Multiphase Flow Metering and Combustion Process Monitoring through Data Driven Modelling

Professor Yong Yan
University of Kent, United Kingdom

Abstract

Over the past ten years various machine learning techniques have been incorporated in a range of measurement systems for multiphase flow metering and combustion process monitoring. Such techniques in conjunction with low-cost sensors and sensor arrays provide either unique solutions to some measurement challenges or offer more cost-effective or complementary options to other possible methods. The established or potential applications of machine learning in measurement and instrumentation appear wide ranging, but the underlining principle, advantages and limitations are very similar. This presentation will review recent advances in the applications of data driven modelling techniques to the measurement of gas-liquid, gas-solids and liquid-solids mixture flows and the advanced monitoring of combustion processes. These include the mass flowrate measurement of air-oil two-phase flow, carbon dioxide two-phase flow, slurry flow and pneumatically conveyed pulverized fuel in a range of industrial sectors. Meanwhile, systems that incorporate machine learning algorithms for the on-line continuous identification of pulverised fuel, burner condition monitoring, and combustion plant optimisation will be introduced. Results from recent experimental programmes and trials on industrial-scale test plants will be reported. 

Biographical Sketch 

Yong Yan is a  Professor of Electronic Instrumentation and Director of Innovation at the School of Engineering, University of Kent, U.K. He received the B.Eng. and M.Sc. degrees in instrumentation and control engineering from Tsinghua University, China in 1985 and 1988, respectively, and the Ph.D. degree in flow measurement from University of Teesside, U.K., in 1992. His research interests include sensors, instrumentation, measurement and condition monitoring. He has published over 480 papers in journals and conference proceedings with an h-index of 45 and 8000 citations. In recognition of his contributions to particle flow metering and burner flame imaging, he was named an IEEE Fellow in 2011 and elected as a Fellow of the Royal Academy of Engineering in 2020. He was awarded the gold medal in 2020 by IEEE Transactions on Instrumentation and Measurement as the most published author of all time from the U.K. He is a Distinguished Lecturer appointed by the IEEE Instrumentation and Measurement Society from 2012-2018 and from 2021-2024.

Agents, IoT, Big Data, and Smart Systems

Professor Weiming Shen

Huazhong University of Science and Technology, China 

Abstract

Originated from distributed artificial intelligence, agents represent an exciting and promising approach to building a wide range of distributed software applications. The Internet of Things (IoT) refers to uniquely identifiable objects as well as their virtual representations in an Internet-like structure. It is related to a number of disciplines and technologies that enable the Internet to reach out into the real world of physical objects and their environments. It has been hailed as the most potentially disruptive technological revolution of our lifetime after the Web and mobile accessibility. It becomes even more promising with smart applications like smart cities, smart Grid, smart factories, smart buildings, smart homes, and smart cars. On the other hand, Big Data is a broad term for data sets so large or complex that traditional data processing technologies are inadequate. It has been considered as a technology and become a very active research area primarily involving topics related to machine learning, database, and distributed computing. Recent developments and fast advancements of Cloud/Fog/Edge Computing, Internet of Things, Cyber-Physical Systems, and Big Data provide new opportunities for applications of intelligent software agents, but also bring a lot of new research challenges. Based on 29 years of first-hand research experience on agents, Internet of Things (IoT), Big Data, and their industrial applications, this talk will provide an overview of agents, IoT and Big Data, including stat-of-the-art and future trends, with a focus on how agents, IoT and Big Data are linked with and applied in various industrial domains and societies.

Biographical Sketch 

Dr. Weiming Shen is a Professor at Huazhong University of Science and Technology (HUST), China. Prior to joining HUST, he worked for 20 years at National Research Council Canada as Research Officer, Senior Research Officer, and Principal Research Officer. He is a Fellow of Canadian Academy of Engineering, Fellow of IEEE, Fellow of Engineering Institute of Canada (EIC). He is an internationally-recognized expert on Agent-Based Collaborative Technologies and Applications. During the past three decades, he has been leading a number of major projects on Agents, IoT, and Big Data with smart applications. He has published several books and over 500 papers in scientific journals and international conferences in the related areas. His work has been cited over 15,000 times with an h-index of 59. He is the Co-Editor-in-Chief of IET Collaborative Intelligent Manufacturing, an Associate Editor or Editorial Board Member of over ten international journals (including IEEE Transactions on Automation Science and Engineering; IEEE Transaction on SMC: Systems; IEEE SMC Magazine; Advanced Engineering Informatics; Computational Intelligence; Intelligent Buildings International; Service Computing and Applications) and served as guest editor for several other international journals. He is the Co-Chair of the IEEE Technical Committee on Computer Supported Cooperative Work in Design, has been Program Committee Co-Chair of the CSCWD conferences since 2001, and served as General Chair/Co-Chair or Program Committee Chair/Co-Chair for over 30 international conferences.

Wearable and Implantable Bioelectronic Devices to Diagnosis and Treat Vital Functions

Professor Mohamad SAWAN

Chair Professor, Westlake University, China

Abstract

Healthcare conditions such as the COVID-19 pandemic issues are calling upon worldwide authorities not only to find solutions but to improve detection mechanisms ahead of time before any new diseases can emerge. The corresponding various diseases are refractory to conventional solutions such as drugs and/or surgical treatment.  Smart medical devices intended for the diagnostic, treatment and prediction of neurodegenerative diseases are becoming most effective way to bring solution to such complex diseases. It is then a multidisciplinary race to bring breakthroughs in mimicking brain when designing learning algorithms and corresponding hardware implementation.  In particular, regular machine learning and advanced deep learning are occupying large parts of emerging healthcare chipsets.  The later are intended to run either wearable or implantable medical devices intended for the diagnostic, treatment and prediction of neurodegenerative diseases. This talk covers artificial intelligence algorithms, circuits and systems intended to implement brain-microsystem interfaces dealing with multidimensional design challenges such as power management, low-power high-data rate wireless communication, and reliable harvesting energy methods. Application-specific system architectures will be demonstrated. Case studies include neurorecording intended for learning about the intracortical vision mechanism, and for spike onset detection of epileptic seizure for foci localization, cortical microstimulation for seizure abortion, and prediction to inform patient about the emergence of epileptic seizures.

Biographical Sketch 

Mohamad Sawan is Chair Professor in Westlake University, Hangzhou, China, and Emeritus Professor in Polytechnique Montreal, Canada.  He is founder and director of the Cutting-Edge Net of Biomedical Research And INnovation (CenBRAIN) in Westlake University, Hangzhou, China. He received the Ph.D. degree from University of Sherbrooke, Canada. He is Co-Founder, Associate Editor and was Editor-in-Chief of the IEEE Transactions on Biomedical Circuits and Systems (2016-2019). He is founder of the Polystim Neurotech Laboratory, and Co-founder of the International IEEE-NEWCAS and the International IEEE-BioCAS Conference. He was General Chair of both the 2016 IEEE International Symposium on Circuits and Systems, and the 2020 IEEE International Medicine, Biology and Engineering Conference (EMBC). He was awarded the Canada Research Chair in Smart Medical Devices (2001-2015), and was leading the Microsystems Strategic Alliance of Quebec, Canada (1999-2018). Dr. Sawan published more than 900 peer reviewed papers, two books, 13 book chapters, and 12 patents and 15 other patents are pending. He received several awards, among them the Zhejiang Westlake Friendship Award, the Qianjiang Friendship Ambassador Award, the Shanghai International Collaboration Award, the Queen Elizabeth II Golden Jubilee Medal, and the Medal of Merit from the President of Lebanon. Dr. Sawan is Fellow of the IEEE, Fellow of the Canadian Academy of Engineering, Fellow of the Engineering Institutes of Canada, and “Officer” of the National Order of Quebec.