From Reaction Stoichiometry to Life Cycle Assessment: Decision Tree-Based Estimation Tool

Area of Science:

• Chemical Engineering
• Environmental Science
• Sustainable Chemistry

Background:

• Early-stage research and development (R&D) decision-making requires both economic and ecological insights.
• Life Cycle Assessment (LCA) is crucial for evaluating environmental effects but often lacks prospective application in early R&D.
• Current methods for early-stage LCA are descriptive and need enhancement to predict future process impacts.

Purpose of the Study:

• To develop a prospective tool for early-stage Life Cycle Assessment (LCA) of chemical processes.
• To enable environmental impact assessment using only the chemical reaction equation.
• To provide decision-makers with ecological data comparable in accuracy to economic cost projections.

Main Methods:

• Proposed regression trees to estimate key inputs for industry-scale life-cycle inventories.
• Utilized chemical reaction equations as the primary input data.
• Estimated raw material impacts, direct greenhouse gas (GHG) emissions (CO2eq), and demands for utilities (electricity, steam, natural gas, water).

Main Results:

• Regression trees provide accurate estimations for crucial life-cycle inventory inputs.
• The method successfully distinguishes between different chemical processes, avoiding single-value limitations.
• Estimated inventory data demonstrates accuracy comparable to traditional cost estimations.

Conclusions:

• Regression trees offer a robust method for prospective, early-stage LCA of chemical processes.
• This approach allows for the integration of environmental considerations alongside economic factors in R&D.
• Enables informed decision-making by providing reliable environmental impact data early in the R&D pipeline.