Grant: Alliance Grants
University of Waterloo
$499,692.00
Remanufacturing - A manufacturing paradigm shift for deep decarbonization in a sustainable economy
EF 2023-24
Résumé
Every stage of manufacturing, from raw materials extraction, refinement, and processing to part manufacturing contributes immensely to greenhouse gas (GHG) emissions. A shift toward re-manufacturing, where worn products or component parts are returned to like-new condition, will dramatically reduce anthropogenic GHG emissions caused by material production and distribution, help industries achieve stringent environmental mandates (net zero GHG by 2050), and contribute directly to a circular economy. The proposed project will connect expertise across mechanical and environmental engineering, computer science, supply chain and logistics, and climate change governance and sustainability to drive advances in the technologies needed for part assessment and remanufacturing, and generate data needed to build understanding and predictive models that capture the benefits of a remanufacturing-based decarbonization strategy and how it will impact policy. Specifically, the proposed research includes life assessment methodology for identifying damaged zones of parts at the end of their life, remanufacturing technology (cold spray additive manufacturing) to replace damaged parts, reverse logistics to design the optimum operation for accessing parts at the end of their life cycle and transferring them to local remanufacturers, assessment of GHG savings for one exemplar component, and forecast models that predict savings across different scenarios (e.g., for the automotive industry, we would forecast savings for exemplar components in all new cars manufactured by 2035, and 2050). A Transfer Learning algorithm will be employed to extend proposed methodology to other components. Policy barriers at federal, provincial, and municipal levels will be analyzed and mitigation strategies for sustainable transition to remanufacturing will be developed.The proposed strategy will significantly reduce GHG, provide exceptional economic opportunities for SME, and generate required data for decision makers to propose policies based on remanufacturing.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
577125-2022
Name nom
Jahed, Hamid HR
Department département
Mechanical Engineering and Mechatronics
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2022
Award amount
499692
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
500
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Manufacturing processes and products
Area of application fr
Procédés et produits industriels
Research subject code
1500
Research subject group en
Environmental engineering
Research subject group fr
Environmental engineering
Research subject en
Génie de l'environnement
Research subject fr
Génie de l'environnement
Application title
Remanufacturing - A manufacturing paradigm shift for deep decarbonization in a sustainable economy
Keyword
Circular economy; Cold spray additive manufacturing; Deep decarbonization; GHG policy; GHG simulation and forecasting; Life assessment; Remanufacturing; Reverse logistics; Sustainability Governance; Transfer learning
Application summary
Every stage of manufacturing, from raw materials extraction, refinement, and processing to part manufacturing contributes immensely to greenhouse gas (GHG) emissions. A shift toward re-manufacturing, where worn products or component parts are returned to like-new condition, will dramatically reduce anthropogenic GHG emissions caused by material production and distribution, help industries achieve stringent environmental mandates (net zero GHG by 2050), and contribute directly to a circular economy. The proposed project will connect expertise across mechanical and environmental engineering, computer science, supply chain and logistics, and climate change governance and sustainability to drive advances in the technologies needed for part assessment and remanufacturing, and generate data needed to build understanding and predictive models that capture the benefits of a remanufacturing-based decarbonization strategy and how it will impact policy. Specifically, the proposed research includes life assessment methodology for identifying damaged zones of parts at the end of their life, remanufacturing technology (cold spray additive manufacturing) to replace damaged parts, reverse logistics to design the optimum operation for accessing parts at the end of their life cycle and transferring them to local remanufacturers, assessment of GHG savings for one exemplar component, and forecast models that predict savings across different scenarios (e.g., for the automotive industry, we would forecast savings for exemplar components in all new cars manufactured by 2035, and 2050). A Transfer Learning algorithm will be employed to extend proposed methodology to other components. Policy barriers at federal, provincial, and municipal levels will be analyzed and mitigation strategies for sustainable transition to remanufacturing will be developed.The proposed strategy will significantly reduce GHG, provide exceptional economic opportunities for SME, and generate required data for decision makers to propose policies based on remanufacturing.
