Recent advances in adsorptive gas separations have focused on the development of porous materials with high operating capacity and selectivity, useful parameters that provide early guidance during the development of new materials. Although this material-focused work is necessary to advance the state of the art in adsorption science and engineering, a substantial problem remains: how to integrate these materials into a fixed bed to efficiently utilize the separation. Structured sorbent contactors can help manage kinetic and engineering factors associated with the separation, including pressure drop, sorption enthalpy effects, and external heat integration (for temperature swing adsorption, or TSA). In this review, we discuss monoliths and fiber sorbents as the two main classes of structured sorbent contactors; recent developments in their manufacture; advantages and disadvantages of each structure relative to each other and to pellet packed beds; recent developments in system modeling; and finally, critical needs in this area of research.

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A mathematical model is developed for carbon dioxide adsorption on hollow fiber post infused with physically impregnated poly(ethylenimine) silica sorbent. The model includes detailed mass transfer mechanisms and is rigorously validated under different operating conditions of flue gas flow rate, number of fibers within the module, fiber length and particle size. A fundamental model formulation for the sorbent polymer diffusivity including the temperature and the sorbent concentration dependency is proposed. The proposed model formulation is based on free volume theory model of polymer diffusion and effectively predicts the experimental observations. The model is able to predict the breakthrough curves at many different operating conditions and module designs such as conditions varied with flue gas flow rate, fiber length, fiber packing fraction and support silica particle size. The concave trend in the temperature dependency of breakthrough capacity that shows a maximum around 45 o C-60 o C is analyzed using the developed model.

The dynamic CO 2 sorption performance of polymer/silica supported polyethylenimine hollow fiber sorbents (CA-S-PEI), focusing on heat and mass transport effects, is investigated experimentally and computationally during sorption of CO 2 from simulated, dry flue gases. The effect of the nonisothermality on the sorption performance is investigated by varying the module materials of construction. The heat effects are minimized by using a heat conductive module case with a diameter of 0.25 in., and, accordingly, the breakthrough capacities are increased by 30% over a similar module constructed from less conductive components, thereby improving fiber sorbents utilization efficiency. The sorption kinetics in CA-S-PEI hollow fiber sorbents are investigated in terms of flow rates, module packing fraction, module length, and silica particle size. A mathematical model developed previously is successfully utilized to predict various contributions to the overall mass transfer resistance. In fiber sorbents where the amine loading is high, such as those employed here, the sorption process is found to be controlled by intraparticle mass transfer resistances. Unlike fiber sorbents based on physisorbents, the external gas diffusion resistance has minimal effects on the breakthrough capacities, as evidenced with the negligible effects of the module packing fraction on the sorption capacities. Sorption capacities are found to increase with the fiber module length as a result of self-sharpening effects. The increase of particle size increases the mass transfer resistance of the fiber sorbents as illustrated by the more diffuse CO 2 breakthrough fronts in fiber modules containing bigger silica particles. The capacities in fiber sorbents with the largest silica particles exhibit the lowest sorption capacity, as expected.

This work presents the uncertainty quantification, which includes parametric inference along with uncertainty propagation, for CO2 adsorption in a hollow fiber sorbent, a complex dynamic chemical process. Parametric inference via Bayesian approach is performed using Sequential Monte Carlo, a completely parallel algorithm, and the predictions are obtained by propagating the posterior distribution through the model. The presence of residual variability in the observed data and model inadequacy often present a significant challenge in performing the parametric inference. In this work, residual variability in the observed data is handled by three different approaches: (a) by performing inference with isolated data sets, (b) by increasing the uncertainty in model parameters, and finally, (c) by using a model discrepancy term to account for the uncertainty. The pros and cons of each of the three approaches are illustrated along with the predicted distributions of CO2 breakthrough capacity for a scaled‐up process. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3352–3368, 2016