Life Cycle Management of F-Gas-Free Refrigeration Technology: The Case of F-Gases-Free Frozen Dessert Equipment

Cappellaro, Francesca; Barberio, Grazia; Masoni, Paolo

doi:10.1007/978-94-007-1899-9_51

Cited by 2 publications

(2 citation statements)

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“…(25) CASCADE, another ontology project, was designed to interpret LCA information by using existing product data management. (26) The work by Edrisi et al focused on the development of an ontology for information and knowledge sharing for an enterprise decision-making support tool. ( 27) A case study for a production process demonstrated the use of ontology in simultaneous consideration of environmental effects and an economic indicator.…”

Section: Semantic Data and Predictive Chemistrymentioning

confidence: 99%

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

Mittal

Bailin²,

Gonzalez

et al. 2017

ACS Sustainable Chem. Eng.

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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.

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Section: Semantic Data and Predictive Chemistrymentioning

confidence: 99%

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

Mittal

Bailin²,

Gonzalez

et al. 2017

ACS Sustainable Chem. Eng.

View full text Add to dashboard Cite

show abstract

“…The environmental impacts throughout the life cycle of a commercial refrigeration system employing HCFCs, HFCs and natural refrigerants were quantified and compared (Bovea et al, 2007), but the authors did not consider indirect emissions caused by energy use. Using the life cycle concept, Cappellaro et al (2011) evaluated the F-gas-free refrigeration technology using the individual criteria energy consumption, GWP, and Ozone Depletion Potential (ODP), but did not consider them together. The life cycle CO 2 -equivalent emissions of CFC-12 and its substitute, HFC-134a, and an emerging HFE were quantified by Blowers and Lownsbury (2010), considering both direct and indirect emissions.…”

Section: Introductionmentioning

confidence: 99%

Reduction of potential greenhouse gas emissions of room air-conditioner refrigerants: a life cycle carbon footprint analysis

Zhao

Zeng

Yuan

2015

Journal of Cleaner Production

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Life Cycle Management of F-Gas-Free Refrigeration Technology: The Case of F-Gases-Free Frozen Dessert Equipment

Cited by 2 publications

References 1 publication

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

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

Reduction of potential greenhouse gas emissions of room air-conditioner refrigerants: a life cycle carbon footprint analysis

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