Data-driven predictions of complex organic mixture permeation in polymer membranes
Young‐Joo Lee, Lihua Chen, Janhavi Nistane, Hye Youn Jang, Dylan J. Weber, Joseph K. Scott, Neel Rangnekar, Bennett D. Marshall, Wenjun Li, J.R. Johnson, Nicholas C. Bruno, M. G. Finn, Rampi Ramprasad, Ryan P. Lively
Abstract
Membrane-based organic solvent separations are rapidly emerging as a promising class of technologies for enhancing the energy efficiency of existing separation and purification systems. Polymeric membranes have shown promise in the fractionation or splitting of complex mixtures of organic molecules such as crude oil. Determining the separation performance of a polymer membrane when challenged with a complex mixture has thus far occurred in an ad hoc manner, and methods to predict the performance based on mixture composition and polymer chemistry are unavailable. Here, we combine physics-informed machine learning algorithms (ML) and mass transport simulations to create an integrated predictive model for the separation of complex mixtures containing up to 400 components via any arbitrary linear polymer membrane. We experimentally demonstrate the effectiveness of the model by predicting the separation of two crude oils within 6-7% of the measurements. Integration of ML predictors of diffusion and sorption properties of molecules with transport simulators enables for the rapid screening of polymer membranes prior to physical experimentation for the separation of complex liquid mixtures.