(662d) Spatiotemporal Modeling and Identification of CO2 Adsorption Columns

Global warming and climate change have recently received widespread attention due to increasing CO₂ emissions [1] that necessitate developing new processes to capture anthropogenic greenhouse gases from the flue gas stream of coal-fired power plants for electricity generation. While a wide range of CO₂ capture technologies have been proposed for post-combustion processes in chemical industries and power plants [2], adsorption on solid-based beds is recognized as one of the most efficient capture processes which is able to handle high throughputs. Although there is a large selection of industrial adsorbents, aminosilica adsorbent packed beds have recently received increasing attention in CO₂ adsorption due to their exceptional capacity and adsorbing CO₂ selectivity over all the other major components of the gas stream being processed [3].

The dynamic behavior of a given CO₂ adsorption column is practically described by a breakthrough curve which presents the temporal profile of the CO₂ concentration at the column outlet. This curve contains essential operational properties of the adsorption process can be obtained by direct experimentation or mathematical modeling. Compared to the experimental method which is typically time-consuming and expensive, mathematical modeling is economically efficient and has recently attracted increasing interest. To apply such a mathematical model as a basis for process design and control, it should be able to accurately predict the experimental behavior employing process parameters and conditions.

We focus on a rigorous model for isothermal CO₂ adsorption columns which describes the spatiotemporal dynamics of the CO₂ concentrations in the bulk and solid bed by a set of partial differential equations. Such mathematical model which considers both dispersion and convection phenomena provides the spatiotemporal behavior of the adsorption rate and circumvents the unphysical simplifying assumption of the linear driving force rate and unified rate through the column length which has been applied in the previous proposed models [3, 4, 5]. The proposed model is the base for a characterization approach seeking to compute physical quantities originating from mass balance laws such as the adsorption rate constant and capacity from a set of experimental data without using empirical parameters assumptions which have been invoked in previous research work [3, 4, 5].

The spatiotemporal dynamics of CO₂ adsorption in an aminosilica packed bed are successfully predicted by the proposed model. The dispersion coefficient, adsorption rate constant and capacity of the bed are then identified using a set of experimental CO₂ concentration measurements at the adsorption column outlet for different temperatures using a dynamic optimization formulation which is solved using shooting methods [6]. Finally, we compute the heat of adsorption for different temperatures and identify the activation energy of adsorption based on an Arrhenius temperature-dependence assumption.

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