(644d) Mass Transport Modeling in Membrane-Based Chemical Separations

Increasing the supply of renewable chemicals and fuels is critical to the deep decarbonization of a multitude of industrial products from polymers to pharmaceuticals. Biomass-derived feedstocks can simultaneously meet the growing demand for sustainable chemicals while reducing our reliance on fossil fuels and minimizing industrial greenhouse gas (GHG) emissions. However, the current production of chemicals can be energy intensive. Separation and purification accounts for over 40% of the energy consumed during the production of key chemicals ranging from ethanol and phenol to acetic acid and styrene.^1,2 Globally, separations processes account for a substantial portion of industrial energy consumption and GHG emissions. Thermal distillation forms an integral part of many chemical separations, separating mixtures by the volatility of their constituent components. Although widely used, distillation is inherently energy intensive, requiring large amounts of heat to drive vaporization, and accounting for 25% of US industrial energy consumption.^1–5

Membrane-based processes, such as organic solvent reverse osmosis (OSRO), separate mixtures based on the size and charge of their constituent components, rather than their volatility. OSRO utilizes hydraulic pressure to create a chemical potential gradient to drive permeation through a selective membrane, splitting the feed into high and low permeability fractions. By avoiding the energy intensive vapor-liquid phase change, OSRO can drastically reduce the energy footprint of chemical separations.^6,7 Developing reliable thermodynamic models combined with a thorough understanding of transmembrane mass transport phenomena can accelerate future membrane design. However, quantifying chemical potential and mass transfer rates in industrially relevant mixtures is challenging due to the large number of components and functional groups.

In this investigation, we study the separation of benzene and its derivatives using nanoporous nanosheet membranes. We begin by building a computational platform to calculate the chemical potential driving force for the transmembrane permeation of non-ideal liquid-phase mixtures. An equation of state is combined with an excess Gibbs free energy model using the Wong-Sandler mixing rules, to quantify liquid-phase fugacity across a large pressure range. Interaction parameters are estimated through the nonlinear regression of fluid phase equilibrium and activity coefficient data. We then develop a computational model for multicomponent transmembrane transport, using the Maxwell-Stefan framework to capture the thermodynamic and diffusive coupling between molecular species in the diffusive boundary layer. Finally, we integrate transmembrane mass transport expressions to calculate how feed flow rate and composition vary along a membrane module. By elucidating the impact of membrane permeability and selectivities on energy consumption and separation efficacy in OSRO, we endeavor to guide future membrane materials and process development.

References:

1. S. Brueske, C. Kramer and A. Fisher, Bandwidth Study on Energy Use and Potential Energy Saving Opportunities in U.S. Chemical Manufacturing, 2015.
2. S. Brueske, R. Sabouni, C. Zach and H. Andres, U.S. Manufacturing Energy Use and Greenhouse Gas Emissions Analysis: Petroleum Refining Sector, 2012.
3. D. S. Sholl and R. P. Lively, Nature, 2016, 532, 435–437.
4. R. P. Lively and D. S. Sholl, Nature Materials, 2017, 16, 276–279.
5. R. P. Lively, AIChE Journal, 2021, 67, 1–11.
6. D.-Y. Koh, B. A. McCool, H. W. Deckman and R. P. Lively, Science, 2016, 353, 804–807.
7. K. A. Thompson, R. Mathias, D. Kim, J. Kim, N. Rangnekar, J. R. Johnson, S. J. Hoy, I. Bechis, A. Tarzia, K. E. Jelfs, B. A. McCool, A. G. Livingston, R. P. Lively and M. G. Finn, Science, 2020, 369, 310–315.