Simulation and Optimization of Pressure Swing Adsorption Systems Using Reduced-Order Modeling

Performance-Based Screening of Porous Materials for Carbon Capture

Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

Influence of the main operating parameters on the DRPSA process design based on the equilibrium theory

Among the adsorption-based separation processes for gaseous mixtures, those exploiting pressure variations, so-called Pressure Swing Adsorption (PSA) processes, are the most popular. In this work, we focus on the specific PSA configuration known as Dual Reflux-Pressure Swing Adsorption (DR-PSA) given its ability to achieve sharp separations. In the case of binary mixtures, an analytical approach based on Equilibrium Theory has been proposed to identify the operating conditions for complete separation under the assumption of linear isotherms. This same approach is not available when the separation is not complete. Accordingly, in this work we study the features of non-complete separations by solving numerically a general DR-PSA model with parameter values suitable to approach equilibrium conditions (no mass transport resistances, no axial mixing, isothermal conditions and no pressure drop), thus reproducing the analytical solution when complete separations are examined. Even for non-complete separations, triangularly shaped regions at constant purity can be identified on a plane whose axes correspond to suitable design parameters. Moreover, we found a general indication on how to select the lateral feed injection position to limit the loss in product purities when complete separation is not established, whatever is the composition of the feeding mixture. Finally, a sensitivity analysis with respect to pressure ratio, light reflux ratio and heavy product flowrate is proposed in order to assess how to recover product purities according to the specific degrees of freedom of a DR-PSA apparatus.

Design of a Pilot SOFC System for the Combined Production of Hydrogen and Electricity under Refueling Station Requirements

The objective of the current work is to support the design of a pilot hydrogen and electricity producing plant that uses natural gas (or biomethane) as raw material, as a transition option towards a 100% renewable transportation system. The plant, with a solid oxide fuel cell (SOFC) as principal technology, is intended to be the main unit of an electric vehicle station. The refueling station has to work at different operation periods characterized by the hydrogen demand and the electricity needed for supply and self-consumption. The same set of heat exchangers has to satisfy the heating and cooling needs of the different operation periods. In order to optimize the operating variables of the pilot plant and to provide the best heat exchanger network, the applied methodology follows a systematic procedure for multi-objective, i.e. maximum plant efficiency and minimum number of heat exchanger matches, and multi-period optimization. The solving strategy combines process flow modeling in steady state, superstructure-based mathematical programming and the use of an evolutionary-based algorithm for optimization. The results show that the plant can reach a daily weighted efficiency exceeding 60%, up to 80% when considering heat utilization.