Molecular-structure-based models of chemical inventories using neural networks

Chemical synthesis is a complex and diverse procedure, and production data are often scarce or incomplete. A detailed inventory analysis of all mass and energy flows necessary for the production of chemicals is often costly and time-intensive. Therefore only few chemical inventories exist, even though they are essential for process optimization and the environmental assessment of many products. This paper introduces a new type of model to provide estimates for inventory data and environmental impacts of chemical production based on the molecular structure of a chemical and without a priori knowledge of the production process. These molecular-structure-based models offer inventory data for users in process design and optimization, screening life cycle assessment (LCA), and supply chain management. They can be applied even if the producer is unknown or the production process is not documented. We assessed the capabilities of linear regression and neural network models for this purpose. All models were generated with a data set of inventory data on 103 chemicals. Different input sets were chosen as ways to transform the chemical structure into a numerical vector of descriptors and the effectiveness of the different input sets was analyzed. The results show that a correctly developed neural network model can perform on an acceptable level for many purposes. The models can assist process developers to improve energy efficiency in all design stages and aid in LCA and supply chain management by filling data gaps.