Mittal Vinit K, Bailin Sidney C, Gonzalez Michael A, Meyer David E, Barrett William M, Smith Raymond L
Oak Ridge Institute of Science and Education (ORISE), Hosted by U.S. Environmental Protection Agency, Office of Research and Development, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States.
Knowledge Evolution, Inc., 1748 Seaton Street NW, Washington, D.C. 20009, United States.
ACS Sustain Chem Eng. 2017 Dec 6;6(2):1961-1976. doi: 10.1021/acssuschemeng.7b03379.
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 chemical represents a key achievement toward the ultimate goal of automated life cycle inventory modeling.
提出了一组耦合语义数据模型，即本体，以推进一种用于生命周期评估中化学制造自动化库存建模的方法。化学制造从摇篮到大门的生命周期清单是与化学品供应链相关的物质和能量流的详细集合。因此，需要管理描述化学品的谱系（或合成途径）和加工条件的数据。为此，提出了一种谱系本体，以揭示从原油或生物材料等原材料生产化学品所需的所有合成步骤，同时开发了一种过程本体来管理描述与每个合成步骤相关的各种单元过程的数据。这两个本体相互耦合，使得作为库存建模基础的过程数据通过化学反应和反应参与者等关键概念与谱系数据相链接。为了促进自动化库存建模，提出了一系列基于祖先和父概念的SPARQL查询，以便从一组反应数据中生成感兴趣化学品的谱系。使用尼龙-6生产的案例研究对所提出的本体和SPARQL查询进行了评估和测试。一旦建立了谱系，过程本体就可用于基于数据挖掘（自上而下）和模拟（自下而上）方法来指导库存建模。生成化学品从摇篮到大门的生命周期的能力代表了朝着自动化生命周期库存建模的最终目标迈出的关键一步。
