Economic Optimization of the Advanced Flash Stripper Using the Framework for Optimization, Quantification of Uncertainty and Surrogates

Source: AIChE
  • Type:
    Conference Presentation
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    November 17, 2020
  • Duration:
    17 minutes
  • Skill Level:
    Intermediate
  • PDHs:
    0.30

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Conventional solvent-based carbon capture systems are considered highly energy-intensive in large part due to the evaporation of water when CO2 is desorbed in regular strippers. While solvent discovery and design can serve as a promising strategy for decreasing stripper heat requirements, process level improvements are another complementary option. One process modification with potential for improvements is the Advanced Flash Stripper (AFS)1 configuration. Due to the improved stripper heat recovery (by means of two bypass solvent streams and two extra economizers) in the AFS configuration, process simulation and sensitivity studies demonstrate that the regeneration cost can be reduced by around 10-15% compared to a more conventional Simple Stripper (SS) configuration1-2. In search of ideal equipment sizes and operating conditions, rigorous optimization for the AFS is needed to evaluate its full techno-economic potential.

In this work, the Framework for Optimization, Quantification of Uncertainty and Surrogates (FOQUS)3, a part of the Carbon Capture Simulation Initiative (CCSI) Toolset4, is applied to build a general optimization framework for integrating the process model with the cost spreadsheet and optimizing the techno-economic performances of different types of CO2 absorbents (MEA, Piperazine) in the AFS configuration and Simple Stripper (SS) configuration. This AFS optimization framework contains a rigorous rate-based Aspen process model and an Excel cost spreadsheet (consistent with the baseline report5) for translating the Aspen model outputs (column sizes, energy consumption etc.) to Cost of Electricity (COE). As FOQUS relies on derivative free solvers for optimization, the convergence rate of the AFS flowsheet needs to be robust. The convergence issues of AFS process simulation resulted from its multi-recycle streams has been mitigated by using the customized tearing orders, allowing for the optimization of seven decision variables simultaneously (absorber and stripper heights, stripper pressure, lean loading, cold and warm bypass ratio, minimum temperature approach). The optimization results suggest that the AFS configuration can reduce the stripper heat by 14.7% and 9.3% for 5m PZ and 7m MEA, respectively, compared to their optimized SS counterparts. However, the COE’s are only reduced by 0.8% and 0.7% due to the higher capital costs resulting from the additional process complexity of the AFS configuration.

Reference

  1. Lin, Y.J. and Rochelle, G.T., 2014. Optimization of advanced flash stripper for CO2 capture using piperazine. Energy Procedia, 63, pp.1504-1513.
  2. Rezazadeh, F., Gale, W.F., Lin, Y.J. and Rochelle, G.T., 2016. Energy performance of advanced reboiled and flash stripper configurations for CO2 capture using monoethanolamine. Industrial & Engineering Chemistry Research, 55(16), pp.4622-4631.
  3. Miller, D.C., 2015. FOQUS: A framework for organizational and quantification of uncertainty (No. NETL-PUB-1212). National Energy Technology Lab.(NETL), Pittsburgh, PA, and Morgantown, WV (United States). In-house Research; National Energy Technology Lab.(NETL), Pittsburgh, PA, and Morgantown, WV (United States).
  4. Miller, D.C., Syamlal, M., Mebane, D.S., Storlie, C., Bhattacharyya, D., Sahinidis, N.V., Agarwal, D., Tong, C., Zitney, S.E., Sarkar, A. and Sun, X., 2014. Carbon capture simulation initiative: a case study in multiscale modeling and new challenges. Annual review of chemical and biomolecular engineering, 5, pp.301-323.
  5. Haslbeck, J.L., Kuehn, N.J., Lewis, E.G., Pinkerton, L.L., Simpson, J., Turner, M.J., Varghese, E. and Woods, M.C., 2010. Cost and performance baseline for fossil energy plants volume 1: Bituminous coal and natural gas to electricity. DOE/NETL
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