Optimization of CO₂ Capture Process with Aqueous Amines—A Comparison of Two Simulation–Optimization Approaches

Abstract

Aqueous amine is a solvent considered for carbon dioxide (CO2) recovery from the flue gas of a refinery gas turbine by chemical absorption/desorption process. The performance and the economics of this process depend on the choice of the amine absorbent, the concentration of the amine absorbent, the number of stages in the absorber and stripper columns, and the operating conditions. We used response surface methodology (RSM)a simulation–optimization technique, which uses local searches to estimate an appropriate direction to reduce the objective functionto optimize the amine-based CO2 capture process in a previous work [Nuchitprasittichai and Cremaschi Comput. Chem. Eng. 2011, 35, 1521–1531]. However, RSM does not provide any information about the quality of the obtained solution. In this paper, the RSM results are compared to those obtained by optimizing a global surrogate model of the system over the whole decision space with a global solver. We used an artificial neural network (ANN) as the global surrogate model. Depending on the accuracy of the global surrogate models, the solutions obtained using them can be shown to be global within the bounds of the data used to generate them. The comparison is used to assess the quality of the RSM results and their relative computational costs. Monoethanolamine (MEA), diglycolamine (DGA), diethanolamine (DEA), methyl diethanolamine (MDEA), triethanolamine (TEA), and blended aqueous solutions of these amines are considered in our analyses. The results reveal that the RSM algorithm yielded optimum solutions close to those obtained by the ANN approach for all solvents.

A perspective on process synthesis: Challenges and prospects

“…The author's group also worked in process synthesis and design using surrogate models. We analyzed the impact of different amine absorbents and their concentrations, the absorber and stripper column heights and the operating conditions on the cost of CO 2 recovery plant for post-combustion CO 2 removal combining second-order response surface surrogate models and steepest descent routine iteratively (Nuchitprasittichai and Cremaschi, 2011), and using artificial neural networks -ANNs (Nuchitprasittichai and Cremaschi, 2013), synthesized a biodiesel production process using ANNs (Fahmi and Cremaschi, 2012), and designed a packed-bed column photobioreactor for algae growth (Smith et al, 2012).…”

Optimization-based design of crude oil distillation units using surrogate column models and a support vector machine

“…Various types of artificial neural network architectures (Basheer and Hajmeer, 2000) have been applied to model process systems of different levels of complexity (Henao and Maravelias, 2010;Gueddar and Dua, 2011;Nuchitprasittichai and Cremaschi, 2013;Ochoa-Estopier and Jobson, 2015). A multi-layer feedforward network is the most widely accepted architecture due to its mathematical simplicity.…”

Thermal integration of different compression-train configurations for coal-fired power plant with carbon capture

“…Within these methods, the most advanced technology used for CO 2 sequestration is a process involving post-combustion capture using chemical absorption. This technology has been investigated with different absorbents, including ammonia (Linnenberg et al 2012 ), glycerol as a green absorbent (Flowers et al 2017 ), aqueous potassium carbonate (Ghosh et al 2009 ), and alcohol amines such as monoethanolamine (MEA), diethanolamine, and triethanolamine (Suleiman et al 2016 ; Boucif et al 2008 ; Nuchitprasittichai and Cremaschi 2013 ; Cormos et al 2018 ; Wang et al 2011a , 2011b ). Many investigations have been conducted on these types of amines in water solutions and using mixtures of two or more amines.…”