 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.
  • Hu, Y. et al. (2017). On-line Continuous Measurement of the Operating Deflection Shape of Power Transmission Belts Through Electrostatic Charge Sensing. IEEE Transactions on Instrumentation and Measurement.
    The measurement of the operating deflection shape (ODS) of power transmission belts is of great importance for the fault diagnosis and prognosis of industrial belt drive systems. This paper presents a novel method based on an electrostatic sensor array to measure the ODS of a belt moving both axially and transversely. The electrostatic sensor integrates a charge amplifier that converts the induced charge on a strip-shaped electrode into a voltage signal. Finite element simulations are performed to study the sensing characteristics of the sensor and the results reveal that the sensor can respond to vibration displacement. Construction of the ODS is achieved in the frequency domain using the ODS frequency response function. Comparative experimental studies with a high-accuracy laser displacement sensor were conducted on a purpose-built test rig and the results show that the vibration frequencies and their relative magnitudes obtained from both sensors agree well with each other. Experiments conducted over a range of belt axial speeds show that the belt vibrates at frequencies that are well separated and identifiable using a peak picking method. The measured ODSs of the first three vibration modes illustrate that the vibration displacement is larger in the middle of the belt span than at both ends and that the phase shift relative to the reference sensor at each measurement point increases monotonically along the belt running direction. The belt axial speed determines the vibration frequencies and displacement, which reaches the maximum amplitude around the natural frequency of the belt.
    Index Terms – Belt drive; vibration measurement; electrostatic sensor; charge amplifier; sensor array; operating deflection shape; frequency response function.
  • 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.
  • 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.
  • Wang, L. et al. (2017). Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement. Measurement Science & Technology [Online] 28:35305. Available at: https://doi.org/10.1088/1361-6501/aa57d6.
    Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including Partial Mutual Information (PMI), Genetic Algorithm - Artificial Neural Network (GA-ANN) and tree-based Iterative Input Selection (IIS) are applied in this study. Typical data-driven models incorporating Support Vector Machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.
  • Zhang, W. et al. (2017). Measurement of moisture content in a fluidized bed dryer using an electrostatic sensor array. Powder Technology [Online] 325:49-57. Available at: https://doi.org/10.1016/j.powtec.2017.11.006.
    Fluidized bed dryers have been widely applied to dry raw materials or final products due to the advantages of good mixing efficiency and high heat and mass transfer rate. In order to control and optimize the drying process of fluidized bed dryers, it is necessary to develop reliable methods to measure the moisture content of solid particles in the bed. Because of the advantages of non-intrusiveness, simple structure and high sensitivity, an electrostatic sensor array has been developed to monitor the drying process. Experimental investigations were conducted on a lab-scale fluidized bed dryer. The moisture content during the drying process was measured using the sampled particles as reference. It is found that the fluctuation of the electrostatic signals can reflect the change in moisture content. However, the relationship between the fluctuation of the electrostatic signal and the moisture content depends on the air velocity in the dryer. To eliminate the velocity effect on moisture content measurement, a model between the moisture content and the root-mean-square magnitude of the electrostatic signal is established. The effectiveness of the model is validated using experimental results under a range of conditions. The findings indicate that the electrostatic sensor array can measure the moisture content in the bed with a maximum error of ±15%.
  • Zhang, W. et al. (2017). Measurement of Charge Distributions in a Bubbling Fluidized Bed Using Wire-Mesh Electrostatic Sensors. IEEE Transactions on Instrumentation and Measurement [Online] 66:522-534. Available at: https://doi.org/10.1109/TIM.2016.2639238.
    In order to maintain safe and efficient operation of a fluidized bed, electrostatic charges in the bed should be monitored continuously. Electrostatic sensors with wire-mesh electrodes are introduced in this paper to measure the charge distribution in the cross section of the fluidized bed. A Finite Element Model is built to investigate the sensing characteristics of the wire-mesh sensors. In comparison with conventional electrostatic sensors, wire-mesh sensors have higher and more uniform sensitivity distribution. Based on the induced charges on the electrodes and the sensitivity distributions of the sensors, the charge distribution in the cross section of the fluidized bed is reconstructed. However, it is difficult to directly measure the induced charges on the electrodes. A charge calibration process is conducted to establish the relationship between the induced charge on the electrode and the electrostatic signal. Experimental studies of charge distribution measurement were conducted on a lab-scale bubbling fluidized bed. The electrostatic signals from the wire-mesh sensors in the dense phase and splash regions of the bed for different fluidization air flow rates were obtained. Based on the results obtained from the charge calibration process, the estimated induced charges on the electrodes are calculated from the Root Mean Square values of the electrostatic signals. The characteristics of the induced charges on the electrodes and the charge distribution in the cross section under different flow conditions are investigated, which proves that wire-mesh electrostatic sensors are able to measure the charge distribution in the bubbling fluidized bed.
  • Wang, L. et al. (2017). Mass Flow Measurement of Gas-Liquid Two-Phase CO2 in CCS Transportation Pipelines using Coriolis Flowmeters. International Journal of Greenhouse Gas Control [Online] 68:269-275. Available at: https://doi.org/10.1016/j.ijggc.2017.11.021.
    Carbon Capture and Storage (CCS) is a promising technology that stops the release of CO2 from industrial processes such as electrical power generation. Accurate measurement of CO2 flows in a CCS system where CO2 flow is a gas, liquid, or gas-liquid two-phase mixture is essential for the fiscal purpose and potential leakage detection. This paper presents a novel method based on Coriolis mass flowmeters in conjunction with least squares support vector machine (LSSVM) models to measure gas-liquid two-phase CO2 flow under CCS conditions. The method uses a classifier to identify the flow pattern and individual LSSVM models for the metering of CO2 mass flowrate and prediction of gas volume fraction of CO2, respectively. Experimental work was undertaken on a multiphase CO2 flow test facility. Performance comparisons between the general LSSVM and flow pattern based LSSVM models are conducted. Results demonstrate that Coriolis mass flowmeters with the LSSVM model incorporating flow pattern identification algorithms perform significantly better than those using the general LSSVM model. The mass flowrate measurement of gas-liquid CO2 is found to yield errors less than ±2% on the horizontal pipeline and ±1.5% on the vertical pipeline, respectively, over flowrates from 250 kg/h to 3200 kg/h. The error in the estimation of CO2 gas volume fraction is within ±10% over the same range of flow rates.
  • 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)
  • Zhang, S. et al. (2017). Homogenization of the Spatial Sensitivity of Electrostatic Sensors for the Flow Measurement of Pneumatically Conveyed Solids in a Square-Shaped Pipe. IEEE Sensors Journal [Online] 17:7516-7525. Available at: https://doi.org/10.1109/JSEN.2017.2758442.
    The spatial sensitivity of an electrostatic sensor is recognized as an important factor that affects the accuracy of solids flow measurement in a pneumatic conveying pipe. However, the distribution of the spatial sensitivity is generally inhomogeneous due to the physical structure of the electrostatic sensor and the inherent electrostatic sensing mechanism. This paper proposes a sensitivity homogenization method based on differential measurement, i.e., using the differential outputs from two electrodes with different axial widths. The validity of the sensitivity homogenization method for a square-shaped electrostatic sensing head, which consists of strip-shaped electrode arrays with different widths, is validated through mathematical analysis. Furthermore, the response of the electrostatic sensing head incorporating the sensitivity homogenization method to roping flow regimes was evaluated on a gravity-fed solids flow test rig. Results from both modeling and experimental tests indicate that the homogeneity of the spatial sensitivity is improved significantly. The mean non-uniformity of the outputs from the sensing head is 11.7% as a result of the homogenization method.
  • Sun, J. and Yan, Y. (2017). Computational characterisation of intermittent hydrodynamic behaviours in a riser with Geldart A particles. Powder Technology [Online] 311:41-51. Available at: http://dx.doi.org/10.1016/j.powtec.2016.12.060.
    In the riser of a gas-solid circulating fluidised bed (CFB) with Geldart A particles, the multiscale interactions and diverse coherent structures give rise to an important hydrodynamic phenomenon called flow intermittency. In this work, the two-fluid model incorporating the energy minimisation multi-scale (EMMS) drag model is employed to simulate the gas-solid flow in the riser. The predicted fluctuating signals are processed to acquire the intermittency indices, wavelet flatness factors, power spectra of solids volume fraction fluctuation, probability density function (PDF) of wavelet coefficients for solids fluctuating velocity, and PDF of solids volume fraction, based on which the flow intermittency and effects of coherent structures are characterised. The results presented in this paper reveal that the EMMS-based computational fluid dynamics (CFD) simulation in combination with the fluctuating signal analysis provide an in-depth understanding of the intermittent flow behaviours in the riser with Geldart A particles. Particle clusters and particle vortices are identified as typical coherent structures in the riser, and the flow intermittency, caused by the flow field heterogeneity and the presence of coherent structures, is found to be significantly dependent on the radial locations and operation conditions.
  • Wang, L., Yan, Y. and Reda, K. (2017). Comparison of Single and Double Electrostatic Sensors for Rotational Speed Measurement. Sensors and Actuators A: Physical [Online] 266:46-55. Available at: http://dx.doi.org/10.1016/j.sna.2017.09.014.
    Accurate and reliable measurement of rotational speed is crucial in many industrial processes. Recent research provides an alternative approach to rotational speed measurement of dielectric rotors through electrostatic sensing and signal processing. This paper aims to explore the electrostatic phenomenon of rotational machineries, design considerations of the spacing between double electrostatic sensors and effect of dielectric rotors on the performance of the rotational speed measurement systems based on single and double electrostatic sensors. Through a series of experimental tests with rotors of different material types, including polytetrafluoroethylene (PTFE), polyvinyl chloride (PVC) and Nylon, different surface roughness (Ra 3.2 and Ra 6.3) and difference diameters (60 mm and 120 mm), the accuracy and reliability of the two measurement systems are assessed and compared. Experimental results suggest that more electrostatic charge is generated on the PTFE rotors with a larger diameter and coarser surface and hence better performance of the measurement systems. The single-sensor system yields a relative error of within ±1% while the double-sensor system produces an error within ±1.5% over the speed range of 500 - 3000 rpm for all tested rotors. However, the single-sensor system outperforms the double-sensor system at high rotational speeds (>2000 rpm) with a relative error less than ±0.05%.
  • 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.
  • Shi, Q. et al. (2017). Simultaneous measurement of electrostatic charge and its effect on particle motions by electrostatic sensors array in gas-solid fluidized beds. Powder Technology [Online] 312:29-37. Available at: https://doi.org/10.1016/j.powtec.2017.02.014.
    Repeated particle-particle and particle-wall collisions and frictions lead to the generation and accumulation of electrostatic charges in the gas-solid fluidized beds. Variations of electrostatic signals are a rich source of information on particle motions and charging, which have rarely been explored and interpreted. To gain a more comprehensive understanding of the induced electrostatic signals in the fluidized beds, an array of arc-shaped induced electrostatic sensors were attached to the outer wall of a fluidized bed. Combined with cross-correlation method, induced electrostatic voltage signals and correlation velocity of particles were measured simultaneously. It was found that electrostatic charges accumulation restrained the particle motions while the average correlation velocity of particles increased with the amount of injecting liquid antistatic agent. Based on the analyses of induced electrostatic signals, the particle correlation velocity, and the particles charge-to-mass ratio under different charging levels, a predictive model of the average particles charge-to-mass ratio was established. Compared with the results obtained from Faraday cup, the estimated results showed a relative error no more than 40%. Simultaneous measurement of particle correlation velocity and particles charge-to-mass ratio were complemented by arc-shaped induced electrostatic sensors array combined with cross-correlation method.
  • Coombes, J. and Yan, Y. (2016). Measurement of Velocity and Concentration Profiles of Pneumatically Conveyed Particles Using an Electrostatic Sensor Array. IEEE Transactions on Instrumentation and Measurement [Online] 65:1139-1148. Available at: http://doi.org/10.1109/TIM.2015.2494620.
    The ability to monitor the velocity and concentration profiles for the whole diameter of a pipe would allow the complex flow dynamics associated with particles in a pneumatic suspension to be measured. This paper presents a method of online monitoring of the particle velocity and particle concentration for the whole diameter of the pipe for a pneumatic bulk solid conveying system. This is achieved using an array structure of five electrostatic sensors across the whole diameter of the pipe to measure the particle velocity and concentration profiles. Experimental tests were carried out on a laboratory-scale test rig over a range of particle velocities. The results show that the electrostatic sensor array is capable of measuring the multiple velocities and concentrations that occur across the diameter of a pneumatic conveying pipe. Through the analysis of velocity and correlation coefficient data, flow across the diameter of the pipe including that along the pipe wall is determined to have more turbulence than the flow at the center of the pipe.
  • Sun, J. and Yan, Y. (2016). Non-intrusive measurement and hydrodynamics characterization of gas–solid fluidized beds: a review. Measurement Science and Technology [Online] 27:112001. Available at: http://doi.org/10.1088/0957-0233/27/11/112001.
    Gas-solid fluidization is a well-established technique to suspend or transport particles and has been applied in a variety of industrial processes. Nevertheless, our knowledge of fluidization hydrodynamics is still limited for the design, scale-up and operation optimization of fluidized bed reactors. It is therefore essential to characterize the two-phase flow behaviours in gas-solid fluidized beds and monitor the fluidization processes for control and optimization. A range of non-intrusive techniques have been developed or proposed for measuring the fluidization dynamic parameters and monitoring the flow status without disturbing or distorting the flow fields. This paper presents a comprehensive review of the non-intrusive measurement techniques and the current state of knowledge and experience in the characterization and monitoring of gas-solid fluidized beds. These techniques are classified into six main categories as per sensing principles, electrostatic, acoustic emission and vibration, visualization, particle tracking, laser Doppler anemometry and phase Doppler anemometry as well as pressure fluctuation methods. Trend and future developments in this field are also discussed.
  • 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.
  • Cui, X. et al. (2016). Localization of CO2 leakage from transportation pipelines through low frequency acoustic emission detection. Sensors and Actuators A: Physical [Online] 237:107-118. Available at: http://doi.org/10.1016/j.sna.2015.11.029.
    Carbon Capture and Storage is a technology to reduce greenhouse gas emissions. CO2 leak from high pressure CO2 transportation pipelines can pose a significant threat to the safety and health of the people living in the vicinity of the pipelines. This paper presents a technique for the efficient localization of CO2 leakage in the transportation pipelines using acoustic emission method with low frequency and narrow band sensors. Experimental tests were carried out on a lab scale test rig releasing CO2 from a stainless steel pipe. Further, the characteristics of the acoustic emission signals are analyzed in both the time and the frequency domains. The impact of using the transverse wave speed and the longitudinal wave speed on the accuracy of the leak localization is investigated. Since the acoustic signals are expected to be attenuated and dispersed when propagating along the pipe, empirical mode decomposition, signal reconstruction and a data fusion method are employed in order to extract high quality data for accurate localization of the leak source. It is demonstrated that a localization error of approximately 5% is achievable with the proposed detecting system.
  • Zhang, J. et al. (2016). Predicting the amount of coke deposition on catalyst through image analysis and soft computing. Measurement Science & Technology [Online] 27. Available at: http://dx.doi.org/10.1088/0957-0233/27/11/114006.
    The amount of coke deposition on catalyst pellets is one of the most important indexes of catalytic property and service life. As a result, it is essential to measure this and analyze the active state of the catalysts during a continuous production process. This paper proposes a new method to predict the amount of coke deposition on catalyst pellets based on image analysis and soft computing. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and feature extraction, twelve effective features are selected and two best feature sets are determined by the prediction tests. A neural network optimized by a particle swarm optimization algorithm is used to establish the prediction model of the coke amount based on various datasets. The root mean square error of the prediction values are all below 0.021 and the coefficient of determination R 2, for the model, are all above 78.71%. Therefore, a feasible, effective and precise method is demonstrated, which may be applied to realize the real-time measurement of coke deposition based on on-line sampling and fast image analysis.
  • Zhang, W. et al. (2016). Measurement of Flow Characteristics in a Bubbling Fluidized Bed Using Electrostatic Sensor Arrays. IEEE Transactions on Instrumentation and Measurement [Online] 65:703-712. Available at: http://doi.org/10.1109/TIM.2016.2514698.
    Fluidized beds are widely applied in a range of industrial processes. In order to maintain the efficient operation of a fluidized bed, the flow parameters in the bed should be monitored continuously. In this paper, electrostatic sensor arrays are used to measure the flow characteristics in a bubbling fluidized bed. In order to investigate the electrostatic charge distribution and the flow dynamics of solid particles in the dense region, time and frequency domain analysis of the electrostatic signals is conducted. In addition, the correlation velocities and weighted average velocity of Geldart A particles in the dense and transit regions are calculated, and the flow dynamics of Geldart A and D particles in the dense and transit regions are compared. Finally, the influence of liquid antistatic agents on the performance of the electrostatic sensor array is investigated. According to the experimental results, it is proved that the flow characteristics in the dense and transit regions of a bubbling fluidized bed can be measured using electrostatic sensor arrays.
  • Hu, Y. et al. (2016). Non-Contact Vibration Monitoring of Power Transmission Belts Through Electrostatic Sensing. IEEE Sensors Journal [Online] 16:3541-3550. Available at: http://doi.org/10.1109/JSEN.2016.2530159.
    On-line vibration monitoring plays an important role in the fault diagnosis and prognosis of industrial belt drive systems. This paper presents a novel measurement technique based on electrostatic sensing to monitor the transverse vibration of power transmission belts in an on-line, continuous, and non-contact manner. The measurement system works on the principle that variations in the distance between a strip-shaped electrode and the naturally electrified dielectric belt give rise to a fluctuating current output. The response of the sensor to a belt moving both axially and transversely is numerically calculated through finite-element modeling. Based on the sensing characteristics of the sensor, the transverse velocity of the belt is characterized through the spectral analysis of the sensor signal. Experiments were conducted on a two-pulley belt drive system to verify the validity of the sensing technique. The belt vibration at different axial speeds was measured and analyzed. The results show that the belt vibrates at well-separated modal frequencies that increase with the axial speed. A closer distance between the electrode and the belt makes higher order vibration modes identifiable, but also leads to severer signal distortion that produces higher order harmonics in the signal.















    On-line vibration monitoring plays an important role in the fault diagnosis and prognosis of industrial belt drive systems. This paper presents a novel measurement technique based on electrostatic sensing to monitor the transverse vibration of power transmission belts in an on-line, continuous, and non-contact manner. The measurement system works on the principle that variations in the distance between a strip-shaped electrode and the naturally electrified dielectric belt give rise to a fluctuating current output. The response of the sensor to a belt moving both axially and transversely is numerically calculated through finite-element modeling. Based on the sensing characteristics of the sensor, the transverse velocity of the belt is characterized through the spectral analysis of the sensor signal. Experiments were conducted on a two-pulley belt drive system to verify the validity of the sensing technique. The belt vibration at different axial speeds was measured and analyzed. The results show that the belt vibrates at well-separated modal frequencies that increase with the axial speed. A closer distance between the electrode and the belt makes higher order vibration modes identifiable, but also leads to severer signal distortion that produces higher order harmonics in the signal.
  • Yang, Y. et al. (2016). Effects of agglomerates on electrostatic behaviors in gas–solid fluidized beds. Powder Technology [Online] 287:139-151. Available at: http://doi.org/10.1016/j.powtec.2015.10.014.
    This work for the first time shows that both falling polyethylene sheets and small agglomerates significantly affect the electrostatic behaviors in a fluidized bed. By cold model experiments, this work found that V-shaped fluctuations of induced electrostatic potentials were observed as a sheet fell to a certain position, and polarity reversals of induced electrostatic potentials were discovered as some small agglomerates were added and fluidized in the lower part of the bed. Further analysis found that the falling sheet could affect the particle concentration distribution in the bed as well as the surface charges of particles, and these two factors always had opposing effect on the induced electrostatic potential and thus caused V-shaped fluctuations to appear. The reason for the reversal of polarity as small agglomerates were added was the appearance of the positively charged agglomerates in the measuring sensitivity zone. This work opens up new possibilities for agglomerates detection.
  • Adefila, K. et al. (2016). Flow Measurement of CO2 in a Binary Gaseous Mixture Using an Averaging Pitot Tube and Coriolis Mass Flowmeters. Flow Measurement and Instrumentation [Online]. Available at: http://dx.doi.org/10.1016/j.flowmeasinst.2016.12.007.
    To combat the growing emissions of CO2 from industrial processes, Carbon Capture and Storage (CCS) and Carbon Capture and Utilization technologies (CCU) have been accepted worldwide to address these pressing concerns. So as to efficiently manage material and financial losses across the entire stream, accurate accounting and monitoring through fiscal metering of CO2 in CCS transportation pipelines are core and required features for the CCS technologies. Moreover, these technical requirements are part of the legal compliance schemes and guidelines from various regulatory bodies. The CO2 transportation pipelines will likely have multiple inputs from different capture plants, each with varying composition of CO2 and thus introducing impurities into the CO2 stream. The presence of other ordinary or hydrocarbon gases in the CO2 gas stream could affect the functionality of metering instruments by introducing additional errors, particularly in the case of volumetric flowmeters. In this study, volumetric and direct mass measurement methods for the flow measurement of CO2 mixtures using two totally different metering principles are experimentally evaluated. An Averaging Pitot Tube with Flow Conditioning Wing (APT-FCW) and Coriolis mass flowmeters (CMF) are used to assess the flow metering of CO2 in a binary gaseous mixture. Different gases (nitrogen, air, oxygen, argon and propane) are diluted as contaminants into the pure CO2 gas flow for various mass fractions to produce an adulterated mixture of the CO2 gas. Comparative analysis of the measurement results under these flow conditions relative to that of pure CO2 gas show that the measurement error of the APT-FCW sensor increases with the mass fraction of the diluent component, and gases with density closer to that of CO2 have a much lesser effect on the performance of the APT-FCW flow sensor for smaller mass fractions. The CMF proved to be very reliable in the gas combination processes and as a reference meter for the APT-FCW sensor. Further analytical observations are discussed in detail.
  • Hu, Y. et al. (2016). Simultaneous Measurement of Belt Speed and Vibration Through Electrostatic Sensing and Data Fusion. IEEE Transactions on Instrumentation and Measurement [Online] 65:1130-1138. Available at: http://doi.org/10.1109/TIM.2015.2490958.
    Accurate and reliable measurement of belt speed and vibration is of great importance in a range of industries. This paper presents a feasibility study of using an electrostatic sensor array and signal processing algorithms for the simultaneous measurement of belt speed and vibration in an online, continuous manner. The design, implementation, and assessment of an experimental system based on this concept are presented. In comparison with existing techniques, the electrostatic sensing method has the advantages of non-contact and simultaneous measurement, low cost, simple structure, and easy installation. The characteristics of electrostatic sensors are studied through finite-element modeling using a point charge moving in the sensing zone of the electrode. The sensor array is arranged in a 2 × 3 matrix, with the belt running between two rows of three identical sensing elements. The three signals in a row are cross correlated for speed calculation, and the results are then fused to give a final measurement. The vibration modes of the belt are identified by fusing the normalized spectra of vertically paired sensor signals. Experiments conducted on a two-pulley belt-driven rig show that the system can measure the belt speed with a relative error within ±2% over the range 2-10 m/s. More accurate and repeatable speed measurements are achieved for higher belt speeds and a shorter distance between the electrode and the belt. It is found that a stretched belt vibrates at the harmonics of the belt pass frequency and hence agrees the expected vibration characteristics.
  • Sun, J. and Yan, Y. (2016). Characterisation of Flow Intermittency and Coherent Structures in a Gas-Solid Circulating Fluidised Bed through Electrostatic Sensing. Industrial & Engineering Chemistry Research [Online] 55:12133-12148. Available at: http://dx.doi.org/10.1021/acs.iecr.6b03283.
    Flow intermittency and coherent structures are important hydrodynamic phenomena in a gas–solid circulating fluidized bed (CFB). In this work, an electrostatic measurement system based on arc-shaped sensing electrodes is designed and implemented on a CFB test rig. Cross correlation, statistical analysis, wavelet transform, and probability density function (PDF) are applied to the electrostatic signal processing, providing a comprehensive description of the solids velocity, solids holdup, flow intermittency, and coherent structure behaviors. A conditional sampling method is used to extract the coherent structure signals from the electrostatic signals. By comparing the extended self-similarity (ESS) scaling law curves before and after the extraction, the effects of coherent structures on the flow intermittency are further confirmed. Experimental results have demonstrated that the electrostatic signals contain important information about the intermittent hydrodynamic behaviors in a CFB, and the analysis of electrostatic signals through appropriate methods results in an in-depth understanding of the fluidization process.

Conference or workshop item

  • Liu, J. et al. (2018). Investigations into the behaviours of Coriolis flowmeters under air-water two-phase flow conditions on an optimized experimental platform. in: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, pp. 1-6. Available at: https://doi.org/10.1109/I2MTC.2018.8409681.
    Gas-liquid two-phase flow is commonly encountered in many industrial processes due to production requirement or inevitable gas entrainment from various sources. Accurate liquid phase measurement under two-phase conditions is challenging but important as it is the key factor to reduce cost, improve safety or meet legal requirements. Coriolis flowmeters, owing to their high accuracy in metering single-phase flow, direct mass flow measurement and multivariable sensing nature, are widely used in industry. Recently developed Coriolis flowmeters can work under multiphase conditions, making it possible to achieve accurate multiphase flow measurement through model based error compensation or training based soft computing correction. This paper assesses the behaviours of Coriolis flowmeters under various two-phase conditions for modelling and soft computing algorithm improvement, including previously investigated factors (flowrate, gas volume fraction, flow tube geometry, flow converter, and process pressure) and new factors (flow regimes in terms of bubble size and distribution). Experimental work was conducted on 25 mm and 50 mm bore air-water two-phase flow rigs for liquid mass flowrates between 2500 kg/h and 35000 kg/h with gas volume fraction of 0-60%. With the influence of each factor identified through univariate analysis, comparisons between existing modelling theories and experimental error curves are established. In the meantime, the rig design and control are optimized to provide efficient and automated data acquisition in order to supply ample and high-quality data for the training of soft computing models as well as enhancing the understanding in theoretical modelling.
  • Wang, L. et al. (2017). Mass flow measurement of two-phase carbon dioxide using Coriolis flowmeters. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2017).. Available at: http://dx.doi.org/10.1109/I2MTC.2017.7969891.
    Carbon Capture and Storage (CCS) is considered as an important technology to reduce CO2 emission from electrical power generation and other industrial processes. In the CCS chain, i.e. from capture to storage via transportation, it is essential to realize accurate measurement of CO2 flows for the purpose of accounting and potential leakage detection. However, there are some significant challenges for the current flow metering technologies to achieve the specified 1.5% measurement uncertainty in the EU-ETS (European Union - Emissions Trading Scheme) for all expected flow conditions. Moreover, there are very few CO2 flow test and calibration facilities that can recreate CCS conditions particularly two-phase CO2 flow in pipelines together with accurate measurement standards. As one of the most potential flowmeters that may be used in the CCS chain, Coriolis flowmeters have the advantages of direct measurement of mass flow rate regardless of its state (liquid, gas, gas/liquid two-phase or supercritical) in addition to the measurement of temperature and density of CO2 for the characterization of flow conditions. This paper assesses the performance of Coriolis flowmeters incorporating a soft-computing correction method for gas-liquid two-phase CO2 flow measurement. The correction method includes a pre-trained backpropagation neural network. Experimental work was conducted on a purpose-built 25 mm bore two-phase CO2 flow test rig for liquid mass flowrate between 300 kg/h and 3050 kg/h and gas mass flowrate from 0 to 330 kg/h under the fluid temperature of 19~21 °C and pressure of 54~58 bar. Experimental results suggest that the Coriolis flowmeters with the developed correction method are capable of providing the mass flow rate of gas-liquid CO2 flow with errors mostly within ±2% and ±1.5% on horizontal and vertical pipelines, respectively.
  • Wang, L. et al. (2016). Gas-liquid Two-phase Flow Measurement Using Coriolis Flowmeters Incorporating Neural Networks. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2016). IEEE, pp. 747-751. Available at: https://doi.org/10.1109/I2MTC.2016.7520458.
    Coriolis flowmeters are commonly used to measure single phase flow. In recent years attempts are being made to apply Coriolis flowmeters to measure two-phase flows. This paper presents a neural network based approach that has been applied to Coriolis flowmeters to measure both the liquid flow rate and the gas void fraction of a two-phase flow. Experimental tests were conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines. The mass flow rate ranges from 700 kg/h to 14500 kg/h whilst the gas volume fraction is between 0 and 30%. A set of variables, including observed density, apparent mass flow, pressure of the fluid and signals to maintain flow tube oscillation, are considered as inputs to a neural network. Two neural networks are established through training with experimental data obtained from the flow rig on horizontal and vertical pipelines, respectively. The performance of both neural networks is assessed in comparison with the reference readings. Experimental results suggest that the relative errors of the corrected mass flow rate of liquid for the vertical and horizontal installations are no greater than ±1.5% and ±2.5%, respectively. The gas volume fraction is predicted with relative errors of less than ±10% and ±20%, respectively, for vertical and horizontal installations in most cases.
  • Qian, X. et al. (2016). Measurement of the Mass Flow Distribution of Pulverized Coal in Primary Air Pipes Using Electrostatic Sensing Techniques. in: IEEE International Instrumentation and Measurement Technology Conference. IEEE, pp. 488-492. Available at: https://doi.org/10.1109/I2MTC.2016.7520412.
    On-line measurement of pulverized fuel (PF) distribution between primary air pipes on a coal-fired power plant is of great importance to achieve balanced fuel supply to the boiler for increased combustion efficiency and reduced pollutant emissions. An instrumentation system using multiple electrostatic sensing heads are developed and installed on 510 mm bore primary air pipes of a 600 MW coal-fired boiler for the measurement of mass flow distribution of PF. An array of electrostatic electrodes with different axial widths is housed in a sensing head. An electrode with a greater axial width and three narrower electrodes are used to derive the electrostatic signals for the determination of PF mass flow rate and velocity, respectively. The measured PF velocity is used for the calibration of coal mass flow rate. On-plant comparison trials of the developed system were conducted under typical operating conditions. Isokinetic sampling equipment is used to obtain reference data to evaluate the performance of the system. Experimental data demonstrate that the developed system is effective and reliable for the on-line continuous measurement of PF mass flow distribution between the primary air pipes of the same mill.
  • 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.
  • Zhang, W. et al. (2016). Charge Distribution Reconstruction in a Bubbling Fluidized Bed Using a Wire-Mesh Electrostatic Sensor. in: IEEE International Instrumentation and Measurement Technology Conference. IEEE, pp. 28-32. Available at: https://doi.org/10.1109/I2MTC.2016.7520328.
    The presence of electrostatic charge in a bubbling fluidized bed influences the operation of the bed. In order to maintain an effective operation, the electrostatic charges in different positions of the bed should be monitored. In this paper a wire-mesh electrostatic sensor is introduced to reconstruct the charge distribution in a bubbling fluidized bed. The wire-mesh sensor is fabricated by two mutually perpendicular strands of insulated wires. A Finite Element Model is built to analyze the sensing characteristics of the sensor. The sensitivity distributions of each wire electrode and the whole sensor are obtained from the model, which proves that wire-mesh electrostatic sensor has a higher and more uniform sensitivity distribution than single wire sensors. Experiments were conducted in a gravity drop test rig to validate the reconstruction method. Experimental results show that the charge distribution can be reconstructed when sand particles pass through the cross section of the sensor.
  • 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.
  • Hu, Y. et al. (2016). On-line Continuous Measurement of the Operating Deflection Shape of Power Transmission Belts Through Electrostatic Sensing. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2016). IEEE, pp. 872-876. Available at: https://doi.org/10.1109/I2MTC.2016.7520481.
    The measurement of the operating deflection shape (ODS) of power transmission belts is of great importance for the fault diagnosis and prognosis of industrial belt drive systems. This paper presents a novel method based on an electrostatic sensor array to measure the ODS of a belt moving both axially and transversely. Finite element simulations are performed to study the sensing characteristics of a strip-shaped electrode and the results reveal that the transverse velocity determines the sensor signal. Construction of the ODS is achieved in the frequency domain using the ODS frequency response function. Experiments conducted on a purpose-built test rig show that the belt vibrates at resonant frequencies that are well separated and identifiable using a peak picking method. The ODSs for different vibration modes exhibit similar deformation patterns and the axial motion of the belt determines that the ODSs propagate along the belt length, rather than stay fixed in space.
  • 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.

Forthcoming

  • Reda, K. and Yan, Y. (2018). Vibration Measurement of an Unbalanced Metallic Shaft Using Electrostatic Sensors. IEEE Transactions on Instrumentation & Measurement.
    Vibration measurement of a rotary shaft is essential for the diagnosis and prognosis of industrial rotating machinery. However, the imbalance of a shaft, as quantified through vibration displacement, is the most common cause of machine vibration. The objective of this study is to develop a novel technique through electrostatic sensing for the on-line, continuous and non-contact displacement measurement of a rotary shaft due to imbalance faults. A mathematical model is established to extract useful information about the shaft displacement vibration from the simulated signal in the frequency domain. Experimental tests were conducted on a purpose-built test rig to measure the displacement vibration of the shaft. An eccentric shaft was tested with the output signal from the electrostatic sensor analyzed. The effectiveness of the proposed method is verified through computer simulation and experimental tests. Results obtained indicate that the measurement system yields a relative error of within ±0.6% in the displacement measurement.
  • Zhang, W. et al. (2018). Online prediction of biomass moisture content in a fluidized bed dryer using electrostatic sensor arrays and the Random Forest method. Fuel.
    The inherent moisture content in biomass needs to be dried before it is used for energy production. Fluidized bed dryers (FBD) are widely applied in drying biomass and the moisture content should be monitored continuously to maximise the efficiency of the drying process. In this paper, the moisture content of biomass in a FBD is predicted using electrostatic sensor arrays and a random forest (RF) based ensemble learning method. The features of electrostatic signals in the time and frequency domains, correlation velocity and the outlet temperature and humidity of exhaust air are chosen to be the input of the RF model. Model training is accomplished using the data taken from a lab-scale experimental platform and the hyper-parameters of the RF model are tuned based on the Bayesian optimization algorithm. Finally, comparisons between the online predicted and sampled values of biomass moisture content are conducted. The maximum relative error between the online predicted and reference values is less than 13%, indicating that the RF model provides a viable solution to the online monitoring of the fluidized bed drying process.
  • Zhang, W. et al. (2018). Experimental investigations into the transient behaviours of CO2 in a horizontal pipeline during flexible CCS operations. International Journal of Greenhouse Gas Control.
    Power plants with CCS facilities should be operated flexibly because of the variability in electricity demand. Load change, start-up and shutdown will occur during flexible CCS operations. It is necessary to investigate the transient behaviours of CO2 flow in the pipeline during these operations for optimized operation of CCS plants. However, very limited experimental data for gas-liquid two-phase CO2 under CCS conditions are available. As a result, experimental observations of the CO2 transient behaviours were conducted on a CO2 gas-liquid two-phase flow rig. Load change, start-up and shutdown of a CO2 flow process were replicated on the rig. Coriolis flowmeters and high-speed imaging equipment were used to observe the mass flow rate, thermophysical properties and flow regimes of the CO2 flow. There are significant discrepancies in the mass flow rate of two-phase CO2 between the test value and the reference value during the load change. During the start-up operation, the flow regime transits from liquid slug flow to gas bubbly flow and the mass flow rate from the Coriolis flowmeter presents two-step changes. In addition, the depressurization and evaporation of liquid CO2 in the pipeline were observed during the shutdown operation.