Prediction of Environmental Properties Using a Hybrid Group Contribution Approach

Resul Al, Jérôme Frutiger, Alexandr Zubov, Gürkan Sin

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Development of predictive environmental property models is increasingly becoming crucial as stringent regulations for substances with high global warming and ozone depletion potentials are being introduced. This contribution presents new environmental property models using two group contribution (GC) based approaches for the prediction of ozone depletion potential (ODP), global warming potential (GWP) and Daphniamagna lethal concentration (LC50, 48 hr), concentration of the test chemical in water(mg/L) that causes 50% of Daphnia magna to die after 48 hours. First, the classical group contribution approach, in which a property of interest is estimated from regression models that make use of available information about the chemical structureof a given compound (i.e. functional groups), is applied to develop models for selectedproperties using robust regression with outlier treatment. Second, a hybrid approachusing only the first order GC-defined functional groups as predictors is presented todevelop a number of data-driven models (a feedforward neural network (ANN) and aradial basis function network (RBFN), regression tree, etc.). Performance of thedifferent models in predicting ODP, GWP, and LC50 is assessed for various industriallyrelevant chemicals and compared with results of the classical GC method. Theexperimental data for the selected properties is collected from the databases ofEnvironmental Protection Agency (EPA) and the fifth assessment report of theIntergovernmental Panel on Climate Change (IPCC). The results showed that hybridapproach presents significant improvement on estimation accuracy for the consideredenvironmental properties. This flexible approach builds on the basis of GC theory andextends it with nonlinear surrogate models to better describe the property of interest,which makes it a promising method to improve accuracy of property models in thewider domain of process systems engineering.
Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018
EditorsMario R. Eden, Marianthi G. Ierapetritou, Gavin P. Towler
Volume44
PublisherElsevier
Publication date2018
Pages1723-1728
ISBN (Electronic)978-0-444-64241-7
DOIs
Publication statusPublished - 2018
Event13th International Symposium on Process Systems Engineering (PSE 2018) - San DIego, United States
Duration: 1 Jul 20185 Jul 2018

Conference

Conference13th International Symposium on Process Systems Engineering (PSE 2018)
CountryUnited States
CitySan DIego
Period01/07/201805/07/2018
SeriesComputer Aided Chemical Engineering
ISSN1570-7946

Keywords

  • Predictive modeling
  • Group contribution method
  • Radial basis function network
  • Artificial neural networks
  • Environmental properties

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