(732q) Physical Property Data and Models in Support of Bioprocessing Separation Technologies for Organic Acids Separation

This project forms an alliance of academia-industry-national lab partners to develop a methodology for modeling the full process of bio-separation units for both single and multi-component systems. The methodology includes the collection and incorporation of physical property data into advanced thermodynamic models, the development of advanced thermodynamic models, and their connection to commercial process simulation environments, supporting the scale-up and development of membrane and (electro)sorption-based processes for separation and recovery of organic acids from aqueous solutions (e.g., fermentation broth, lignin-rich streams, etc.). By bringing together a combination of experimental and modeling expertise, the project aims to facilitate the scale-up predictions and design of key bioseparation technologies for organic acids separations. The project team leverages a systematic experimental data generation to build thermodynamic models that will be input into bioseparation process models along with the relevant kinetic parameters. The process models will be compared and verified to scale up separations processes for pilot experiments. The simulation software vendor partners will support and guide the implementation and application of the data and models in commercial process simulators. The development of advanced Machine Learning (ML)-based models to speed up the simulation performance will also be considered.