- Research Interests
- Research Projects
- Research Supervision
- External Roles
Yong Yan received his BEng and MSc degrees in instrumentation and control engineering from Tsinghua University, Beijing, China in 1985 and 1988, respectively, and his PhD degree in gas-solids flow measurement from the University of Teesside, UK in 1992. Prof. Yan started his academic career in 1988 as an Assistant Lecturer at Tsinghua University. In 1989 he joined the University of Teesside as a Research Assistant. After a short period of postdoctoral research, he worked as a lecturer at Teesside from 1993 to 1996, and then as a senior lecturer, reader and professor, respectively, with the University of Greenwich, UK from 1996 to 2004. He joined the University of Kent in 2004 as a Professor of Electronic Instrumentation and the Head of Instrumentation, Control and Embedded Systems Research Group. He has been Director of Research of the School of Engineering and Digital Arts since 2008. He has published more than 170 papers in peer reviewed journals. His h-index is 39 with over 5200 citations. He is an Associate Editor of the IEEE Transactions on Instrumentation and Measurement. In recognition of his contributions to pulverised fuel flow metering and burner flame imaging, Prof. Yan was named an IEEE Fellow in 2011. He is the first IEEE Fellow in the UK in the field of instrumentation and measurement. 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 has been teaching electronic instrumentation and related modules at both undergraduate and postgraduate levels for 25 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.
University of Kent
Conference News - Call For Papers
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
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
- On-line Particle Sizing of Pulverised Coal and Biomass using Digital Imaging Techniques
- Dynamics and Movement Behaviours of Biomass/Coal Flow
- Multi-Channel Force Measurement using Piezoelectric Triaxial Force Transducers and Charge Multiplexers
- Development of a Multiparameter Assessment Tool for Upper Limb Motion in Neurorehabiliation - A Non-Interventional Study
- Three-Dimensional Visualisation and Quantitative Characterisation of Combustion Flames Using Digital Imaging Techniques
- On-line Measurement of Particle Size Distribution Using Piezoelectric Sensors
- Intelligent Flame Detection Incorporating Burner Condition Monitoring and On-Line Fuel Tracking
- Analysis of Energy Conservation through Product-Integrated Persuasive Feedback using a Smart Sensor in a University Campus
Professor Yong Yan has supervised 28 PhD students and 3 MPhil/MSc-R students to successful completion.
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.
PhD Research Topics
We are recruiting new PhD students to undertake research in the following areas. International PhD students sponsored by their governments or employers are particularly welcome. Candidates should have, or are expecting to obtain a First Class or good 2.1 Honours Degree in Electronic Engineering, Automation, Computer Science, Physics or a related discipline. An appropriate degree at Masters level is an advantage, but not essential. A range of scholarships are available for qualified home and international candidates.
CO2 Flow Metering under CCS Conditions through Multi-Modal Sensing and Intelligent Data Fusion
Project Details: Measurement and monitoring of CO2 flows across the entire CCS chain are essential to ensure accurate accounting of captured CO2 and help prevent leaking during transportation to and from storage sites. The accurate measurement of CO2 is vital to lift the strict regulations from legislative bodies off the full deployment of CCS and create a more positive public perception towards CCS. The significant changes in physical properties of CO2 depending on its state (gas, liquid, two-phase or supercritical) mean that CO2 flows in CCS pipelines are complex by their nature. Meanwhile, impurities in a CO2 pipeline also make the flow more likely in the form of two-phase mixture. However, the vast majority of existing techniques provide only volumetric flow rate or are unsuitable for metering complex CO2 flows. This project aims to develop a cutting-edge technology for the measurement of CO2 flows in CCS pipelines. A prototype multi-modal sensing system incorporating Coriolis flow sensors and intelligent statistical data fusion algorithms will be developed to achieve the mass flow metering of CO2 in CCS pipelines. The new system, once developed, is expected to measure the mass flow rate of CO2 with an error of no greatrer than 1% under single-phase and two-phase confitions. Extensive experimental tests will be conducted on the recently established CO2 flow test facilities at the University of Kent.
Dynamic Signal Processing for Complex Industrial Processes
Project Details: Complex processes become increasingly important and challenging in process manufacturing industries which include chemical, oil & gas, food & beverage, pharmaceutical, and mining industries. In these industries, raw materials are transformed through intermediates to finished products, and a distinctive characteristic of these industries is the flow of materials in their liquid, gas, solid or mixture phases. Conventional sensors are capable of providing some basic process parameters such as flow, density, or viscosity under normal conditions. However, under complex process conditions, they have become increasingly difficult to meet the challenges. This project aims to develop advanced signal processing methods and associated devices to analyse dynamic signals from industrial processes so as to determine complex process parameters. These dynamic signals are essentially vibrational or acoustic signals generated by the interaction between process fluids and pipes. The student will design and implement a new integrated sensing system incorporating the latest vibrational and acoustic sensors. Advanced signal processing algorithms will also be developed to uncover the complex nature of the complex process in real time. Meanwhile, the student will design and implement an experimental system on which the performance of the developed sensing system and algorithms will be evaluated on laboratory-scale test rigs and on industrial-scale test facilities.
Vibration Monitoring of Rotating Machinery Using Electrostatic Sensors
Project Details: Vibration monitoring of rotational machinery is desirable in many industrial processes. There are existing tools available for vibration monitoring, but they are expensive and do not work well under hostile environments. In this project the student will design and implement a novel vibration monitoring system using electrostatic sensors and signal processing techniques. Electrodes in a suitable shape will be used as the sensors. An electronic circuit will be designed and built so that it can derive signals from the sensors. Signal processing techniques will be deployed to analyse the signal in the time and frequency domains. Both amplitude and frequency of the vibration will be derived through signal analysis. Experimental work will be conducted on a test rig in the Instrumentation Laboratory. A commercial vibration device will be used as a reference to assess the performance of the new system. The student is expected to be interested in sensors, instrumentation, digital signal processing and software development.
Measurement of Rotational Speed Using Electrostatic Sensors
Project Details: On-line continuous measurement of rational speed is desirable in many industrial processes where the angular velocity or revolutions per minute of a rotating object should be measured. In this project the student will design and implement a tachometer using electrostatic sensors and correlation signal processing techniques. Metal electrodes in a suitable shape will be used as the sensors. An electronic circuit will be designed and built so that it can derive signals from the sensors. Correlation techniques will be deployed to derive the rotational speed of a moving object (e.g. a motorised wheel) from the sensor signals. The direction of the moving object should also be detected. Experimental work will be conducted on a small test rig in the Instrumentation Research Laboratory. The student is expected to be interested in instrumentation, embedded systems, digital signal processing and software development.
Particle Flow Measurement Using Integrated Piezoelectric and Electrostatic Sensor Arrays Incorporating FPGAs
Project Details: On-line continuous flow measurement of particles in pneumatic conveying pipelines is desirable in many industrial sectors. In this project the student will design and implement an integrated instrumentation system using piezoelectric and electrostatic sensor arrays in conjunction with advanced signal processing and data fusion techniques. A signal conditioning unit will be designed and built so that it can process and denoise the signals from the sensor arrays. A range of measurements will be made by processing the signals, including particle velocity, solids concentration, masss flow rate and particle size distribution. Meanwhile, FPGA technologies will be deployed to process the high number of signals from both types of sensor to achieve better measurement accuracy, fast response, enhanced robustness and compactness. In this project the student will design and implement an FPGA based system (both hardware and software) using piezoelectric and electrostatic sensors and signal processing algorithms. Experimental work will be conducted on a particulate flow test rig in the Instrumentation Laboratory at Kent. The student is expected to be interested in instrumentation, embedded systems, digital signal processing and software development.
Digital Imaging Based Characterisation of Biomass Particles in Power Generation
Project Details: As a renewable energy source biomass is now widely used in electrical power generation. Biomass originates from a range of different sources in a wide variety of forms from untreated biomass (straw, pail kernels, etc) to treated biomass (wood pellets, olive pellets etc) and from cultivated energy crops (miscanthus, willow, etc) to residues and waste-derived fuels. Since biomass differs substantially from coal in terms of size, shape and other physical properties, power plants firing biomass have experienced combustion problems which affect flame stability, emission levels and plant maintenance. It is therefore imperative to quantify the physical characteristics of biomass fuels which will inform the combustion engineers for optimised operation of their plants. Digital imaging has been identified as a cost-effective, non-destructive approach to the requirement. This project aims to develop an imaging based portable system for the quantitative analysis of biomass fuels. The challenges include the design and implementation of an image acquisition system suitable for a diverse range of biomass fuels in terms of size, shape and colour and the development of bespoke computer software for the measurement of characteristic parameters such as particle size and shape distributions and surface properties. An extensive experimental programme will be conducted to evaluate the operability and effectiveness of the system.
On-line Detection of Large Particles in Fine Dust In Pneumatic Conveying Pipelines Using Acoustic Sensors
Project Details: As a renewable energy source biomass is now widely used in electrical power generation. Biomass originates from a range of different sources in a wide variety of forms from untreated biomass (straw, pail kernels, etc) to treated biomass (wood pellets, olive pellets etc) and from cultivated energy crops (miscanthus, willow, etc) to residues and waste-derived fuels. Since biomass differs substantially from coal in terms of size, shape and other physical properties, biomass power plants have experienced a range of problems. For example, wood pellets are a common biomass fuel, which often contains fine wood-dust presenting an explosion hazard. A mechanical separation process can separate fine wood-dust from the wood pellets and then feed directly to the burners. However, the presence of a small proportion of wood pellets in the wood-dust creates combustion problems. It is thus desirable to measure on-line continuously the size distribution of biomass particles in fuel handling pipelines. Acoustic sensors coupled with advanced signal processing algorithms have been identified as a cost-effective, non-destructive approach to the measurement problem. This project aims to develop a novel measurement system for the on-line sizing of biomass fuels. The challenges include the design and implementation of a cost-effective system suitable for a diverse range of biomass fuels. An extensive experimental programme will be conducted at a biomass power plant to evaluate the operability and effectiveness of the system.
Advanced Flame Monitoring through Digital Imaging
Project Details: Fossil fuel fired industrial furnaces are firing a range of fuels (nature gas, pulverized coal and biomass) under variable operation conditions. This variability in fuel diet and load conditions is linked to various problems in combustion performance, particularly the flame quality which is associated with plant safety, combustion efficiency and pollutant emissions. This project aims to develop an advanced flame monitoring technique that can not only measure the key flame properties but also indicate the emissions in flue gas in fossil fuel fired combustion systems. The researcher will conduct theoretical and experimental studies using digital imaging and spectrometric techniques. On-plant trials will be undertaken on coal and biomass fired power stations.
Monitoring of Particulate Emissions through Digital Imaging and Light Scattering
Project Details: It is estimated that the life expectancy of every individual person in the UK is reduced by 7-8 months due to particulate matters in the air with subsequent health costs of £20 billion each year. A range of industrial processes, particularly, those in the combustion, metal, mineral, and chemical industries release more particulates into the atmosphere than other processes. It is therefore imperative to measure accurately the amount of particulate emissions from such industrial processes. This project aims to develop a novel, cost-effective technology capable of monitoring particulate emissions from industrial stacks by combining digital imaging and light scattering techniques. The project has two primary objectives: (1) to develop a demonstration system that can monitor the emissions of particles of variable size distributions from sub-microns to over 100 microns under dry, wet and low dust emission conditions; (2) to study the fundamental characteristics of particulates from typical industrial stacks using the developed technology. The light scattering technique will be used to measure the density of particles smaller than 10 microns whilst the digital imaging technique will be deployed for the measurement of dust density and size distribution of particles greater than 1 micron.
Contactless Temperature Measurement of Stored Biomass
Project Details: As a renewable energy source biomass is now widely used in electrical power generation. Biomass fuels such as wood pellets, olive pellets, straw are often stored in bulk volumes at biomass power plants. Since biomass differs significantly from coal in terms of size, shape and other physical properties, biomass power plants have experienced a range of new technical challenges. The naturally low ignition temperature of biomass has increased the risk of fire at biomass power plants, so the temperature profiling of stored biomass has become essential for safety reasons. Power station operators have used conventional devices such as a matrix of thermocouples to gain the crude profile of a biomass site, but such devices are intrusive and often moved away from their original location or even ripped out by the biomass due to excessive forces exerted on them. A contactless, three-dimensional, cost-effective temperature profiling system is thus desirable. Acoustic sensors coupled with advanced signal processing and image reconstruction algorithms have been identified as a potential approach to resolving the measurement problem. This project aims to develop a demonstration system for the on-line temperature profiling of a biomass storage site. The challenges include investigations into the transmission characteristics of acoustic waves in biomass, reconstruction of the temperature profiles from noisy acoustic data, and design and implementation of a cost-effective system suitable for the installation and operation on a large scale (typically 50x25x5m) biomass pellet pile. An extensive experimental programme will be conducted initially on a biomass storage model in a laboratory environment and then on a biomass power plant.
Monitoring and Characterisation of Large Scale Industrial Fires
Project Details: Monitoring and characterisation of large-scale pool fires have become increasingly important in the field of fire safety and prevention. Quantified information about large-scale pool fires will inform fire engineers to study large scale fire dynamics, develop better fire sprinkler systems, and validate numerical models of industrial fires. With the advent of digital imaging and image processing techniques, vision based monitoring and characterization techniques have the potential to be deployed. The fire imaging system should be capable of measuring a range of fire parameters and suitable for installation on a large scale pool fire test bed in a university or industrial laboratory. Correlations between the measurements and computation modelling results will be established.back to top
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
Digital Signal Processing
Data Acquisition with Microcomputers
Electrical Principles and Measurement
Adjunct 1000-Talent-Plan Scholar at North China Electric Power University, P.R. China
Distinguished Lecturer, IEEE Instrumentation and Measurement Society
Associate Editor of the IEEE Transactions on Instrumentation and Measurement
Member of Editorial Advisory Board of Flow Measurement and Instrumentation
Member of Editorial Board of Industrial Combustion Journal
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 of Nominations 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, Glamorgan, Greenwich, Imperial College, Glasgow Caledonian, Leeds, Manchester, Nottingham, Sheffield, Surrey, Teesside and Westminster Universitiesback to top