Integrating Life Cycle Assessment, Techno-Economic Analysis, Process Optimization, and Multi-Criteria Decision-Making Approaches for Holistic Sustainability Assessment

Main Presenter:    Jannatul Ferdous 

Co-Authors:   Farid Bensebaa     Nathan Pelletier                                          

Life Cycle Assessment (LCA) is commonly used to quantify the environmental impacts of a product system throughout its life cycle. Techno-Economic Analysis (TEA) is used to assess the technical and economic feasibility of a product system – most frequently industrial processes. Although it is common practice to employ them separately, examples of integrated applications can be found in the literature, but a notable gap exists with respect to agri-food processing industries. Process simulation, multi-objective optimization, and multi-criteria decision-making approaches can also be combined with LCA and TEA in order to support more holistic sustainability decision-making, but coherent frameworks for such an integration are lacking. Considering the increasing demand for processed agri-food products and the evolving nature of the industrial processes involved, a framework to integrate LCA, TEA, process simulation, multi-objective optimization, and multi-criteria decision-making approaches
in agri-food processing contexts was developed. For integrating LCA and TEA, the main concerns are the system boundary and functional units. The studies reviewed often employed different system boundaries and functional units, which is not recommended. As process simulation-based TEA mainly targets industry gate-to-gate systems, the system boundaries used in LCAs of the same processes typically differ (i.e., cradle to gate or cradle to grave. It is strongly recommended to use the same functional unit (output-based) for both LCA and TEA to make them comparable. As for industrial processes, collecting primary data is often challenging or impossible. In such cases, process simulation software can facilitate data gap-filling for both LCA and TEA. Additionally, genetic-algorithm-based multi-objective optimization methods can be applied to identify the most optimized and sustainable process based on environmental (i.e., carbon footprint, land use footprint), technical (i.e., yield, energy
use efficiency), and economic (i.e., cost, profit) criteria. This optimization will result in a solution set, hence multi-criteria decision-making approaches (combination of weighting and ranking methods) can facilitate identifying the best solution from the non-dominated solution set. The unique feature of this integrated framework is the inclusion of GIS models for spatially explicit LCA of raw material production to determine if the results change significantly based on the location of raw material production.

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