(634d) Modeling, Simulation and Optimization of Pressure Swing Adsorption (PSA) Processes for Post-Combustion Carbon Dioxide (CO2) Capture from Flue Gas

Anthropogenic carbon dioxide (CO₂) emissions are considered as a great threat to the environment because of their contribution to the greenhouse effect and global warming. CO₂ is considered to be responsible for 60% of the global warming caused by greenhouse gases (GHGs) [1]. However, post-combustion CO₂ capture is still required to avoid excess emissions of CO₂ from existing power plants. The process of CO₂ capture and sequestration (CCS), involves CO₂ separation followed by pressurization, transportation, and sequestration. The capture units are expected to concentrate the CO₂ from flue gas with CO₂ purity and CO₂ recovery exceeding 95% and 90%, respectively according to the Department of Energy requirements [2]. Thus it is important to develop energy-efficient industrial technologies for CO₂ capture. A recent study presents a general and critical review of the state of the art emerging CO₂ capture technologies [3]. Pressure or vacuum swing adsorption (PSA/VSA) is a promising technology for carbon capture due to its relatively better separation performance, lower energy consumption and lower cost [4]. In a typical adsorption process, CO₂ is selectively adsorbed onto a solid sorbent while the clean flue gas passes through. The adsorbed CO₂ is released or desorbed by lowering the pressure of the process. Several PSA/VSA cycles specific to CO₂ capture have also been studied in the literature which indicate that cyclic adsorption processes are promising options for CO₂ capture [5]. Adsorption-based CO₂ capture using zeolites and metal-organic frameworks (MOFs) with large internal surfaces are promising technologies to separate CO₂ from power plant flue gases [6, 7].

This work presents a mathematical modeling framework developed from our research group [8,9] for the simulation and optimization of PSA/VSA processes for post-combustion CO₂ capture from dry flue gas (15% CO₂, 85% N₂). The core of the modeling framework represents a detailed adsorption column model relying on a coupled set of mixed partial differential and algebraic equations (PDAEs) for mass, heat and momentum balance at both bulk gas and particle level, equilibrium isotherm equations, transport and thermo-physical properties of the gas mixture and boundary conditions according to the operating steps. The proposed modeling equations have been implemented in the gPROMS™ modeling environment. The modeling framework is first validated against data available from the literature [10], illustrating good agreement in terms of several process performance indicators [11]. Accordingly, the developed modeling framework is employed for a comparative evaluation of three available potential adsorbents for CO₂ capture, namely, zeolite 13X, activated carbon and Mg-MOF-74 [12]. A two-bed configuration (six-step VSA cycle) with light product pressurization has been employed in all simulations in accord with recent studies [13, 14]. The results from systematic comparative simulations demonstrate that zeolite 13X has the best process performance among the three adsorbents, in terms of CO₂ purity and CO₂ recovery. On the other hand, Mg-MOF-74 appears to be a promising adsorbent for CO₂ capture, as it has considerably higher CO₂ productivity compared to the other two adsorbents. Furthermore process optimization studies using zeolite 13X and Mg-MOF-74, have been performed to minimize energy consumption for specified minimum requirements in CO₂ purity and in CO₂ recovery at nearly atmospheric feed pressures. The optimization results indicate that there is a complex relationship between optimal process performance indicators and operating conditions that varies among the different adsorbents and cannot be quantified by simple comparison of CO₂/N₂ adsorption isotherms and selectivity data. Evidently, detailed process modeling, simulation and optimization strategies, provide the most reliable way to evaluate both qualitatively and quantitatively potential adsorbents for CO₂ capture. Finally, the reduction of CO₂ emissions from dry flue gas employing two successive PVSA units, using zeolite 13X and Mg-MOF-74 as adsorbents, is considered and the effects of operating parameters on the separation quality are investigated by simulation and optimization. With the proposed two-stage PVSA process, a CO₂ purity higher than 95% with a relatively high CO₂ recovery unit (greater than 90%) is obtained.

 

References

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