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Grant: Alliance Grants

Queen's University

$485,700.00

Development of a data-driven Llfe-cycle greenhouse gas emissions decision tool for municipal organic waste management

FY 2022-23

Summary
Landfill disposal of organic waste contributes to 24% of national methane emissions in Canada. Achieving net-zero greenhouse gas (GHG) emissions from the Canadian waste sector by 2050 rely on implementing the "Waste-To-Resource" approach. This approach mitigates GHG emissions from traditional end-of-life disposal practices. It also reduces GHGs by substituting fossil-based energy and resources, facilitating the transition to a low-carbon economy. Provincial Climate Change Action Plans encourage municipalities to incorporate "Waste-To-Resource" technologies. However, current decision-making on the selection of the best practices is limited due to 1) the lack of a comprehensive data repository for available technologies, 2) neglection of life cycle assessment (LCA) of GHGs when comparing different scenarios, and 3) the absence of uncertainty impact in almost all proposed decision/optimization models. This research aims to develop a data-driven life-cycle assessment (LCA)-based tool that supports the evidence-based decision-making process for selecting the best municipal organic waste management scenarios. The first phase of research involves data gathering on the performance of various configurations of bio and thermochemical technologies. The data will be collected through experimental investigations, full-scale facilities, process modeling, and the literature. The second phase includes a techno-economic assessment (TEA) of each process along with the LCA of each technology by considering all potential routes of GHG emissions (e.g., gas, liquid, and solids). The third phase involves developing a decision tool incorporating key performance indicators (KPIs), TEA, and LCA data to build predictive models under uncertainty. Utilities Kingston and The City of Kingston are partner organizations in this project. Their contribution involves but is not limited to providing full-scale facilities data, offering organic waste samples, giving access to reports or information, facilitating connections with other potential partners, and providing feedback throughout the research. The outcome of this research is expected to be of interest to both government policymakers and private sector.
Program
Alliance Grants
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
577221-2022
Name nom
Koupaie, Ehssan EO
Department département
Chemical Engineering
Organization id
29
Institution établissement
Queen's University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
485700
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
1200
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Advancement of knowledge
Area of application fr
Avancement des connaissances
Research subject code
1500
Research subject group en
Environmental engineering
Research subject group fr
Génie de l'environnement
Research subject en
Environmental engineering
Research subject fr
Génie de l'environnement
Installment
inst:1;2
Partie
2
Nb partie
2
Application title
Development of a data-driven Llfe-cycle greenhouse gas emissions decision tool for municipal organic waste management
Application summary
Landfill disposal of organic waste contributes to 24% of national methane emissions in Canada. Achieving net-zero greenhouse gas (GHG) emissions from the Canadian waste sector by 2050 rely on implementing the "Waste-To-Resource" approach. This approach mitigates GHG emissions from traditional end-of-life disposal practices. It also reduces GHGs by substituting fossil-based energy and resources, facilitating the transition to a low-carbon economy. Provincial Climate Change Action Plans encourage municipalities to incorporate "Waste-To-Resource" technologies. However, current decision-making on the selection of the best practices is limited due to 1) the lack of a comprehensive data repository for available technologies, 2) neglection of life cycle assessment (LCA) of GHGs when comparing different scenarios, and 3) the absence of uncertainty impact in almost all proposed decision/optimization models. This research aims to develop a data-driven life-cycle assessment (LCA)-based tool that supports the evidence-based decision-making process for selecting the best municipal organic waste management scenarios. The first phase of research involves data gathering on the performance of various configurations of bio and thermochemical technologies. The data will be collected through experimental investigations, full-scale facilities, process modeling, and the literature. The second phase includes a techno-economic assessment (TEA) of each process along with the LCA of each technology by considering all potential routes of GHG emissions (e.g., gas, liquid, and solids). The third phase involves developing a decision tool incorporating key performance indicators (KPIs), TEA, and LCA data to build predictive models under uncertainty. Utilities Kingston and The City of Kingston are partner organizations in this project. Their contribution involves but is not limited to providing full-scale facilities data, offering organic waste samples, giving access to reports or information, facilitating connections with other potential partners, and providing feedback throughout the research. The outcome of this research is expected to be of interest to both government policymakers and private sector.