Dr Lijuan Wang

Lijuan Wang received her BEng degree in Computer Science and Technology from Qiqihar University, Heilongjiang, China in 2010 and her PhD degree in Measurement and Automation from North China Electric Power University (NCEPU), Beijing, China in 2014. She subsequently obtained a second PhD degree in Electronic Engineering from the University of Kent, Canterbury, UK in 2017. After that she worked at the University of Teesside as a Lecturer in Instrumentation and Control Engineering. She joined University of Kent as a Lecturer in Electronic Engineering in June 2018. She has become the head of Instrumentation and Control Research Group from July 2024. Her main areas of expertise include multiphase flow measurement, condition monitoring of mechanical systems, material identification and measurement, computational modelling (FEM, CFD), electrostatic sensing, hyperspectral imaging and machine learning.

She was awarded the Best Student Poster in 2014 I2MTC (International Instrumentation and Measurement Technology Conference), the Best Presentation Award in 2015 ISMTMF (International Symposium on Measurement Techniques for Multiphase Flows), the Prize for Excellent PhD Thesis by NCEPU, the 2015 IEEE Graduate Fellowship by the IEEE Instrumentation and Measurement Society and the 2019 J. Barry Oakes Advancement Award in recognition of her contributions to the development of soft computing models for multiphase flow measurement. She was also a recipient for the New Investigator Award by the EPSRC in 2022.

She currently acts an associate editor of the Measurement: Energy journal. She served as a guest editor for the special issue of the Measurement Journal on Measurements of Energy and Related Quantities in 2023 and the Young Professionals Rep for the IEEE Instrumentation and Measurement Society from 2019 to 2020.

• Flow measurement and instrumentation
• Condition monitoring of mechanical systems
• Material Identification and measurement
• Electrostatic sensing
• Applications of artificial intelligence

She teaches electronics, sensors, instrumentation, measurement, microcontroller and digital systems design. She also supervises individual and group projects.

PhD research topics

• Gas-solid Flow Measurement in Fluidized Beds through Multi-modal Sensing and Data Modelling

• Measurement of Waste Plastics through Hybrid Sensing and Machine Learning

• CO2 Flow Metering under CCS Conditions using Coriolis Mass Flowmeters and Soft Computing Techniques

• Heath Monitoring of Crops through Hyperspectral Imaging and Machine Learning

• Human Motion Detection through Electrostatic Sensing

• Vibration Measurement of Rotor-bearing Systems using Electrostatic Sensors

• Structural Health Monitoring of Wind Turbines through Dynamic Multispectral Imaging and Deep Learning

Professional Qualifications

• · Fellow of Higher Education Academy· 
• Senior Member of IEEE and the IEEE Instrumentation and Measurement Society·
• Member of IMEKO TC20 (Measurements of Energy & Related Quantities)

Prizes and Awards

• · 2022 New Investigator Award, the UK Engineering and Physical Sciences Research Council.
•  2019 J. Barry Oaks Advancement Award “For contribution to the development of soft computing models for multiphase flow measurement”, IEEE Instrumentation and Measurement Society.
• 2015 IEEE Graduate Fellowship Award, IEEE Instrumentation and Measurement Society.
• 2015 Excellent PhD Thesis, North China Electric Power University.
• 2015 Best Presentation Award, International Symposium on Measurement Techniques For Multiphase Flows, Sapporo, Japan.
• 2014 Best Student Poster Award (1st Place), IEEE International Instrumentation and Measurement Technology Conference, Montevideo, Uruguay.
• 2014 Student Travel Award, IEEE International Instrumentation and Measurement Technology Conference, Montevideo, Uruguay.