Prediction of Environmental Properties Using a Hybrid Group Contribution Approach

Abstract

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 Daphnia magna 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 structure of a given compound (i.e. functional groups), is applied to develop models for selected properties using robust regression with outlier treatment. Second, a hybrid approach using only the first order GC-defined functional groups as predictors is presented to develop a number of data-driven models (a feedforward neural network (ANN) and a radial basis function network (RBFN), regression tree, etc.). Performance of the different models in predicting ODP, GWP, and LC50 is assessed for various industrially relevant chemicals and compared with results of the classical GC method. The experimental data for the selected properties is collected from the databases of Environmental Protection Agency (EPA) and the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC). The results showed that hybrid approach presents significant improvement on estimation accuracy for the considered environmental properties. This flexible approach builds on the basis of GC theory and extends 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 the wider domain of process systems engineering.