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Rapid Estimation of Life Cycle Inventory
Citation:
Smith, R., D. Meyer, G. Ruiz-Mercado, B. Niblick, M. Gonzalez, V. Mittal, J. Abraham, AND W. Barrett. Rapid Estimation of Life Cycle Inventory. Life Cycle Management (LCM) 2017, Luxembourg CityL, September 03 - 06, 2017.
Impact/Purpose:
This presentation focuses on chemical life-cycle data mining and process modeling methods, including top-down and bottom-up approaches, as well as the future possibility for reconciliation between these two approaches. This research is applicable to a wide technical audience, including government agencies, industry, and academia.
Description:
Many chemical manufacturers and regulators use life cycle assessment (LCA) to manage the sustainability of chemical manufacturing processes. A significant challenge to using LCA, however, is the sheer quantity of data related to energy and material flows that needs to be collected and harmonized before it can be incorporated into rigorous sustainability analyses. Government agencies like the United States Environmental Protection Agency (U.S. EPA) maintain electronic databases of environmental flows that could be used to rapidly generate more comprehensive and reliable life cycle inventories (LCI) than those currently available. The basic approach to generating such inventories is to combine four individual efforts: 1) top-down data mining, 2) bottom-up simulation (combining material and energy balances with emissions modeling), 3) LCI chemical lineage (computationally tracing the ancestry of a chemical alternative), and 4) LCI reconciliation of both the top-down and bottom-up approaches. Together, these efforts help generate gate-to-gate LCI datasets for chemical manufacturing that include linkages between basic starting chemicals and those in final consumer products. Inventories developed through these efforts can be continually updated as new versions of the U.S. EPA databases are released and new data sources become available. Similar approaches could be taken for other countries with comparable inventory databases. In the long term, the proposed method would support the decision needs of both the U.S. EPA and the broader LCA community by generating rapid top-down chemical LCIs through automated semantic modeling tools.