Raffaella Ocone

Heriot-Watt University, UK

Raffaella Ocone obtained her first degree in Chemical Engineering from the Università di Napoli, Italy and her MA and PhD in Chemical Engineering from Princeton University, USA. She holds the Chair of Chemical Engineering in the School of Engineering and Physical Sciences at Heriot-Watt University (HWU) since 1999. She is a Fellow of the Royal Academy of Engineering, the Royal Society of Edinburgh, the Institution of Chemical Engineers, and the Royal Society of Chemistry. In 2007 she was appointed Cavaliere (Knight) of the Order of the Star of Italian Solidarity by the President of the Italian Republic. In The Queen’s 2019 New Year Honours she was appointed OBE for services to Engineering. Recently she has been announced as one of the top 100 Most Influential Women in the Engineering Sector.

At HWU, she is the Head of the Multiphase Multiscale Engineering Modelling (MMEM) research group. Raffaella has worked in a number of highly recognised international Institutions such as the Università di Napoli (Italy); Claude Bérnard Université, Lyon (France); Louisiana State University (USA); Princeton University (USA). She was the first engineering “Caroline Herschel Visiting Professor” at RUHR Universität, Bochum, Germany (July-November 2017) and the recipient of a Visiting Research Fellowship from the Institute for Advanced Studies, Università di Bologna, Italy (March-April 2018). Raffaella’s main area of research is in the field of modelling complex (multi-phase) reactive systems. Raffaella has taken a leading role in debating the role that ethics plays in engineering. Currently she is the EPSRC Established Career Fellow in Particle Technology.

Responsible Technology: are we ready for it?

Abstract:Technology is at the heart of the world where we live providing, among other things, energy solutions, assuring food and drinking water, generating electricity, goods and services. Emerging technologies rise fast carrying the potential to deliver economic and social benefits to a world that is challenged to sustain 10 billion people. Technological and scientific achievements pose challenges and opportunities. The exponential growth of computers, communication and artificial intelligence, for example, is changing the way we work and think, impacting on human activities and ways of living. This talk will explore how global responsibility is embedded in technological solutions and how the ethical dimension affects the way scientists and engineers work and operate.

Jinlong Gong

Tianjin University, China

Jinlong Gongreceived B.S. (2001) and M.S. (2004) degrees from Tianjin University and a Ph.D. (2008) degree from the University of Texas at Austin (with C. B. Mullins), all in chemical engineering. After a stint with Professor George M. Whitesides as a postdoctoral research fellow at Harvard University, he joined the faculty of the School of Chemical Engineering and Technology at Tianjin University in 2010, where he currently holds a Cheung Kong Chair Professorship. He is a Vice President of Tianjin University. He has served on the editorial boards for several journals including Chemical Society Reviews, Chemical Science, and AIChE Journal. He also serves as an Associate Editor for ACS Sustainable Chemistry & Engineering. He is an elected Fellow of the Royal Society of Chemistry. He received ACS Sustainable Chemistry & Engineering Lectureship Award (2017), Lectureship Award in Osaka-Kansai International Symposium on Catalysis (2017), The SCEJ (Society of Chemical Engineers, Japan) Award for Outstanding Asian Researcher and Engineer (2017), Xplorer Prize (2019). He is among the Chinese Most Cited Researchers (Elsevier) (2018) and Clarivate Highly Cited Researchers (2019).

Artificial Leaf

Abstract: "Artificial Leaf" is a promising route for the efficient conversion of CO2 and H2O into chemicals and fuels such as methanol and methane. To construct an efficient artificial leaf, it is necessary to find suitable catalysts to solve the important problems of carbon dioxide reduction reaction (CO2RR) system: reaction kinetics, CO2 activation, stability and selectivity. Here, we study the metal electrocatalysts (PdAu and AuCu) and metal oxides catalysts (Sn-based oxides and Cu-based oxides) systematically. Through the guidance of Density Functional Theory, the reaction kinetics can be enhanced though synergistic ensemble and ligand effects, which utilizes neighboring Pd-Au sites and electron transfer between them. Meanwhile, CO2 can be activated efficiently via the construction of Cu vacancies at an abrupt surface over a dealloyed AuCu catalyst, as well as the introduction of oxygen vacancies over SnO2. Moreover, maintaining a moderate surface coverage of hydroxyl could solve the stability problem, since an appropriate amount of surface hydroxyl groups offers effective sites to boost CO2 adsorption via hydrogen bond and CO2 aqueous solution, which stabilizes surface hydroxyl groups on cathode. Finally, the selectivity of specific products can be improved via the adjustment of adsorption strength of key intermediates. By achieving a balanced adsorption of H over CO on Cu, the selective formation of CH3OH could be promoted significantly. These findings revealed that the process of catalyst design could be largely simplified by theoretical calculations, which also suggests a logical extension to other catalysts for CO2RR.

Zhongjun Hou

Shanghai Hydrogen Propulsion Technology Com. Ltd., China

Dr. Zhongjun Hou received his doctor degree in chemical engineering from Dalian institute of chemical physics, CAS in 2003. He spent his career in the field of fuel cell technology, including being Chief Engineer and Deputy General Manager at Sunrise Power & National Engineering Research Center of Fuel Cell & Hydrogen Technology until 2018, and Vice general manager of Shanghai Hydrogen Propulsion Technology. He is the Winner of the national leader in science and technology innovation in the "Ten Thousand People Plan", and the State Council government special allowance experts. He had finished several Chinese “863 project” and participated in one Chinese “973 project”. He had published and co-published more than 20 papers and 60 patents.

Fuel cell activities in SAIC and technical challenge for PEMFCs

Abstract:The development of fuel cell electric vehicles (FCEVs) was performed in Shanghai Automotive Industry Company (SAIC) for nearly two decades. The performance, durability and reliability of the FCEVs were validated to meet the requirement of commercial application. To promote the FCEVs’ market extension, the cost, durability and performance of fuel cell should be further improved. The challenges against the fuel cell technology’s enhancement were discussed accordingly.

Markus Kraft

University of Cambridge, UK

Prof Markus Kraft is a Fellow of Churchill College Cambridge and Professor in the Department of Chemical Engineering and Biotechnology. He is the director of CARES, the Singapore-Cambridge CREATE Research Centre, and Principle Investigator of C4T the “Cambridge Centre for Carbon Reduction in Chemical Technology”, which is a CARES research programme. Professor Kraft obtained the academic degree 'Diplom Technomathematiker' at the University of Kaiserslautern in 1992 and completed his Doctor rerum naturalium in Chemistry at the same University in 1997. Subsequently, he worked at the University of Karlsruhe and the Weierstrass Institute for Applied Analysis and Stochastics in Berlin. In 1999 he became a lecturer in the Department of Chemical Engineering, University of Cambridge. In 2012 he obtained a ScD form the same University. He has a strong interest in the area of computational modelling and optimisation targeted towards developing CO2 abatement and emissions reduction technologies for the automotive, power and chemical industries.

Intelligent Decarbonisation

Abstract:Global warming caused by greenhouse gases have caused great concern for a number of reasons. It is clear that drastic changes have to be implemented in the near future to reduce or stop the increase of average temperature and the many negative consequences that go with it. In my talk I shall concentrate on AI-based Cyberphysical systems and knowledge graphs. The decarbonisation of energy provision is key to managing global greenhouse gas emissions and hence mitigating climate change. Digital technologies such as big data, machine learning, and the Internet of Things are receiving more and more attention as they can aid the decarbonisation process while requiring limited investments. The orchestration of these novel technologies, so-called cyber-physical systems (CPS), provides further, synergetic effects that increase efficiency of energy provision and industrial production, thereby optimising economic feasibility and environmental impact. This comprehensive review article assesses the current as well as the potential impact of digital technologies within CPS on the decarbonisation of energy systems. Ad-hoc calculation for selected applications of CPS and its subsystems estimates not only the economic impact but also the emission reduction potential. This assessment clearly shows that digitalisation of energy systems using CPS completely alters the marginal abatement cost curve (MACC) and creates novel pathways for the transition to a low-carbon energy system. Moreover, the assessment concludes that when CPS are combined with artificial intelligence (AI), decarbonisation could potentially progress at an unforeseeable pace while introducing unpredictable and potentially existential risks. The cyber-physical system we are currently developing is called J-Park Simulator (JPS) which is the signature project in the C4T programme of CARES at the University of Cambridge and part of the http://www.theworldavatar.com/ project. JPS consists of a network of IRIs comprising domain ontologies, a knowledge base and different types of agents. One important application is the modelling and optimisation of eco-industrial parks. This includes the electrical grid, various networks of materials, for example, waste heat network along with a detailed model of each industrial process. In my talk I shall explain how JPS works and show a couple of examples.

Xianguo Li

University of Waterloo, Ontario, Canada

Dr. Xianguo Li is a Professor of Mechanical and Mechatronics Engineering, and a University Research Chair, at the University of Waterloo. Dr. Li is internationally recognized for his research in the area of fuel cells, liquid fuel atomization and sprays, and green energy systems. His book, Principles of Fuel Cells, is the world’s first textbook on fuel cells and is used worldwide. Dr. Li has more than 210 journal and 240 conference publications. He has also authored/co-authored 4 books, over 20 book chapters, and 13 patent applications. His published articles have received extensive citations, with an H index of over 60. Dr. Li serves as the editor in chief for the International Journal of Green Energy, and also established the International Green Energy Conference series. He serves on the editorial/advisory board for more than 20 journals, book series, encyclopedia and handbooks. He is the founding division chair for the CSME Advanced Energy Systems Division, CSME Vice President Technical Program; President of the Fuel Cell Division of the International Association for Hydrogen Energy, and has also served as guest editors for a number of journals. He is a fellow of Canadian Academy of Engineering, Engineering Institute of Canada, and Canadian Society for Mechanical Engineering (CSME).

Energy 4.0:
Evolution and Revolution of Energy Systems

Abstract:Energy is essential for the very existence, development and prosperity of human society and human civilization. The evolution and revolution of energy systems have been accompanied with every significant improvement (or quantum jump) in human civilization. However, energy is a double-edge sword, while it brings wealth and welfare to our society, its side effect has significant negative social, economic and environmental impact, including the pressing issue of climate change encompassing global warming and climate variability leading to extreme weather conditions with severe damages. The energy system has evolved through three stages that is being referred to as Energy 1.0, 2.0 and 3.0; and is in the transition process to Energy 4.0 where energy resources are efficiently utilized with cost effectiveness, sustainability and environmentally friendliness, energy connectivity and energy-mass (materials) interconnection and interchange – all these can be achieved through hydrogen as the carrier for both energy and materials. Hydrogen provides the possibility of clean, efficient, reliable and versatile connection to meet the need of future civilization for sustainable human civilization.

Deborah Logan

Elsevier, France

Deborah Logan is Publishing Director for Elsevier’s Energy & Earth journals’ programme, which is the largest global publishing programme in the energy and earth sciences, and which includes many flagship titles publishing world-class content. Over the past few years, Deborah has looked to develop extensive publishing collaborations with China, with a strong focus on recruiting journal editors with high standards of excellence and in launching new journals that will shape and serve the future energy needs of our global society. Deborah is based in Paris and has been working with Elsevier since 2006. Before then, she worked at Oxford University Press in UK; at a non-governmental agency in Kenya; with the Japanese Ministry of Education; and at Sony in Japan. Deborah’s passions lie in raising standards, championing excellence, and promoting greater diversity in science.

Publishing in Energy & AI: trends and perspectives

Abstract:The way science is disseminated is changing, with journal publishing seeing significant changes in recent years. These changes have a noticeable impact on how academic research will be written, published, promoted, and used in the future. Artificial Intelligence in the energy research field is a fast-growing area with strong support from government policy and funding bodies. Strong content growth from published research is expected in the coming years.

Understanding publishing trends and developments in this exciting new area is an important part of the energy research cycle. This talk will cover 3 areas that can equip researchers with more insight on energy and AI trends; some factors to consider when presenting your work; and current and future developments in publishing. Deborah Logan will cover specific elements of each area and point participants to tools available for further guidance.

Henrik Madsen

Technical University of Denmark, Denmark

He got a PhD in Statistics at the Technical University of Denmark in 1986. He was appointed Ass. Prof. in Statistics in 1986, Assoc. Prof. in 1989, and Professor in Mathematical Statistics with a special focus on Stochastic Dynamical Systems in 1999. In 2017 he was appointed Professor II at NTNU in Trondheim. His main research interest is related to analysis and modelling of stochastic dynamics systems. This includes signal processing, time series analysis, identification, estimation, grey-box modelling, forecasting, optimization and control. Since 1992 he has been the leader of one of the most active research groups in Europe in relation to wind and solar power forecasting, as well as methods for operation of power system with a large penetration of fluctuating renewable energy production.

He has got several awards. Lately, in June 2016, he has been appointed Knight of the Order of Dannebrog by Her Majesty the Queen of Denmark, and he was appointed Doctor HC at Lund University in June 2017.

He has authored or co-authored approximately 550 papers and 12 books. The most recent books are Time Series Analysis (2008); General and Generalized Linear Models (2011); Integrating Renewables in Electricity Markets (2013), and Statistics for Finance (2015).

Accelerating the Green Transition Using AI and Energy Systems Integration

Abstract:The energy system needs to undertake a fundamental change from a system where production follows demand to a system where the demand follows the production provided by fluctuating renewable energy sources. This talk describes methodologies for accelerating the green transition using AI, big data analytics, grey-box models, IoT, Edge and Cloud Computing. First of all we shall focus on methods for characterizing and enabling the energy flexibility at the prosumer level, ie. at buildings, supermarkets, wastewater treatment plants, etc. Secondly we will describe a framework, called the Smart-Energy Operating-System, for using this flexibility for controlling the power load in integrated energy systems. Furthermore, this framework contains a set of methodologies which can be used for providing ancillary services (like frequency control, voltage control, and congestion management) for power networks with a large penetration of wind and solar power. The set of methodologies is based on grey-box modeling, forecasting, optimization and control for integrated (power, gas, thermal) energy systems. We will demonstrate that by carefully selecting the cost function associated with the optimal controllers, the system can ensure energy, cost and emission efficiency. Consequently, by using online-predicted values of the CO2 emission of the related power production, the framework provides an AI-based method to accelerate the transition to a fossil-free society.

Hui Xie

Tianjin University, China

Prof. Hui Xie received his PhD in propulsion machine and engineering at Tianjin University in 1998, and now he holds a position as professor and vice director in State Key Laboratory of Engines at Tianjin University, also as director of Autonomous Driving Cross-research Platform. His research interests include intelligent control of engine, powertrain and vehicle, autonomous driving vehicle and big data analysis. His research achievements include advanced intelligent control algorithms of engines, multi-core hardware control architecture and self-optimization energy management methods. He published 80+ papers and 30+ authorized invention patents. He got 2014 National Educating Achievement Award, 2018 China machinery industry science and technology award and 2019 Tianjin government science and technology award.

Self-optimization method of HEV energy management with Reinforcement Learning

Abstract: HEV is an effective powertrain to improve vehicle energy efficiency. How to carry out self-adaptive energy management according to the difference between driver and driving condition is the main challenge for hybrid vehicles to improve energy efficiency. A new energy management framework based on driver behavior observation and driving cycle prediction is proposed, in which, a reinforcement learning method is used to make energy management strategy adapt to the changes of driving cycle, and make the energy efficiency of vehicle system reach optimization by itself. The effectiveness of this method is verified by HIL test.

Jiujun, Zhang

Shanghai University, China

Dr. Jiujun Zhang is a Professor, Dean of the College of Sciences and Dean of Institute for Sustainable Energy at Shanghai University. He is a former Principal Research Officer at the National Research Council of Canada (NRC), Fellow of Academy of Science of the Royal Society of Canada (FRSC-CA), Fellow of International Society of Electrochemistry (FISE), Fellow of the Engineering Institute of Canada (FEIC), Fellow of the Canadian Academy of Engineering (FCAE), Fellow of the Royal Society of Chemistry (FRSC-UK), and the Founder/Chairman of The International Academy of Electrochemical Energy Science (IAOEES). In 2014, 2015, 2016, 2017, 2018 and 2019, Dr. Zhang was ranked as the top 1% of Highly Cited Researchers in the world, has also listed as one of the “3000 World's Most Influential Scientific Minds” by Thomson Reuters in 2014, 2015 and 2016. He was awarded the prize of “Lifetime Achievement” by the International Academy of Electrochemical Energy Science (IAOEES) in 2018. The technical expertise areas of Dr. Zhang are Electrochemistry, Photoelectrochemistry, Spectroelectrochemistry, Electrocatalysis, Fuel cells (PEMFC, SOFC, and DMFC), Batteries, and Supercapacitors. Dr. Zhang received his B.S. and M.Sc. in Electrochemistry from Peking University in 1982 and 1985, respectively, and his Ph.D. in Electrochemistry from Wuhan University in 1988. Starting in 1990, he carried out three terms of postdoctoral research at the California Institute of Technology, York University, and the University of British Columbia. Dr. Zhang holds more than 14 adjunct professorships, including one at the University of Waterloo, one at the University of British Columbia and one at Peking University. Up to now, Dr. Zhang has more than 500 publications with approximately 40000 citations, including 350 refereed journal papers, 25 edited /co-authored books, 43 book chapters, 190 conference keynotes and invited oral presentations, as well as over 16 US/EU/WO/JP/CA patents, and produced in excess of 90 industrial technical reports. Dr. Zhang serves as the Editor-in-Chief of Electrochemical Energy Reviews (Springer Nature), and editor /editorial board member for several international journals as well as Editor for book series (Electrochemical Energy Storage and Conversion, CRC press). Dr. Zhang is also an active member of The Electrochemical Society ECS), the International Society of Electrochemistry (ISE), and the American Chemical Society (ACS, as well as the Canadian Institute of Chemistry (CIC).

Electrochemical Batteries and Lithium Batteries for New Energy Electric
Vehicles: Status, Challenges, Perspectives

Abstract： This speech summarizes the current development of new energy in electric vehicles, especially the development of batteries, challenges and perspectives. The summarized batteries include lithium-ion batteries, hydrogen fuel cells, metal-air batteries (lithium and zinc), super Capacitors and lithium-sulfur batteries. This speech is divided into the following sections: (1) trends in the development of new energy vehicles; (2) power batteries for new energy vehicles; (3) development of lithium-ion batteries, current status, challenges and prospects. This speech predicts the development trend of power batteries and points out the directions of future R&D efforts.

Ziliang Zhao

China FAW Group, China

Dr. Ziliang Zhao, born in November 1971, is a senior research engineer of China FAW Group Corp. Dr. Zhao graduated in the vehicle engineering from Jilin University in 2001, and is currently appointed as the director of Battery Research Dept. of Limited New Energy R&D Institute of FAW Corp. He is long involved in the development of electric vehicle products and fuel cell power systems. As the project leader, he has leaded and accomplished a series of “863" special items of developing electric vehicles and FAW product development project.

Big Data Based Safety Design and Engineering Application of Traction Battery for Automotive Application

Abstract: The traction battery is the core component of a new energy vehicle. Its safety problems have gained much attention concerning the sustainable development of new energy vehicles. This report presents an analysis on the vehicle fire caused by the thermal runaway of traction battery. Combined with the big data samples, a novel safety design and engineering application was proposed for the safety improvement of traction battery.

Jun Cai

Shanghai Hydrogen Propulsion Technology, China

Mr. Cai, Jun, currently serves as the director of system development department at Shanghai Hydrogen Propulsion Technology (SHPT, a company under SAIC). He leads the system development department in developing next-gen large power fuel cell system, focusing on system design & integration, control software design, and key BOP component development. During his time at SAIC, Jun also leads system design, control software development and vehicle calibration in multiple vehicle platforms including Roewe 950 FCV, Maxus FCV80 and Sunwin FC Bus. Before joining SAIC, Jun worked as Technical Lead and Senior Engineer in Fuel Cell department at General Motors for almost 10 years. Jun published over 10 global patents and he is also the recipient of the 2018 Shanghai Pujiang Talent Program.

The ABC’s Application in Fuel Cell System Development – AI, Big Data and Cloud

As human beings gradually enter the so called intelligent society, AI, Big Data and Cloud (ABC) has played a more and more important role in every industry. Fuel Cell industry certainly is not an exception. Developing fuel cell stack and system is complicated work which requires comprehensive knowledge. As of today, mechanism in fuel cell system such as heat transfer and stack degradation cannot be fully understood through a pure theoretical approach. Luckily, with the help of AI and big data, a rather complicated process can be simplified and understood using a numerical approach. This presentation introduces several R&D work using ABC approach in SHPT, including database design, cloud application and AI-based intelligent algorithm development. Throughout this work, data can be much better exploited to reveal unknown correlations. Also, AI based algorithm acts a key role in improving durability and reliability of a fuel cell system.

Fu Xiao

The Hong Kong Polytechnic University, China

Dr. Xiao obtained her double Bachelor degrees in Air-Conditioning Engineering (major) and Marketing (minor) from Xi'an Jiao Tong University in 1998, her Master degree from Shang Hai Jiao Tong University in 2001, and her PhD in Building Services Engineering from the Hong Kong Polytechnic University in 2004. She worked in Ove Arup & Partners Hong Kong Ltd. from 2005 to 2006. She returned to the Hong Kong Polytechnic University as a lecturer in 2006 and was promoted to Associate Professor in 2013.

Dr. Xiao is an active researcher in building energy and automation with a focus on automated diagnosis and optimization as well as big data analytics and AI for smart buildings and smart cities. Dr. Xiao has published over 100 SCI journal papers and secured a number of research grants and awards. Dr. Xiao is Editorial Board Member of Automation in Construction (SCI Q1) and guesting editors for Science and Technology for the Built Environment and Building Simulation. She is the reviewer of over 20 SCI journals. Dr. Xiao also participate in many big projects and her work brings significant energy savings in various buildings.

Big Data and AI for Smart Energy Efficient Buildings

Abstract:Big data and AI technologies are transforming the world including buildings. Buildings are responsible for over 1/3 of the world primary energy use and are major users in power grids. Their energy performance has great impacts on global sustainability and grid reliability. While modern IT technologies adopted in Building Automation Systems (BASs) make buildings smarter, they also provide a tremendous amount of real-time building operation data. The volume of data grows continuously in the building lifecycle and grows significantly with the widespread of IoT. Effective utilization of the big building operational data by using advanced data mining and machine learning algorithms enables buildings to become smarter and to achieve higher energy efficiency.

This presentation reports our team’s recent R&D work on application of big data analytics based on data mining and machine learning algorithms in smart energy efficient buildings. A holistic framework for discovering and applying knowledge hidden in the big BAS data using machine learning based data analytics is developed, taking into account of the low quality and complexity of BAS data, the diversity of advanced data analytics techniques, as well as the integration of data-driven knowledge and domain knowledge. Data-driven knowledge discovered from big building data is a valuable complementary to domain knowledge like physical principles. Implementation of the framework is demonstrated via several case studies on the operational data from real buildings. The knowledge discovered has been applied to identify dynamics, patterns and anomalies in building operations, evaluate building system performance and discover opportunities in energy conservation.

Yinshi Li

Xi’an Jiaotong University, China

Dr. Li is a professor of School of Energy and Power Engineering at Xi’an Jiaotong University, China. After receiving his PhD degree from the Hong Kong University of Science and Technology, he has made much contribution in the fields of Fuel Cell, Flow Battery, Solar Energy, Heat and Mass Transport. Dr. Li has published more than 60 international papers in various prestigious journals, such as Angew. Chem. Int. Edit, Phys. Rev. E, Int. J. Heat Mass Tran., ChemSusChem. He has received many awards, including the New Century Excellent Talents in University, Top 100 of the Create the Future Design Contest launched by NASA Tech Briefs. Currently, he serves as the editorial board members of Scientific Reports, Journal of Thermal Science, Science China Technological Sciences, and Chinese Science Bulletin.

Benefits achieved from intelligent algorithm in solar energy utilization

Abstract: Renewable energy technology is crucial to weakening the dependency on fossil fuels and minimizing the environmental pollution. Solar energy utilization is one of the most promising alternatives in reshaping the sustainable development pattern of human society due to the advantages of the abundance, wide-distribution and reproducibility. From large-scale power generation to off-grid energy solutions, concentrating solar thermal technology that allows concentration ratio ranging from tens to hundreds is capable of a broad operating temperature window. However, the inherent intermittency and geographical dispersion of solar resource hinders its direct utilization. The solar fuel that can be used in energy conversion devices, such as fuel cells, is able to address the storable and transportable challenges in solar energy field. Although appealing, questions about the reliability, stability and efficiency still need to be answered. Therefore, this talk relates to an intelligent risk prediction basing on the neural network model and process intelligent control of solar energy utilization system. With the aid of the intelligent algorithm, the risk-prevention, system-reliability and output-stability can be well enhanced.

Keywords: Solar energy; Solar thermal; Solar fuel; Intelligent control; Fuel cell

Yanli Liu

Tianjin University, China

Yanli Liu is the associate professor of the school of electrical and information engineering, head of the department of electrical engineering and executive deputy director of integrated energy power system intellectual centre in Tianjin University.

Her research area includes power system stability and security, cyber physical power system, and data-driven method applications in Smart Grid. She is now the “Smart Grid and Energy Internet” Subject Associate Editor of the journal Engineering (published by Chinese Academy of Engineering, 2018IF=4.568), Guest Editor-in-Chief of the Special Issue on “Data-Analytics for Stability Analysis, Control, and Situational Awareness of Power System with High-Penetration of Renewable Energy” of the journal International Journal of Electrical Power & Energy Systems (2018IF=4.418), and the core member of National Key Research and Development Program of China “Basic Theories and Methods of Analysis and Control of the Cyber Physical Systems for Power Grid”.

Data-Analytics for Enhanced Situational Awareness of the Smart Grid

Abstract:This speech will address the following three questions: why we would we like the power grid to be smarter? What’s big data and the role of data-analytics in the smart grid? How the data-analytics help develop enhanced situational awareness of the smart grid?

Daniele Marchisio

Politecnico di Torino, Italy

Daniele Marchisio graduated in Chemical Engineering cum laude in 1997 at Politecnico di Torino and obtained a PhD from the same university in collaboration with Iowa State University in 2001. After post-docs at Iowa State University and the Eidgenossische Technische Hochschule Zurich he started his career at Politecnico di Torino where he is now Full Professor. He has also been Adjunct Visiting Professor at the Beijing University of Chemical Technology (2016-2019). His research activity focuses on the development and experimental validation of multiscale computational methods for polydisperse particulate and multiphase flows. He is Associate Editor for the Canadian Journal of Chemical Engineering. He has authored 150 papers published on international journals and co-authored (with Prof. Rodney Fox) a book titled "Computational Models for Polydisperse Particulate and Multiphase Systems" (Cambridge University Press, 2013).

Application of multiscale modelling and deep learning tools for the simulation of multiphase polydisperse flows

Highlights

• Multiscale modelling is applied to the simulation of multiphase flows

• Multiscale modelling is augmented by artificial intelligence and deep learning tools

• Experimental data is used for model validation

Abstract:Multiphase polydisperse flows appear in many energy production and transformation processes. Bubble columns are used in the Fischer-Tropsch process for the conversion of gas molecules into liquid fuels, boiling (gas-liquid) flows are omnipresent in the conversion of heat into steam, liquid-liquid emulsions and the flow of droplets and particles in porous media plays a key role in enhanced oil recovery. The common element of all these systems is the presence of a continuous primary phase and a disperse secondary phase, which can be constituted by gas bubbles, liquid droplets and solid particles. In turn this population of bubbles, droplets and particles, because of coalescence/aggregation and breakage and because of the interactions with the primary phase, are characterized by different values of their size and velocity. This population is defined in terms of distributions, such as the size or velocity distributions, whose evolution is dictated by the population balance equation (PBE). However, since the PBE is strongly connected with the fluid dynamics of the multiphase system, very often it is tightly coupled with a computational fluid dynamics (CFD) model. The coupling is often realized with quadrature-based moments methods (QBMM), such as the quadrature method of moments (QMOM) or the conditional quadrature method of moments (CQMOM) [1]. Moreover, as the rates with which bubbles, droplets and particles are formed or evolve is governed also by molecular processes, very often these descriptions are coupled also with atomistic and molecular models. Among the different modelling choices, full atom molecular dynamics (MD), coarse-grained molecular dynamics (CGMD) and dissipative particle dynamics (DPD) are mostly employed. A plethora of codes is used to run these simulations, to couple the different models and to orchestrate the workflows, ranging from Ansys Fluent, OpenFOAM and code_saturne to LAMMPS, GROMACS, Salome, etc. All the above-mentioned models, being based on first principles and physical laws are labelled as physicsbased models. However, recently the simulation of these processes also relies on the use of artificial intelligence and deep learning tools to build data-driven models, which in contrast to the previous ones, are not based on first principles. An interesting idea is that since very often the amount of experimental data is not enough to build a data-driven model, validated physics-based models can be used to augment and enrich a limited experimental data set and to build the data-driven model, following the digital twin concept. Moreover, data-driven models can also be employed to build surrogate models that compress the rich information available in one model (and the corresponding scale) to another one. In this presentation three different examples will be discussed, namely the simulation of gas-liquid flows (bubble columns and boiling flows), liquid-liquid emulsions and flow and particle transport in porous media.

References

[1] Marchisio D.L., Fox R.O. Computational models for polydisperse multiphase and particulate flows (2013) Cambridge University Press, Cambridge, UK.

Jae Wan Park

University of California, Davis, USA

Jae Wan Park received his B.S., M.S., and Ph.D. degrees in Mechanical Engineering from Pohang University of Science and Technology in 1997, 1999, and 2004, respectively. Since 2000, he has published over 60 academic articles including two book chapters and 65 peer-reviewed journal articles and proceedings (which has been cited over 1610 google scholar times). Dr. Jae Wan Park is an Associate Professor in the Department of Mechanical and Aerospace Engineering (MAE) at the University of California, Davis since 2014. His research interests include green energy systems with batteries and proton exchange membrane (PEM) fuel cells. His research team has recently developed a neutron radiography/tomography system for fuel cells and batteries at the UC Davis McClellan Nuclear Research Center. Using the developed system, he has been performing various experiments to investigate heat and mass transport phenomena in the fuel cells and batteries. He is the director of the Green Transportation Laboratory, the UCD Formula Hybrid Racing Team, and the Center for Energy Storage and Microgrid, at UC Davis. He serves as a faculty and executive committee member of Institute of Transportation Study, Davis. He is also the CEO of RePurpose Energy, Inc. He is actively contributing to the fuel cells, batteries, and hybrid power system fields as a reviewer for and on the editorial board of many journals.

Development and Demonstration of Microgrid System Utilizing Second-Life Electric Vehicle Batteries

Microgrids have begun to move from the realm of academia into industry thanks to the numerous benefits they can provide. These include increased electrical supply resiliency due to local generation and the ability to island, higher power quality thanks to inverters connected to low inertia power sources such as batteries, increased penetration of renewable sources of electricity, and reduced reliance on the electrical distribution system. Despite this, there have been relatively few papers dealing with the design, installation, and operational issues of real microgrid systems. Most papers focus on simulations and energy management algorithms. This paper will discuss the design considerations, installation procedure, commissioning and initial troubleshooting for the microgrid system installed at the Robert Mondavi Institute’s (RMI) Brewery Winery and Food Science building (BWF)at UC Davis. This microgrid consists of 2 buildings, including the UC Davis winery (BWF) and the Jackson Sustainable Winery Building (JSWB), 200kW of solar PV and 290kWh of energy storage. The construction of a microgrid from scratch is truly an interdisciplinary undertaking, including mechanical, electrical, software, and civil engineering as well as collaboration with fire departments, safety officials, and property owners. In short, actually deploying a real microgrid system requires many steps and involves significant challenges often overlooked in the literature. The RMI microgrid has been running since Jan. 2019 showing excellent performance and reliability.

Zhiguo Qu

Xi’an Jiaotong University

Dr. Qu is a professor of Energy & Power Engineering at Xi’an Jiaotong University. He obtained the Ph. D degree in engineering thermophysics from Xi’an Jiaotong University in 2005. He worked as a visiting scholar at Advanced Heat Transfer, LLC, USA and Pennsylvania State University in 2006 and 2013, respectively. His main research interests include multiscale transport phenomena, energy conversion and storage, micro-nanofluidics, and electronic thermal management. He has published 153 SCI indexed papers in peer-reviewed journals. These papers are cited by more than 3600 times from google scholar with h-index of 31. He serves as the editorial board member for several journals. Dr. Qu is a recipient of Young Scholars of the Yangtze River, National Young Top-notch Talent Support Program of China, China National Funds for Excellent Young Scientists, Young Scholar Fund from Fok Ying Tung Education Foundation of China, and the Chinese Ministry of Education program for New Century Excellent Talents.

Numerical and deep learning study on multi-scale problem for adsorption and diffusion processes in porous media

Abstract:The multi-scale heat and mass transfer process in porous media is a widespread phenomenon that exists pervasively in multi-scale gas adsorption for shale gas matrix and adsorbent bed. In this keynote lecture, a deep learning coupled with genetic algorithm model is established to predict the adsorption capacity based on the experimental data. The model provides more accurate prediction than those of theoretical models and BP neural network model at lower pressure range. Then, a modified lattice Boltzmann model (LBM) is developed on the pore-scale to accurately predict the effective diffusivity of heterogeneous shale matrix, where the multicomponent and irregular morphological features are fully considered. The effects of shale porosity, average gain diameter, organic matrix volume fraction and diffusivity, and irregular structures on the matrix diffusion ability are investigated. A modified empirical formula is proposed to effectively capture the heterogeneous shale matrix diffusion ability. The gas adsorption and separation on porous surface of the absorbent at different scales are solved by a multi-scale method that couples LBM with grand canonical Monte Carlo (GCMC). In interfacial boundary, saturation adsorption capacities are obtained by GCMC method to replace empirical values. Langmuir–Freundlich model and linear fitting formula are used to calculate the saturation adsorption capacities in Langmuir adsorption kinetics model and the adsorption heat in heat transfer in LBM model. Lastly, the mass transfer process of mixture gases in membranes is investigated by the above multi-scale method. The proposed coupled method can be helpful in the design of efficient membranes.

Key words: porous media, multi-scale, shale gas, deep learning, gas adsorption, GCMC, LBM

Akeel Shah

Chongqing University, China

Professor Akeel Shah graduated with a first-class honours degree in Mathematical Physics in 1995 and a PhD in Applied Mathematics (both from University of Manchester Institute of Science and Technology) in 2001. He is currently a Professor in the School of Energy and Power Engineering at the Chongqing University, with expertise in electrochemical energy conversion, computational engineering and applied machine learning. He previously held positions at University of Southampton and University of Warwick. His work is primarily focused on the modelling and simulation of energy-conversion devices (flow batteries, metal-air batteries, organic/inorganic fuel cells), including computational modelling, and the development of fast algorithms for computer codes in science and engineering based on machine learning and computational statistics. Between 2004 and 2006, he held a joint Pacific Institute of Mathematics Sciences (PIMS) and Mathematics of Information Technology and Complex Systems (MITACS) Fellowship. He is the author of over 70 publications in leading, international peer-reviewed journals (Google Scholar h-index = 24; Scopus h-index = 23). Professor Shah has worked closely with the fuel cell and battery industry (Ballard Power Systems, Johnson Matthey Plc, Sharp Laboratories, ACAL Energy Ltd) to develop models/numerical codes for design purposes. He has received funding from the TSB, FP7, dstl and directly from industry.

Shared-Latent Surrogate For High-Dimensional Stochastic Input Simulation

Abstract:Data-driven surrogate models are widely used for applications such as design optimization and uncertainty quantification, where repeated evaluations of an expensive simulator are required. In many real applications, the model inputs are high-dimensional (e.g., 1 million) stochastic random fields, and thus a direct implementation of the standard surrogate model is practically impossible due to the curse of dimensionality. It is a common practice to find approximate representations for these random fields as alternative inputs based upon dimension reduction methods. The challenge is 1) to build a model that is optimized for the approximation of the target simulator and 2) to efficiently capture the complex nonlinear correlation in the random fields. In this paper, we first generalize the standard KL decomposition with an infinite number of bases to capture any complex nonlinear correlations. We then use the kernel trick to derive a practical computational method. Finally, we introduce a shared representational space to allow optimization for the target simulator. The method is applied to various, including energy related, data sets to demonstrate its accuracy.

Hong Sun

Shenyang Jianzhu University, China

Prof. Hong Sun is the dean of Mechanical Engineering School of Shenyang Jianzhu University. He got PhD from Xi’an Jiaotong University in 2005. His interesting is PEM fuel cell and Lithium air battery. He has published more than 200 papers. Prof. Hong Sun won the second prize of National Science and Technology Progress, the first prize of Liaoning Province Science and Technology Progress and the second prize of Liaoning Province Science and Technology Progress. He won Program for New Century Excellent Talents in University of Ministry of Education of China in 2008, and he was awarded Excellent Expert of Liaoning Province in 2017, Distinguished Professor of Liaoning Province in 2017, Outstanding talents in The plan of rejuvenating the talents of Liaoning Province in 2018.

Mass transfer and structure design of Li-air battery

Abstract:Li-air batteries are considered to be a potential alternative to lithium-ion batteries for transportation applications. However, there are impressive challenges in its development and application. Besides the material preparation and the improvement of porous electrode and catalyst, the mass transfer and electrochemical polarization in the Li-air battery are also the key issues for its application in the electric vehicles. In order to find out the battery reaction mechanism including mass transfer resistance and electrochemical polarization, this study employs the macro simulation, the mesoscopic simulation, the molecular simulation based on the fundamental theory of mass transfer, electrochemistry, molecular dynamics and quantum mechanics. The experimental verification is also performed. The mass transfers of lithium ion, water, hydroxide ion and dissolved oxygen in electrode porous media are studied. The effects of the air electrode thickness, the porosity, the diameter of particles and operating parameters are discussed to further improvement of the performance and lifetime by reducing the internal mass transfer resistance and electrochemical polarization. Moreover, the oxygen reduction reaction in the air electrode is performed to carry out the stepwise reaction process by the density functional theory and the first principle.

Chao Tan

Tianjin University, China

Prof. Chao TAN Recieved his Bachelor, Master and Ph.D degree from Tianjin University in the year 2003, 2006 and 2009 respectively. Worked in Cranfield University as a joint Ph.D student between 2007-2008. Since 2009, he joined School of Electrical and Information Engineering, Tianjin University. He’s now a professor in SEIE of Tianjin University, head of the Tianjin International Joint Research and Development Center of Process Imaging and Measurement, and JSPS invitational Fellow. His research interests include process tomography, multiphase flow sensing techniques, biomedical measurement and imaging, multi-sensor data fusion. He serves as an Associate Editor (AE) in IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal, Transactions of the Institute of Measurement and Control, IEEE Access. Currently, IEEE Senior Member, International Society of Industrial Process Tomography (ISIPT) Consultative Scientific Panel. Research papers won the best paper award in IEEE-IST conference (2009), Chen Xuejun Prize for Excellent Paper of Young Scholars (2009), Municipal Outstanding Doctoral Dissertation Award of Tianjin (2013), Best paper award in <Chinese Journal of Scientific Instrument> (2015), InstMC Poster Prize - Highly Commended in the 22nd IMEKO (2018).

Intelligent monitoring and measurement of industrial multiphase flow

Abstract:Multiphase flow widely exists in industrial process in energy sectors such as petroleum and nuclear engineering. Online monitoring and measurement of flow process is very important and urgent to model, optimize and control the process. The complex structure and transient behavior of the multiphase flow present an ultimate challenge to the online measurement and instrumentation. This talk will introduce the latest development of process imaging and measurement techniques for the industrial multiphase pipe flow. These technologies provide the internal structure of transient multiphase flow in a visualized and non-intrusive manner, and also the flow process parameter measurement and diagnosis through fluid mechanics modeling. The content focuses on the electrical and ultrasonic tomographic sensors and system development, along with their mathematical solutions and measurement models. The proposed sensors and systems are based on industrial-standard data bus, so the sensing modalities of electrical resistance, capacitance, inductance, and ultrasonic transmission, reflection and Doppler mode could be flexibly configured and combined to meet different sensing applications.

Zhengkai Tu

Huazhong University of Science and Technology, China

Dr Tu obtained his PhD degree from Huazhong University of Science & Technology in 2009. Thereafter, he worked as a postdoctoral researcher in State Key Laboratory of Advanced Technology for Materials Synthesis and Processing in Wuhan University of Technology and began his research in Proton Exchange Membrane Fuel Cells (PEMFC). He has become a Full Professor in New Energy in September 2016. In 2017, Dr Tu returned to Huazhong University of Science & Technology as a group leader in PEMFC. His research now is very much inclined to PEMFC designed for unmanned underwater vehicle (UUV) and unmanned aerial vehicle (UAV) applications. Dr. Tu has published more than 90 refereed journal papers. These papers registered his great contribution in water and heat management in PEMFC, which provide guidelines for the design of PEMFC systems. He is now an expert in Military Science and Technology Commission for fuel cell R&D, and also the adviser of fuel cell for GREE Electric Applications, Inc. of Zhuhai.

Lifespan prediction of a FC vehicle based on data analysis

Abstract: Fuel cell vehicle has attracted more and more attention due to its high efficiency and low emission. Driving cycle construction method based on velocity of conventional petrol vehicle is no longer applicable for fuel cell vehicle, because the operation characteristic of fuel cell vehicle is a combination of fuel cell and lithium battery. Based on power and power variation of fuel cell, a driving cycle of fuel cell vehicle has been developed using k-means clustering method according to the typical polarization characteristics of fuel cells. To avoid too many influence factors, average power, power standard deviation, average power variation and power variation standard deviation of short samples divided according to the velocity cycle are selected to cluster. Combining the number of short samples and the expected division dimension, the short samples are clustered into 3, 6 and 9 categories. Through the visualization and analysis of the results, the optimal clustering was classified into 6 categories, indicating six levels of power and power variation. The correlation coefficient of characteristic values between each short sample and cluster was used to select the representative short sample of each cluster. According to the time fraction of each cluster, the accelerated decay condition was obtained, which would provide the research basis of the mechanism of performance decay in the further study.

Yun Wang

University of California, USA

Yun Wang received his B.S. and M.S. degrees in Mechanics and Engineering Science from Peking University in 1998 and 2001, respectively. He went to the Pennsylvania State University where he received his Ph.D degree in Mechanical Engineering in 2006. Wang joined the MAE (Mechanical and Aerospace Engineering) faculty at the University of California, Irvine in 2006. Wang has produced over 70 publications in PEM fuel cell and Li-air battery, including a book on PEM Fuel Cell Water and Thermal Management Fundamentals in 2013 and a PEM fuel cell review paper in 2011 (which has been cited over 2,150 google scholar times). He received a few awards, including the prestigious Distinguished President's Award and Outstanding Educator Award from Orange County Engineering Council, the Seasky Scholarship from Dalian University of Technology, China, and the 2011-2012 Applied Energy Certificate of Excellence: Most Downloaded Authors. Several of his seminal works are highly cited in the major fuel cell journals. His 45 journal papers published in 2005-17 have been cited over 5,100 times. Scopus (last access on 10/3/2018) shows one first-authored paper is the most cited in the history of Applied Energy since 1975 (among 12,590 papers). Wang served as Track chair/co-chair, session chair/co-chair, conference chair and committee member for many international conferences of power, thermal energy, and engineering. Wang is currently Professor at the UC Irvine and ASME fellow.

Machine Learning in Dynamics and Power Management of PEM Fuel Cell

Abstract:Polymer electrolyte membrane (PEM) fuel cell has been regarded as a potential power source for various applications due to its noteworthy features of high efficiency and zero emission. Its performance and dynamics are controlled by electrochemically coupled transport processes, including fluid flow, phase change, species transport, energy conservation, and proton/electron conduction, and are important to its practical applications. Artificial neural network (ANN), inspired by the biological neural networks that constitute animal brains, provides logical connection between input and output variables to predict output values according to given input variables. It has been successfully applied to many power and energy systems to optimize component materials, dynamic control, and system design. In this talk, I will present several fundamental aspects regarding the dynamics and power management of PEM fuel cell, including dynamic response, voltage evolution, and power management, and discuss several ANN approaches that are currently under development to advance PEM fuel cell technology and applications.

Billy Wu

Imperial College London, UK

Dr. Billy Wu is a senior lecturer at Imperial College London in the Dyson School of Design Engineering where he works on electrochemical devices and manufacturing. Within the School he is the part of the Energy Technologies and Systems theme and also jointly leads the Electrochemical Science and Engineering group. He is on the editorial board of Scientific Reports and the Journal of Power and Energy and also co-leads the Imperial College London Additive Manufacturing Network and is part of the management team for the Imperial College Advanced Hackspace. He is a fellow of the Higher Education Academy.

Battery digital twins: The fusion of data, models and artificial intelligence for next generation electric vehicles

Battery electric vehicle uptake is rapidly increasing. This is due to increasing concerns about global climate change but is also catalysed by the increased performance and cost reductions in lithium-ion battery technology. However, more needs to be done with increasing battery lifetimes, which are highly usage dependent with real-world usage differing from lab based testing conditions. To address this problem, physics based models which describe the electrochemical degradation processes that occur in the battery have been developed which can be used for model-led control algorithms, however these are not perfect and require extensive parameterisation. Compounded by the diversity of battery chemistries, load profiles and environmental conditions, their diagnostics and control are a major challenge. This talk will explore how in-vehicle instrumentation can collect a wealth of data which can be used to create digital twins of electric vehicles to inform optimised control for improved lifetime and performance. Models with different levels of fidelity can be implemented in this digital twin framework such as physics based equivalent circuits for on-board state-estimation (state-of-charge, state-of-available-power, state-of-health) or continuum level physics models run on the cloud to update key operational limits on the vehicle in real-time. With this abundance of data combined with machine learning approaches, lifetime and performance estimation can also be made more accurate, with the vehicle becoming smarter with increasing use. Knowledge from these discrete digital twins can thus be combined to provide deep insights into aggregated fleet behaviours and also enable 2nd life applications of batteries onto the electrical grid.

Bing Xu

Heriot-Watt University, UK

Dr Bing Xu is an Associate Professor of Finance in the Edinburgh Business School at Heriot-Watt University, UK. She holds a MA (Hons) in Business Studies and Accounting, and a PhD in Management both from the University of Edinburgh. Her re¬search concerns banking and finance, energy eco¬nomics, and multi-criteria decision-making analysis. She has also worked on several multidisciplinary energy project by focusing on stakeholder engagement, performance evaluation of competing energy strategies, and social acceptance. Bing is on board of director for the Roundtable on Sustainable Biomaterials (RSB), and the Energy Economics working group chair for the Chinese Economics Association (CEA).

Global Oil Market Uncertainty and Oil Prices: Data-Centric Solutions

Abstract:Oil is a vital source of energy for the global economy. Modelling oil price movements is essential for many decision-making processes such as macroeconomic policy, capital investment, production decisions, consumptions, risk and portfolio management. Supply and demand are clearly important, a factor also needs to consider is the impact of uncertainty on the oil market. So far, the oil market uncertainty literature has primarily relied on macroeconomic or oil price uncertainty proxies such as the implied or realized volatility of stock market returns or oil prices, the cross-sectional dispersion of forecasts. The potential issues are they are based on structure of specific theoretical models or relied on single or small number of observable indicators. Our paper proposes new measures of global oil-market uncertainty and relate them to the real price of oil. In contract to the existing literature, we use factor augmented vector autoregression to construct time-varying global oil market uncertainty in a data-rich environment. Our estimates display significant independent variations from popular uncertainty proxies. We also find oil price shows heterogeneous response towards to different types of uncertainty shocks. For instance, macroeconomic shocks and financial shocks have an immediate impact upon the oil prices while alternative energy demand shocks have a delayed effect.

HongTao Xu

University of Shanghai for Science and Technology, China

Hongtao Xu received his B.S. and M.S. from Xi'an Jiaotong University in 1999, 2002 and PhD from the Hong Kong Polytechnic University in 2005. After that, he worked in two consulting companies of Parson Brinckerhoff and AECOM for 6 years. In 2011, he joined University of Shanghai for Science and Technology. Now, he is active in many academic committee, such as Youth Working Committee of China Engineering Thermophysics Society, Boiler Technical Committee of China Boiler and Boiler Water Treatment Association et.al. He is also one drafter of the national standard of GB/T 36699-2018 Technical Conditions for Boiler Liquid and Gas Fuel Burners. Since 2013, he conducted one NSFC project, two Shanghai Fund projects and joined one National Key R&D Program. Now, he has published 34 SCI papers.

The 3D information-integrated system development for industrial and utility boilers based on the VR Technology

Abstract:To promote the traditional boiler knowledge acquisition, we developed a 3D information-integrated system for industrial and utility boilers based on the virtual reality (VR) technology. The comprehensive utilizations of VR technology, network multimedia technology, graphics and big data, and other digital technologies were adopted to realize the visualization and interactive dynamic display of the 3D integrated information related to five industrial boilers and three utility boilers. The VR system can display the details of the working principle, internal structures, component split, water circulation and flue gas flow in the industrial and utility boilers. This system can provide a complete and comprehensive "one-stop" and "what you see is what you get" concepts for boiler related designers and users. This system is positioned as a public service system, and it is open to public without charge.

Keywords: 3D information, boiler, virtual reality

Jiao Yu

Shanghai Palcan New Energy, Co., Ltd, China

Dr Jiao is the Research & Development Director of Shanghai Palcan New Energy, Co., Ltd.

Reformed methanol fuel cell technology and its application in intelligent energy

Abstract:Reformed Methanol Fuel Cell（RMFC）is a system that combines methanol and water steam reformer and High Temperature proton-exchange membrane（HT-PEM）fuel cell stack. The methanol and water mixture firstly be converted into a hydrogen-rich gas by the use of a reformer and then be fed into the HT-PEM stack for electric generation.

The reformer is a heat exchanger and catalyst device that can produce hydrogen gas and carbon dioxide by reacting a methanol and water (steam) mixture. Methanol reforming typically takes place at 220-300° and is an endothermic process, meaning thermal energy is needed to drive the process. The HT-PEM generally operates at temperatures of 150-180° and is an exothermic process. The high temperature means that the fuel cell can use hydrogen with a higher CO concentration compared to other systems. And the radiated heat from the stack can be exchanged to heat the reformer for high efficiency energy application.

Palcan’s reformed methanol fuel cell technology is being widely used in intelligent energy field as back-up power and combined heat and power (CHP) for energy supply cooperated with other renewable energy coordinated in micro-grid. It is also already applied in electric vehicles as range extender for passenger cars, buses, cold chain logistics light duty trucks, and as APU in other transportation vehicles.

Nada Zamel

Fraunhofer Institute for Solar Energy Systems, Germany

Nada Zamel obtained her Bachelors, Masters and Doctorate degrees all in Mechanical Engineering from the University of Waterloo in 2005, 2007 and 2011, respectively. Since 2006, she has published over 40 academic articles with 30 peer-reviewed journal articles. Dr. Zamel is currently a Senior Scientist in the Fuel Cell Systems Department at Fraunhofer Institute for Solar Energy Systems, ISE and has been a member of the department since 2011. Her research interests are focused on polymer electrolyte membrane fuel cells, specifically on material development and cell characterization. She is also actively involved in the research community via conference/workshop organization and as a reviewer for many journals tailored towards renewable energy. She is also actively involved in various industrial and publicly funded projects with many being collaborative between Germany and international partners.

Production of catalyst coated membranes for low temperature PEM fuel cells

Abstract: Further advancement of polymer electrolyte membrane (PEM) fuel cells, particularly for use in the automotive industry, must be achieved as a balance between cost and functionality. The catalyst layer as the heart of the cell controls the half-cell reactions and their products. Its structure governs the various transport phenomena simultaneously taking place and affects its overall activity, stability and life time. Throughout the years, the optimization of the structure of the catalyst layer, with special attention given to the cathode, has been achieved via systematic optimization of its components. Understanding the interaction between the layer’s ingredients, its structure and performance is, thus, important to its advancement. At Fraunhofer Institute for Solar Energy Systems ISE, we have been actively working on understanding the full life cycle of the manufacturing process of a catalyst coated membrane (CCM), from the ink, to its application on the membrane, to the quality control of the entire membrane electrode assembly (MEA). This investigation is carried out using various in-situ and ex-situ analysis of every step of the manufacturing process. The question then that arises is how best to deal with the extensive data collected during all processes, and how to couple it with the corresponding process parameters. Intelligent data processing can, hence, be utilized to build this link and would help to carry out a comprehensive optimization of CCM production parameters, to improve the catalyst performance and enhance our understanding of the parameters and their interconnection.

Zhigang Zhan

Wuhan University of Technology, China

Zhigang Zhan, professor of State Key Lab of Advanced Technology for Materials Synthesis and Progressing, Wuhan University of Technology. PHD supervisor.

His research interests include transport phenomena in fuel cell; optimal design of PEMFC stack and system; power plant of new energy for marine engineering. etc.

He has been in charge of a series of research programs, including national 863 high technology project, national nature science foundation, national key development project “new energy vehicle”, and some company cooperation projects, etc.

PEM Fuel Cell Modeling and Engineering Application

Abstract:Fuel cell computer modeling is an important method both for exploring the mechanism of transport and electrochemical processes inside fuel cells, and for fuel cell/stack structure optimal design. This report presents related work recently done by WHUT fuel cell group, including gas pressure drop in flow field related to two phase flow, full size and large active area cell structure optimal design, and fuel cell cold start, etc. Also, some ideas are proposed in which AI technology may be used to improve the durability of PEMFC.

Dongda Zhang

University of Manchester, UK

Dr. Dongda Zhang is a University Lecturer at the Department of Chemical Engineering and Analytical Science, University of Manchester, and an Honorary Research Fellow at the Centre for Process Systems Engineering, Imperial College London. He currently leads research activities in the field of Bioprocess Systems Engineering and Machine Learning at the Centre for Process Integration, University of Manchester, and is the University's IChemE Representative. He holds BSc degree (2011) from Tianjin University and MSc (Distinction) degree (2013) from Imperial College London. He started his PhD research at the University of Cambridge in 2013, completed his research within 2 years, and graduated at the beginning of the third year after the university special approval for Thesis Early Submission. Upon the completion of his PhD in 2016, he moved to Imperial College London as a postdoctoral research associate. In 2017, he was awarded the prestigious Leverhulme Early Career Fellowship (Principal Investigator) at the University of Cambridge, followed by his appointment at the University of Manchester in the same year. His research group focuses on the theory development and application of industrially focused mathematical modelling tools to digitalise, visualise, optimise and scale up complex (bio)chemical systems for renewable energy and high-value chemicals production.

Machine Learning Techniques for Chemical and Biochemical Process Digitalisation

Abstract: Developing advanced digital technologies to operate industrial manufacturing processes is one of the grand research themes prioritised by the 4th Industrial Revolution. However, selecting and combining effective digital technologies (e.g. mechanistic modelling, artificial intelligence, process analytical technology, automation, and software sensing) to enable a smarter design and autonomous optimisation of complex chemical and biochemical systems remains a challenging topic. In our work, we develop and apply both physical models (e.g. kinetic model, CFD, superstructure) and machine learning based data-driven models (e.g. deep learning networks, Gaussian process, reinforcement learning) to a number of energy-related (bio)chemical processes. We also adopt emerging hybrid modelling and surrogate modelling strategies that take advantage of both physical models and data-driven models to resolve otherwise intractable problems. This presentation will illustrate the use of state-of-the-art machine learning techniques for (bio)chemical process quantification and prediction, online optimisation and control, and large scale system design and plant-wide decision-making. An outlook of how to exploit frontier data-driven technologies for future process digitalisation and visualisation is also provided.