Group Contribution-based LCA models to enable screening for
environmentally benign novel chemicals in CAMD applications
Abstract
This study considers the development of suitable models for the
estimation of Life Cycle Assessment (LCA) indices of organic chemicals
based on their molecular structure. The models developed here follow the
well-established Group-Contribution (GC) approach and a variety of
regression and non-regression methodologies are recruited to achieve the
optimum correlation. These models can then be used, alongside other GC
models, to screen for molecules with optimal and/or desirable
properties, using appropriate molecular design synthesis algorithms. The
LCA indices considered here are the Global Warming Potential (GWP),
Cumulative Energy Demand (CED) and EcoIndicator 99 (EI99). The model
development uses data from existing LCA databases, where each material
is associated with its cradle-to-gate LCA metrics, GWP, CED and EI99.
The paper presents the model development results, and applies the
proposed LCA models on a typical case study for the design of
LL-extraction solvents to separate an n-butanol – water mixture.