#University of Birmingham#
PART
NO.1
分论坛场地
伯明翰大学老工程楼/ Engineering Building (Y3) -现场通知
气候变化与净零排放分论坛
Dr. Shan Huang, Assistant Professor of Paleontology at the University of Birmingham, Ph.D. in Ecology, engaged in research on macroecology and macroevolution. The research combines fossil and modern biodiversity patterns to analyze the complex impacts of environmental changes on biodiversity over large spatial and temporal scales.
报告内容：
Marine Biodiversity and Its Conservation Under Climate Change
Modern marine ecosystems provide essential biological resources for human society but are also significantly impacted by various environmental changes and human activities. Economic species, in particular, are under multiple pressures such as climate change, ocean acidification, habitat alteration, and overfishing. By analyzing fossil data on the responses of ancient marine animals to climate change, we develop extinction prediction models and apply them to modern economic bivalves (Bivalvia). This research aims to explore key areas and directions for future marine biodiversity conservation.
Dr. Yirui Jiang, Research Fellow at Cranfield University, Ph.D. in Engineering. Her research interests include digitalization, sustainability, and innovation, with extensive experience in digital transformation. She excels at leveraging emerging technologies such as artificial intelligence, blockchain, the Internet of Things (IoT), the metaverse, and data analytics to develop smarter and more sustainable systems, introducing digital solutions across various industries, including health and healthcare. She is also actively involved in public affairs, promoting digital literacy and environmental awareness.
报告内容：
Advancing Sustainable Health through Digitalization: Water and Energy
The application of smart technology and data analytics in sanitation not only enhances resource utilization efficiency but also significantly reduces carbon emissions, aiming for net-zero emissions. We employ IoT technology to monitor and analyze data generated during sanitation processes in real-time, optimizing resource use efficiency. For example, smart sensors and monitoring systems can detect water quality, flow rates, and pollutant levels, allowing for real-time adjustments in treatment processes to ensure water resource recycling. This intelligent management not only improves water resource utilization efficiency but also reduces energy consumption and carbon emissions during wastewater treatment. We have developed an intelligent management system that automatically adjusts equipment operation to minimize energy consumption. The introduction of this system has greatly reduced the carbon footprint in sanitation processes, providing strong support for achieving net-zero emissions. Additionally, we have introduced advanced waste treatment technologies to convert sanitation waste into renewable energy. Using biomass technology, we utilize sanitation waste for anaerobic digestion to produce biogas and organic fertilizers, achieving resource regeneration and environmental protection. Biogas, as a clean energy source, can be used for power generation and as fuel, further reducing fossil fuel usage and greenhouse gas emissions.
Dr. Ning Zhao, Assistant Professor at the Birmingham Centre for Railway Research and Education, School of Engineering, University of Birmingham, holding a Ph.D. in Engineering. His research interests include intelligent rail traffic control, train traction power, railway signaling, and emerging technologies such as hydrogen energy. He has developed smarter, more autonomous train systems and achieved outstanding results in collaboration with industry partners in areas including autonomous train operation and energy optimization. His intelligent driving assistance system has been utilized throughout the Edinburgh Trams network for five years, achieving significant energy savings.
报告内容：
Intelligent Control and Optimization of Railway Traffic
Rail traffic accounts for 40%-60% of urban electricity usage, significantly impacting the stability of power transmission networks, energy consumption, and the environment. Using intelligent technologies and data analysis techniques to enhance train control systems can improve the smoothness of train operations, reduce energy consumption, and increase passenger comfort. This also enhances the overall efficiency of the rail network, contributing to improved train operation quality and supporting the achievement of carbon neutrality goals. We employ intelligent real-time algorithm technologies to propose a comprehensive train control system optimization scheme for both autonomous and manual train operation. This helps train operating companies increase the intelligence, stability, and smoothness of train operations without affecting existing train schedules. As a result, train energy consumption is significantly reduced, the quality of network traffic is improved, and companies realize substantial economic benefits.
Dr.Yongjing Wang is an Associate Professor in the Department of Mechanical Engineering, School of Engineering, University of Birmingham.
Dr. Wang is the Principal Investigator of the three major EPSRC grants in the area of smart robotics for sustainable manufacturing with a total value of £3.5M. Wang is a Co-investigator and a theme lead in the £35M EPSRC national hub in robotics for circular economy. Wang has over 50 publications in the area of robotics for sustainable manufacturing. His book ‘Optimisation of Robotic Disassembly for Remanufacturing’ was the first book addressing optimisation problems in disassembly sequence planning.
He is also a funding reviewer for research councils in the UK, Switzerland and the US, and an editor for world-leading journals and conferences (e.g. the IEEE, Nature and Royal Society series).
Wang's research work has been supported by world-leading companies including but not limited to Airbus, Caterpillar, Toshiba, Jaguar Land Rover, KUKA and Dyson. Wang is on the advisory board of the United Nations Higher Education Sustainability Initiative, an editor of the United Nations guidelines on artificial intelligence for sustainability, an invited member of a standard committee with the British Standards Institution (BSI), UK, and one of the seven elected members of UK Robotics and Autonomous Systems Network Early Career committee.
He is a Fellow of the Higher Education Academy, a Chartered Engineer with the Institution of Engineering and Technology, and a member of IEEE.
报告内容：
Robotic disassembly and remanufacturing automation
Disassembly is a key step in remanufacturing and recycling, both of which are critical components in a circular economy. Disassembly is also a common operation in the repair and maintenance of machines and public infrastructure facilities (e.g. transport and energy). In many ways, disassembly is challenging to robotise due to variability in the condition of the returned products and the required dexterity in robotic manipulations. Compared to the assembly of new products, which is deterministic because the components to be assembled are of known geometries, dimensions and states, disassembly is more stochastic as it has to contend with used products of uncertain shapes, sizes and conditions. This talk introduces recent research developments in the area of robotic disassembly and remanufacturing automation, and highlight key opportunities and technical gaps in the use of robots to support sustainable manufacturing.
Dr. Peipei Chen, Postdoctoral Researcher at the Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, and PhD in Ecological Economics from University College London. His research areas include energy policy evaluation, energy transition, and input-output analysis.
报告内容：
Data-Driven Efficiency Improvement of Global Coal-Fired Power Plant Units
Reducing carbon emissions from coal-fired power plants is crucial for achieving net-zero emission goals. While coal remains a transitional energy source in the near and mid-term, improving the efficiency of coal-fired power plants is a practical emission reduction strategy. However, there is a significant gap in the potential scale of engineering and technical improvements achievable in coal-fired power plants. We have developed an assessment framework using artificial neural networks and optimization models that incorporates the engineering characteristics of 8,883 coal-fired power plant units worldwide. The framework identifies potential efficiency improvement measures at the unit level, including changes in boiler type, coal type, and adjustments in steam temperature and pressure. Results indicate that these improvements could increase the average efficiency of global units by 8%. By 2050, the potential cumulative emission reductions achieved through efficiency improvements could amount to 31Gt (24-33Gt). Early implementation of efficiency improvements can significantly enhance cumulative emission reductions, with China, the United States, and India being major contributors. The study suggests that merely improving plant efficiency may not be sufficient to mitigate climate change. Therefore, timely implementation of alternative measures is critical, such as early retirement of power plants, carbon capture, utilization and storage (CCUS) technologies, and co-firing technologies.
Dr. Chang Yang, Postdoctoral Researcher in Finance at Shanghai Jiao Tong University and Visiting Postdoctoral Researcher at the University of Manchester. Her research areas include ESG and corporate finance, sustainable finance, green finance, and climate finance. She is a recipient of the High-Level Paper Scholarship from Shanghai Jiao Tong University and the "Outbound Program" project of the Postdoctoral Management Committee. She has been a visiting scholar at the Chinese University of Hong Kong, the University of Texas at Dallas, and Durham University. She represented young scholars from China and the UK at the UN Climate Conference (COP28). Her research on renewable energy policy has been reported by over a dozen mainstream Chinese media outlets, including People's Daily, CPPCC News, Phoenix News, China.com, and China Economic Herald.
报告内容：
Renewable Energy Policy and the Value of Corporate Cash Holdings
The use of renewable energy is central to transitioning to a net-zero economy, but its impact on the corporate sector remains unclear. This paper examines the effect of renewable energy policies in the United States on the value of corporate cash holdings. The study finds that renewable energy policies significantly increase the value of corporate cash holdings, indicating that under the backdrop of such policies, the precautionary motive for cash plays a crucial role in corporate liquidity decisions. This research provides insights into how companies can better adapt to renewable energy policies and contributes to promoting economic and social green transitions.
TBC
Prof. Xinghua Zhu, Professor in the Department of Geological Engineering, PhD Supervisor for International Students, and "Youth Chang'an Scholar" at Chang'an University. He serves as the Associate Chair of the Department of Geological Engineering and is a C-level Professor in Geological Resources and Geological Engineering. He has long been engaged in research on key technologies for the prevention and control of geological disasters and ecological restoration, focusing on debris flows and loess landslides. He has led or participated in multiple projects funded by the National Natural Science Foundation of China and provincial and ministerial-level projects. His research findings have been published in SCI, EI, ISTP, and Chinese CSCD indexed journals.
PART
NO.2
报名链接
Registration Link
https://docs.qq.com/form/page/DSm56VVVFeEhuZmhE
观众报名截止日期：
5月29日17时
Executive Chair：
Dr Shangfeng Du: s.du@bham.ac.uk
Forum Coordinator：
Mr Yongjian Li: yjl015@bham.ac.uk
Mr Mengda Wu: mxw157@bham.ac.uk
主办单位：
伯明翰国际青年学者论坛组委会
承办单位：
博士联盟、
伯明翰大学华人学者协会、
伯明翰大学中国学联
联合承办单位：
南京信息工程大学
协办单位：
玛丽居里华人学会
特别鸣谢：
中国地质科学院地质力学研究所李四光纪念馆
