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2.8 What Does the Future Hold?
As presented previously, CFD modeling has proved a beneficial tool for the modeling of CCSU technologies, thus helping to get insight into problems where experimental measurements cannot be obtained or analytical solutions are impossible. Process simulations on the other hand are used in order to provide information for the design and operation of entire plants. Both CFD and process simulations have their advantages and disadvantages when applied separately, but the combination of both techniques to run in parallel and live feedback each other would offer new opportunities to analyze and optimize the overall plant performance.
Figure 2.6 Pressure maps within PBR reactors obtained using CFD simulations to shed light on the pressure distribution of three geometric configurations: (a) fractal‐inspired shape, (b) multi‐tubular, and (c) serpentine.
Source: Adapted from Tao et al. (2019).
There have been several efforts in different fields of engineering to integrate CFD and process simulations. Proof of this is the recent development a co‐simulation software framework at the DOE National Energy Technology Laboratory in the United States, which was applied to fossil energy systems with carbon capture. This tool is called APECS (advanced process engineering co‐simulator) and allows the design and optimization of the overall plant performance based on detailed high‐fidelity fluid dynamic models (CFD). Other instances of co‐simulation strategies applied to CCSU technologies are the work of Zitney [67], where an integrated gasification combined cycle (IGCC) power and hydrogen coproduction plant with carbon capture was analyzed by feeding data from CFD models into Aspen Plus. The results of the integration showed that the overall plant performance is affected by complex thermal and fluid flow phenomena that can only be analyzed at the CFD level; otherwise, process simulations miss those details. Another example of the intertwining between CFD and process simulations can be found in the work of Fei et al. [55], where the link between CFD and process simulations was accomplished by developing reduced order models (ROM) with the CFD data and introducing them into the process model. Edge et al. [54] on the other hand obtained temperature contours, velocities, and mole fraction maps of different species involved by using CFD and introduced the data into the process simulation tool gPROMS to assess the retrofitting of a coal‐fired power plant into an oxy‐fuel plant. Their approach resulted in guidelines as for the conditions where the system results in the same efficiency as air‐firing. Also, similar to Fei et al. [55], Lang et al. [68] presented a co‐simulation approach for an IGCC by developing an ROM via CFD in order to reduce the computational time and then optimize the plant by using process simulations. The efficiency of the process was increased by 7%, compared to the conventional configuration.
The above examples show the benefits of the co‐simulation approach, in allowing the detailed interactions between fluid mechanics, heat transfer, reaction, and control strategy to be examined, and give valuable outputs to the design and operational model. The aforementioned examples also show that there is a lot to be done, given the scarcity of CFD process co‐simulation studies published in the literature. For instance, and to the best of the author's knowledge, no co‐simulation study has been reported regarding carbon utilization. As previously mentioned however, the combination of CFD and process simulations will certainly lead to significant research outcomes, especially in cases with CO2 utilization where new catalysts (CFD) need to be tested in a reactor (part of a bigger process simulation) in which steady‐state performance, dynamics, and control strategy depend on mixing and fluid flow behavior. More specifically, in the area of methanation, there are two different aspects that need to be combined: the methanation reactor configuration and the catalysts. Not only is the reactor design clearly influenced by the catalyst applied, its activity, and selectivity, but also are up‐ and downstream processes [69]. A tight interfacing between CFD calculations for the performance assessment of a given catalyst and process simulation tools for the reactor design will open the possibility for process modeling on a detailed and optimized approach.
It is evident from the aforementioned examples that the combination of process simulations and CFD will lead to a future with improved and optimized CCSU technologies. Also, the combination and implementation of different control strategies shall also provide an extra benefit.
References
1 1 Shaikh, A.R., Karchanechi, H., Kamio, E. et al. (2016). Quantum mechanical and molecular dynamics simulations of dual‐amino‐acid ionic liquids for CO2 capture. J. Phys. Chem. C 120 (49): 27734–27745.
2 2 Cygan, R.T., Romanov, V.N., and Myshakin, E.M. (2012). Molecular simulation of carbon dioxide capture by montmorillonite using an accurate and flexible force field. J. Phys. Chem. C 116 (24): 13079–13091.
3 3 Tao, M., Xu, N., Gao, J. et al. (2019). Phase‐change mechanism for capturing CO2 into an environmentally benign nonaqueous solution: a combined NMR and molecular dynamics simulation study. Energy Fuels 33 (1): 474–483.
4 4 Trinh, T.T., Tran, K.Q., Bach, Q.V., and Trinh, D.Q. (2016). A molecular dynamics simulation study on separation selectivity of CO2/CH4 mixture in mesoporous carbons. Energy Procedia 86: 144–149.
5 5 Wilcox, J. (2012). Carbon Capture. New York, NY: Springer Science + Business Media.
6 6 Raynal, L. and Royon‐Lebeaud, A. (2007). A multi‐scale approach for CFD calculations of gas‐liquid flow within large size column equipped with structured packing. Chem. Eng. Sci. 62 (24): 7196–7204.
7 7 Raynal, L., Ben, R.F., and Royon‐Lebeaud, A. (2009). Use of CFD for CO2 absorbers optimum design: from local scale to large industrial scale. Energy Procedia 1: 917–924.
8 8 Cooke, J.J., Armstrong, L.M., Luo, K.H., and Gu, S. (2014). Adaptive mesh refinement of gas‐liquid flow on an inclined plane. Comput. Chem. Eng. 60: 297–306.
9 9 Sebastia‐Saez, D., Gu, S., and Ramaioli, M. (2018). Effect of the contact angle on the morphology, residence time distribution and mass transfer into liquid rivulets: a CFD study. Chem. Eng. Sci. 176: 356–366.
10 10 Larachi, F., Petre, C.F., Iliuta, I., and Grandjean, B. (2003). Tailoring the pressure drop of structured packings through CFD simulations. Chem. Eng. Process. 42 (7): 535–541.
11 11 Sun, B., He, L., Liu, B.T. et al. (2013). A new multi‐scale model based on CFD and macroscopic calculation for corrugated structured packing column. AIChE J. 59 (8): 3119–3130.
12 12 Haroun, Y., Legendre, D., and Raynal, L. (2010). Direct numerical simulation of reactive absorption in gas‐liquid flow on structured packing using interface capturing method. Chem. Eng. Sci. 65 (1): 351–356.
13 13 Sebastia‐Saez, D., Gu, S., Ranganathan, P., and Papadikis, K. (2015). Micro‐scale CFD modeling of reactive mass transfer in falling liquid films within structured packing materials. Int. J. Greenhouse Gas Control 33: 40–50.
14 14 Said, W., Nemer, M., and Clodic, D. (2011). Modeling of dry pressure drop for fully developed gas flow in structured packing using CFD simulations. Chem. Eng. Sci. 66 (10): 2107–2117.
15 15 Pham, D.A., Lim, Y.I., Jee, H. et al. (2015). Porous media Eulerian computational fluid dynamics (CFD) model of amine absorber with structured‐packing for CO2 removal. Chem. Eng. Sci. 132: 259–270.
16 16 van Baten, J.M., Ellenberger, J., and Krishna, R. (2001). Radial and axial dispersion of the liquid phase within a KATAPAK‐S® structure: experiments vs. CFD simulations. Chem. Eng. Sci. 56 (3): 813–821.
17 17 Petre, C.F., Larachi, F., Iliuta, I., and Grandjean, B.P.A. (2003). Pressure drop through structured packings: breakdown into the contributing mechanisms by CFD modeling. Chem. Eng. Sci. 58: 163–177.
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