NCCR Catalysis sub-project 2: Development of a pre-LCA framework for assessing new chemicals

The project is carried out by Dr. Dachuan Zhang. In the short video here above, he gives a brief overview of this project (if the video is not available in your region, please use external page this link).

Life cycle assessments (LCA) have become a key method to evaluate the environmental sustainability of chemical products and processes. However, life-cycle inventories are lacking for most chemicals, hampering environmental impact assessments of them. Over 100 million chemicals are registered and in nearly all cases there is insufficient data to carry out LCAs. Therefore, during the early design stages of molecule and process, model-based estimations are required to fill the inevitable data gaps. This project aims to use state-of-the-art techniques of machine learning to address data deficiencies and improve quality of LCAs for both basic and fine chemicals with varied complexity in the molecular structure, building upon Wernet et al. (2009). This tool would complement existing public and industry LCA databases of chemicals, particularly by screening LCA results of complex chemicals that are otherwise not available. The primary use of the tool would be for evaluating design of chemical molecules and processes in order to allow development of greener chemicals and processes.

G. Wernet, S. Papadokonstantakis, S. Hellweg, and K. Hungerbühler, “Bridging data gaps in environmental assessments: Modeling impacts of fine and basic chemical production,” Green Chem., vol. 11, no. 11, pp. 1826–1831, 2009.