A hybrid modelling approach to compare chemical separation technologies in terms of energy consumption and carbon dioxide emissions

Abstract Accurate energy system modelling of chemical separations is a critical component of technology selection to minimize operating costs, energy consumption and emissions. Here we report a hybrid modelling approach based on data-driven and mechanistic models to holistically compare chemical separation performance. Our model can be used to select the most suitable technology for a given chemical separation, such as membrane separation, evaporation, extraction or hybrid configurations, by training a machine learning model to predict solute rejection using an open-access membrane dataset. We estimated an average 40% reduction in energy consumption and carbon dioxide emissions for industrially relevant separations using our methodology. We predicted and analysed 7.1 million solute rejections across several industrial sectors. Pharmaceutical purification could realize carbon dioxide emissions reductions of up to 90% by selecting the most efficient technology. We mapped the reduction in carbon dioxide emissions and the reduction in operating costs globally, establishing parameter thresholds to facilitate corporate and governmental decision-making.