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Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-WorldChemical Production
Citation:
Mittal, V., S. Bailin, M. Gonzalez, D. Meyer, W. Barrett, AND R. Smith. Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-WorldChemical Production. ACS Sustainable Chemistry & Engineering. American Chemical Society, Washington, DC, 6:1961-1976, (2017).
Impact/Purpose:
This journal article is a product for the Chemical Safety for Sustainability Life Cycle - Human Exposure Modeling project. The work provides an important step towards automating life cycle inventories.
Description:
A set of coupled semantic data models, i.e., ontologies, are presented to advance a methodology towards 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 steps. 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 series of SPARQL queries, based on the concepts of ancestor and parent, are presented to generate a lineage for a chemical of interest from a set of reaction data. The proposed ontologies and SPARQL queries are evaluated and tested using a case study of Nylon 6 production. Once a lineage is established, the process ontology can be used to guide inventory modeling based on both data mining (top-down) and simulation (bottom-up) approaches. The ability to generate a cradle-to-gate life cycle for a chemical represents a key achievement towards the ultimate goal of automated life cycle inventory modeling.
