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 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 Terms & Conditions Electronic Supporting Information files are available without a subscription to ACS Web Editions.

A framework is presented to address the toolbox of chemical release estimation methods available for manufacturing processes. Although scientists and engineers often strive for increased accuracy, the development of fit-for-purpose release estimates can speed results that could otherwise delay decisions important to protecting human health and the environment. A number of release estimation approaches are presented, with the newest using decision trees for regression and prediction. Each method is evaluated in a case study for cumene production to study the reconciliation of data quality concerns and requirements for time, resources, training, and knowledge. The evaluation of these decision support criteria and the lessons learned are used to develop a purpose-driven framework for estimating chemical releases.