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.

The production of bioethanol fuels using extractive fermentation increases the efficiency of the bioconversion reaction by reducing the toxic product inhibition. The choice of appropriate solvents to remove the bioethanol product without inhibiting the fermentation is important to enable industrial scale application. This work applies computer-aided molecular design technologies to systematically screen a wide variety of candidate solvents to enhance the separation, also considering the microorganisms that perform the fermentation. The performance of the candidates was evaluated using a rigorous process simulator for extractive fermentation, assisted by functional group-contribution (QSPR/QSAR) models for the prediction of various solvent properties, including toxicity and life cycle impacts. The solvent designs generated through this approach can provide powerful insights on the kind of molecular structures and functionalities that satisfy the process objectives and constraints, as well the desired sustainability features.

The sections in this article are Introduction Concept and Development of the FineChem Tool Illustrative Applications of the FineChem Tool LCA Aspects of Solvent Selection for Postcombustion CO 2 Capture ( PCC ) Bio‐Based Production of Platform Chemicals Toward A New Group Contribution‐Based Version of the FineChem Tool Introduction to GC models Development of GC ‐Based LCA Models Screening for Substances with Desirable Properties Illustrative Example of Screening Molecules Conclusions and Outlook

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.