AuLCA: augmented life cycle assessment for chemical data gaps

Area of Science:

• Environmental Science
• Chemical Engineering
• Computational Chemistry

Background:

• Life cycle assessment (LCA) is crucial for quantifying chemical process impacts but suffers from data gaps, especially for fine chemicals.
• Existing LCA databases are limited, hindering comprehensive environmental impact analysis.

Purpose of the Study:

• To develop an augmented LCA (AuLCA) framework to predict life cycle inventories and impacts of chemicals.
• To overcome data limitations in current LCA practices for a wider range of chemical products.

Main Methods:

• Utilized chemical reaction networks (CRN) to model chemical processes.
• Implemented mass-based impact propagation for inventory analysis.
• Employed first principles-based energy estimations for impact quantification.

Main Results:

• The AuLCA framework demonstrated good agreement with commercial LCA data in four case studies.
• Accuracy of AuLCA predictions correlates with the size and density of the chemical reaction network used.

Conclusions:

• AuLCA provides a robust method to estimate environmental impacts, particularly for underrepresented fine chemicals.
• This framework supports early-stage sustainable decision-making in selecting chemical reaction pathways.