Sorbent Performance Estimation
Adsorption equilibrium information is a fundamental resource to evaluate the performance of sorbent candidates for application at process scale.36 We estimated CO2 and N2 single-component adsorption isotherms by Grand Canonical Monte Carlo (GCMC) simulations37,38 in UiO-66 and MIL-101(Cr). This technique has been extensively used to estimate the adsorption properties of CO2 and similar species in a range of porous sorbent materials.39-44GCMC simulations were carried out at a range of operating conditions for which we performed design calculations, i.e., sub-ambient temperatures ranging from 213 to 273 K. The details of molecular modeling are provided in the Supporting Information S4.1.1. We then employed Ideal Adsorbed Solution Theory (IAST)45 to predict the bulk 0.14:0.86 CO2:N2 mixture adsorption equilibrium. IAST predicts the mixture equilibrium from single-component adsorption isotherms under the assumption that an ideal solution is formed by the adsorbed phase.46-48 H2O was not considered in mixture equilibrium assuming only a trace amount remains in a feed into the separation unit after pretreatment of the flue gas. The mixture isotherms were used to estimate sorbent performance via the full-order model outlined below for fiber sorbents with heat management. For zeolite 13X, we applied the extended dual-site Langmuir (DSL) model reported by Haghpanah et al.19(Table S10) for CO2:N2 mixture equilibrium evaluation.
A fixed-bed packed with thermally-modulated fiber composites31,32 was used for modeling of a pressure swing adsorption (PSA) cycle. A complete account of details describing this model can be found in Rubiera Landa et al49 and the Supplemental Information S4.1.2. The cycle employed for the VPSA process consists of four steps: 1. light product pressurization, 2. adsorption, 3. co-current blowdown, and 4. counter current evacuation. To compute Pareto frontiers efficiently the VPSA-cycle simulation is treated as a black-box and the multi-objective optimization algorithm ‘Surrogate Optimization of Computationally Expensive Multiobjective Problems’ (SOCEMO), developed by Müller50 was implemented in Matlab.51 This optimizer allows computation of Pareto frontiers at a fraction of the computational cost required for other commonly applied genetic algorithms.52 The Pareto frontier values (purity, recovery, system operating conditions) were integrated into the Aspen Plus process model to simulate performance of different adsorbent and cycle design choices.19