Thursday, October 15th 2024
(Milan Time) 13:00 -14:00
(Beijing Time) 19:00-20:00
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Biography
Dr. Fu Zhao is a Professor with joint appointment in the School of Mechanical Engineering and the School of Environmental and Ecological Engineering at Purdue University. Dr. Zhao’s research is largely in the area of environmentally sustainable design and manufacturing. He has extensive experience conducting life cycle assessment (LCA) for a variety of products and applications, including biofuel, solar thermal systems, traditional and additive manufacturing processes. His research has been supported by NSF, DOE, DOD, EPA, NIST, as well as industry. He has published more than 130 journal papers and over 50 conference proceedings. Dr. Zhao received his BS (1993) and MS (1996) degree, both in Thermal Engineering, from Tsinghua University. He received his second MS degree in Electrical Engineering-Systems (2001) and his PhD in Mechanical Engineering (2005) from the University of Michigan.
Title: Digital Twin Enabled Life Cycle Inventory Modelling – Building the Data Foundation for Sustainable Manufacturing
Abstract
Life cycle assessment (LCA) has been widely recognised as the most objective tool to evaluate the environmental performance of a product or process. LCA recently has attracted increasing interest, in part because it is the underlying method to support carbon footprint reporting. LCA also finds applications in supporting design and development of environmentally friendly products and technologies, including features/processes that realize circular economy. Inventory analysis is a key step and data foundation of LCA– it quantifies all the material and energy inputs and outputs involved in every process of the life cycle. However, data quality and availability at unit process level have been cited as the biggest challenges faced by LCA community. Current life cycle inventory (LCI) databases have serious shortcomings and limitations: they are usually static and outdated, carry large uncertainties, are rather generic (reflecting only representative or average industrial processes), and lack datasets on use phase and end of life management. Digital twin technologies create virtual replicates of physical assets by using high fidelity models and real time data collected from installed sensors. This opens up critical opportunities to advance LCI modeling. This talk will discuss the potential of using digital twins to address the major shortcomings and limitations of existing LCI modeling approaches as related to carbon footprint reporting at product level and green product/process development. Moreover, to apply digital twin based LCI modeling at supply chain or corporation level and to benefit the LCA community, algorithms and approaches that could enable proprietary information protection, data sharing, data validation, rewarding mechanisms are needed.