Global warming and climate change have recently received widespread attention due to increasing CO2 emissions  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 , 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 .
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 . Finally, we compute the heat of adsorption for different temperatures and identify the activation energy of adsorption based on an Arrhenius temperature-dependence assumption.
 S. PACALA, R. SOCOLOW, Stabilization wedges: Solving the climate problem for the next 50 years with current technologies, Science, 305:968-972, 2004.
 K.Z. HOUSE, C.F. HARVEY, M.J. AZIZ, D.P. SCHRAG, The energy penalty of post-combustion CO2 capture & storage and its implications for retrofitting the U.S. installed base, Energy Environ. Sci., 2:193-205, 2009.
 P. BOLLINI, N.A. BRUNELLI, S.A. DIDAS, C.W. JONES, Dynamics of CO2 adsorption on Amine adsorbents. 1. Impact of heat effects, Ind. Eng. Chem. Res., 51:15145-15152, 2012.
 A. HEYDARI-GORJI, A. SAYARI, CO2 capture on Polyethylenimine-impregnated hydrophobic mesoporous Dilica: Experimental and kinetic modeling. Chem. Eng. J. 173:72–79, 2011.
 J. KALYANARAMAN, Y. FAN, R.P. LIVELY, W.J. KOROS, C.W. JONES, M.J. REALLF, Y. KAWAJIRI, Modeling and experimental validation of carbon dioxide sorption on hollow fibers loaded with Silica-supported Poly(ethylenimine), Chem. Eng. J., 259:737-751, 2015.
 V.S. VASSILIADIS, R.W.H. SARGENT, C.C. PANTELIADES, Solution of a class of multistage dynamic optimization problems. 1. Problems without path constraints & 2. Problems with path constraints, Ind. Eng. Chem. Res., 33:2111-2133, 1994.