Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict...

Area of Science:

• Chemical Engineering
• Life Cycle Assessment
• Semantic Data Modeling

Background:

• Chemical manufacturing life cycle inventories require detailed data on material and energy flows.
• Managing synthesis pathways (lineage) and processing conditions is crucial for accurate assessments.
• Existing methods lack robust data management for complex chemical supply chains.

Purpose of the Study:

• To develop coupled semantic data models (ontologies) for automated chemical manufacturing life cycle inventory modeling.
• To establish a methodology for managing chemical lineage and process data.
• To facilitate the generation of cradle-to-gate life cycle inventories.

Main Methods:

• Development of a Lineage ontology to map chemical synthesis steps.
• Development of a Process ontology to manage unit process data.
• Coupling ontologies via chemical reactions and participants.
• Utilizing SPARQL queries for automated lineage generation.
• Case study evaluation using nylon-6 production.

Main Results:

• A coupled ontology framework linking chemical lineage and process data was successfully developed.
• SPARQL queries enabled automated generation of chemical synthesis lineages.
• The methodology was validated through a nylon-6 production case study.
• Demonstrated ability to guide inventory modeling using both top-down and bottom-up approaches.

Conclusions:

• The proposed ontologies and SPARQL queries advance automated life cycle inventory modeling for chemical manufacturing.
• This framework provides a robust method for managing complex chemical supply chain data.
• The approach facilitates efficient cradle-to-gate life cycle assessment generation.