SwePub - sökning: WFRF:(Baxevanidis Pantelis)

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1.	Baxevanidis, Pantelis, et al. (författare) Group contribution-based LCA models to enable screening for environmentally benign novel chemicals in CAMD applications 2022 Ingår i: AICHE Journal. - : Wiley. - 1547-5905 .- 0001-1541. ; 68:3 Tidskriftsartikel (refereegranskat)abstract This study considers the development of suitable models for the estimation of life cycle assessment (LCA) indices of organic chemicals. Unlike state-of-the-art models, the tools developed here correlate LCA indices with the molecular composition according to the well-established group contribution (GC) approach. The LCA indices considered here are global warming potential, cumulative energy demand, and Eco-Indicator 99. The model development uses data from existing LCA databases, where each material is associated with its cradle-to-gate LCA metrics. A variety of regression and nonregression methodologies are recruited to achieve the optimum correlation. GC models can be used to screen for molecules with optimal and/or desirable properties, using appropriate molecular design synthesis algorithms. In this framework, the models developed here are linked to the design algorithm to enable the consideration of LCA features together with other properties, for the design of environmentally benign liquid–liquid extraction solvents.

Baxevanidis, Pantelis, et al. (författare)
Group contribution-based LCA models to enable screening for environmentally benign novel chemicals in CAMD applications
2022
Ingår i: AICHE Journal. - : Wiley. - 1547-5905 .- 0001-1541. ; 68:3
Tidskriftsartikel (refereegranskat)abstract
- This study considers the development of suitable models for the estimation of life cycle assessment (LCA) indices of organic chemicals. Unlike state-of-the-art models, the tools developed here correlate LCA indices with the molecular composition according to the well-established group contribution (GC) approach. The LCA indices considered here are global warming potential, cumulative energy demand, and Eco-Indicator 99. The model development uses data from existing LCA databases, where each material is associated with its cradle-to-gate LCA metrics. A variety of regression and nonregression methodologies are recruited to achieve the optimum correlation. GC models can be used to screen for molecules with optimal and/or desirable properties, using appropriate molecular design synthesis algorithms. In this framework, the models developed here are linked to the design algorithm to enable the consideration of LCA features together with other properties, for the design of environmentally benign liquid–liquid extraction solvents.

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