Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production

A set of coupled semantic data models, i.e., ontologies, are presented to advance a methodology toward automated inventory modeling of chemical manufacturing in life cycle assessment. The cradle-to-gate life cycle inventory for chemical manufacturing is a detailed collection of the material and energy flows associated with a chemical’s supply chain. Thus, there is a need to manage data describing both the lineage (or synthesis pathway) and processing conditions for a chemical. To this end, a Lineage ontology is proposed to reveal all the synthesis steps required to produce a chemical from raw materials, such as crude oil or biomaterials, while a Process ontology is developed to manage data describing the various unit processes associated with each synthesis step. The two ontologies are coupled such that process data, which is the basis for inventory modeling, is linked to lineage data through key concepts like the chemical reaction and reaction participants. To facilitate automated inventory modeling, a s...