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AuLCA: augmented life cycle assessment for chemical data gaps.

Maximilian G Hoepfner1,2, Dion Jakobs1, Lucas F Santos1,2

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This study introduces an augmented life cycle assessment (AuLCA) framework using chemical reaction networks to predict chemical impacts, addressing data gaps for fine chemicals and supporting sustainable process selection.

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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.