RESUMO
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.
RESUMO
Metal nanoparticles encapsulated within metal organic frameworks (MOFs) offer steric restrictions near the catalytic metal that can improve selectivity, much like in enzymes. A microkinetic model is developed for the regio-selective oxidation of n-butane to 1-butanol with O2 over a model for MOF-encapsulated bimetallic nanoparticles. The model consists of a Ag3Pd(111) surface decorated with a 2-atom-thick ring of (immobile) helium atoms which creates an artificial pore of similar size to that in common MOFs, which sterically constrains the adsorbed reaction intermediates. The kinetic parameters are based on energies calculated using density functional theory (DFT). The microkinetic model was analysed at 423 K to determine the dominant pathways and which species (adsorbed intermediates and transition states in the reaction mechanism) have energies that most sensitively affect the reaction rates to the different products, using degree-of-rate-control (DRC) analysis. This analysis revealed that activation of the C-H bond is assisted by adsorbed oxygen atoms, O*. Unfortunately, O* also abstracts H from adsorbed 1-butanol and butoxy as well, leading to butanal as the only significant product. This suggested to (1) add water to produce more OH*, thus inhibiting these undesired steps which produce OH*, and (2) eliminate most of the O2 pressure to reduce the O* coverage, thus also inhibiting these steps. Combined with increasing butane pressure, this dramatically improved the 1-butanol selectivity (from 0 to 95%) and the rate (to 2 molecules per site per s). Moreover, 40% less O2 was consumed per oxygen atom in the products. Under these conditions, a terminal H in butane is directly eliminated to the Pd site, and the resulting adsorbed butyl combines with OH* to give the desired 1-butanol. These results demonstrate that DRC analysis provides a powerful approach for optimizing catalytic process conditions, and that highly selectivity oxidation can sometimes be achieved by using a mixture of O2 and H2O as the oxidant. This was further demonstrated by DRC analysis of a second microkinetic model based on a related but hypothetical catalyst, where the activation energies for two of the steps were modified.