(816b) Modeling, Analysis and Optimization of Process Technologies for Flue Gas Dehydration | AIChE

(816b) Modeling, Analysis and Optimization of Process Technologies for Flue Gas Dehydration


Gao, H. - Presenter, Northwestern University
You, F., Cornell University
Snurr, R., Northwestern University

Carbon Capture and Sequestration (CCS) of flue gas from large power plants have received increasing attention because of concerns about the role of CO2 in global climate change. Among different potential technologies for CCS, adsorption processes based on metal-organic frameworks (MOFs) have received significant attention because MOFs can be designed to achieve very high selectivity of CO2 over N2[1]. However, the stability and capacity of MOFs can be seriously affected by the presence of water in the flue gas[2]. Many researchers are working to develop new MOFs that can operate in the presence of water, but here we explore options for removing water from flue gas before a downstream adsorption process.  Such a dehydration step can contribute a significant part of the total cost for COcapture[3]. Furthermore, different MOFs may have different tolerance of moisture, so the dehydration requirement (i.e. the water vapor percentage in the dried flue gas), needs to be considered as a variable in order to be connected with possible material selection for the downstream process. To reduce the cost of an adsorption based carbon capture process, the dehydration unit needs careful design and optimization, but this issue is rarely addressed in the existing literature. In this study, we modeled and optimized different process technologies for the dehydration of flue gas. Our goal is to identify the best dehydration technology and minimize the cost of the dehydration process.

We investigated four alternatives for flue gas dehydration, including compression & cooling, cooling & condensation, TEG absorption and solid adsorption. They are compared in terms of cost and separation performance (i.e. loss of other components when satisfying the dehydration requirement). We varied the dehydration requirement from 0.1% to 1% of water in the dry gas for all four technologies. The first three processes are continuous processes and can be well simulated using Aspen Plus. We then apply a response surface method (RSM)[4, 5] to build a function linking the input-output data and minimize the process costs. Adsorption is a dynamic process operated in a cyclic manner[6]. Governing equations form a partial differential equation system, which is fully discretized both in temporal and spatial domain. We apply a first order finite volume method to discretize the spatial domain, and a Radau collocation scheme for temporal discretization. This transfers the original dynamic optimization problem to a nonlinear optimization problem, which can then be modeled and solved in GAMS using general-purpose MINLP solvers to obtain the optimal operating conditions and cost.

Results show the cost for water removal from flue gas can range from a few dollars to tens of dollars per ton CO2 captured, varying with the dehydration requirement. Among the four technologies, cooling & condensation proved to be the cheapest process for water removal, but it can only dehydrate to about 0.5% water content with no more than 10% loss of CO2. TEG absorption loses the least CObut the process cost is higher. The advantages and disadvantages of other technologies are also analyzed and discussed. The consideration of different dehydration requirements provides the foundation for linking with material selection in the downstream adsorption process.


[1]        M. Zhao, A. I. Minett, and A. T. Harris, "A review of techno-economic models for the retrofitting of conventional pulverised-coal power plants for post-combustion capture (PCC) of CO2," Energy & Environmental Science, vol. 6, pp. 25-40, Jan 2013.

[2]        A. C. Kizzie, A. G. Wong-Foy, and A. J. Matzger, "Effect of Humidity on the Performance of Microporous Coordination Polymers as Adsorbents for CO2 Capture," Langmuir, vol. 27, pp. 6368-6373, May 17 2011.

[3]        M. M. F. Hasan, R. C. Baliban, J. A. Elia, and C. A. Floudas, "Modeling, Simulation, and Optimization of Postcombustion CO2 Capture for Variable Feed Concentration and Flow Rate. 1. Chemical Absorption and Membrane Processes," Industrial & Engineering Chemistry Research, vol. 51, pp. 15642-15664, Dec 5 2012.

[4]        J. A. Caballero and I. E. Grossmann, "An algorithm for the use of surrogate models in modular flowsheet optimization," Aiche Journal, vol. 54, pp. 2633-2650, Oct 2008.

[5]        D. R. Jones, M. Schonlau, and W. J. Welch, "Efficient global optimization of expensive black-box functions," Journal of Global Optimization, vol. 13, pp. 455-492, Dec 1998.

[6]        R. T. Yang, Gas separation by adsorption processes. Boston: Butterworths, 1987.