The thermal distillation of crude oil mixtures is an energy-intensive process, accounting for nearly 1% of global energy consumption. Membrane-based separations are an appealing alternative or tandem process to distillation due to intrinsic energy efficiency advantages. We developed a family of spirocyclic polytriazoles from structurally diverse monomers for membrane applications. The resulting polymers were prepared by a convenient step-growth method using copper-catalysed azide-alkyne cycloaddition, providing very fast reaction rates, high molecular weights and solubilities in common organic solvents and non-interconnected microporosity. Fractionation of whole Arabian light crude oil and atmospheric tower bottom feeds using these materials enriched the low-boiling-point components and removed trace heteroatom and metal impurities (comparable performance with the lighter feed as the commercial polyimide, Matrimid), demonstrating opportunities to reduce the energy cost of crude oil distillation with tandem membrane processes. Membrane-based molecular separation under these demanding conditions is made possible by high thermal stability and a moderate level of dynamic chain mobility, leading to transient interconnections between micropores, as revealed by the calculations of static and swollen pore structures.
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.
Hydrocarbon separation relies on energy-intensive distillation. Membrane technology can offer an energy-efficient alternative but requires selective differentiation of crude oil molecules with rapid liquid transport. We synthesized multiblock oligomer amines, which comprised a central amine segment with two hydrophobic oligomer blocks, and used them to fabricate hydrophobic polyamide nanofilms by interfacial polymerization from self-assembled vesicles. These polyamide nanofilms provide transport of hydrophobic liquids more than 100 times faster than that of conventional hydrophilic counterparts. In the fractionation of light crude oil, manipulation of the film thickness down to ~10 nanometers achieves permeance one order of magnitude higher than that of current state-of-the-art hydrophobic membranes while retaining comparable size- and class-based separation. This high permeance can markedly reduce plant footprint, which expands the potential for using membranes made of ultrathin nanofilms in crude oil fractionation.
