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Equation-Based Design, Integration, and Optimization of Oxycombustion Power Systems

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Abstract

The application of “systems-based tools’’ including exergy/pinch analysis and process simulation has facilitated increases in the thermal efficiency of ambient pressure oxycombustion coal-fired power systems with carbon capture from 36 to 39–40 %LHV, while also considering capital costs. This corresponds to a decrease in the energy penalty 10 %-points to 6–7 %-points (absolute), relative to reference air-fired coal power plants without CO2 capture (46 %LHV). These efficiency improvements are primarily due to tailored next-generation air separation systems and plant-wide heat integration. Furthermore, oxycombustion power systems are an ideal candidate for numerical optimization, given the complex interactions between its five subsystems. This chapter extensively surveys the oxycombustion literature and summarizes four key design questions. A new, fully equation-based, flowsheet optimization framework is then introduced and applied to three oxycombustion-related case studies: design of a minimum energy air separation unit to produce an O2 enriched stream for the boiler, optimization of the CO2 polishing unit and compression train to minimize specific energy, and maximization of thermal efficiency in the oxy-fired steam cycle using a hybrid 1D/3D boiler model.

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Notes

  1. 1.

    Utilization applications for CO2 from oxycombustion requires an additional CO2/Ar, N2, O2 separation step.

  2. 2.

    HHV and LHV abbreviate higher heating value and lower heating value, respectively.

  3. 3.

    Another source of skepticism regarding pressurized oxycombustion comes from the small predicted energy penalties. Analysis of the theoretical minimum separation energy for pressurized oxycombustion would help address these concerns.

  4. 4.

    This limitation is primarily due to the nature of sequential-modular process simulators, such as Aspen Plus. Derivatives for optimization must be calculated with perturbations, which are noisy due to the iteration tolerances for convergence of individual unit models and flash calculations. See Biegler et al. (1997) for additional discussion. For equation-oriented process simulators, it is unclear if exact first derivatives are available with thermodynamic function evaluations and how non-ideal phase equilibrium calculations are formulated to ensure continuous derivatives across phase changes.

  5. 5.

    When three real roots exist, the smallest corresponds to the liquid phase; the largest corresponds to the vapor phase and the middle root is considered spurious.

  6. 6.

    Because \(f(Z)\) is a cubic polynomial with respect to \(Z\), calculating \(f^{{\prime}} (Z)\) and \(f^{{\prime \prime }} (Z)\) is straightforward.

  7. 7.

    In essence, \(0 \le x \bot y \ge 0\) (\(x\) complements \(y\)) means either \(x = 0\) and/or \(y = 0\) (and \(x \ge 0\), \(y \ge 0\)).

  8. 8.

    The cubic equation of state model is also used to calculate the phase and enthalpy of the feed stream(s), but is omitted from (1) for brevity.

  9. 9.

    Most nonlinear optimization algorithms rely on continuity assumptions for first (and possibly second) derivatives. When these assumptions are violated, solver performance typically deteriorates. Development of large-scale nonsmooth nonlinear optimization algorithms remains an open research challenge.

  10. 10.

    In problems (2), (3) and (5), the complementarity constraints, such as (1j), are accommodated using the penalty formulation. See Biegler (2010) for an overview of theoretical aspects of optimization problems with complementarity constraints, along with applications in chemical engineering and solution techniques.

  11. 11.

    Problem (3) is infeasbile for the following CO2 recovery-purity combinations: (94.6 % recovery, 97.0 mol% purity), (94.6 % recovery, 97.5 mol% purity), (92.0 % recovery, 97.5 mol% purity), and (90.0 mol% recovery, 98.0 mol% purity).

  12. 12.

    Thermal efficiencies are calculated on a higher heating value basis. These calculation does not consider all of the auxillary loads, such as the coal mill.

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Acknowledgements

This work was supported by the U.S. Department of Energy, Office of Fossil Energy as part of the Carbon Capture Simulation Initiative (CCSI). This technical effort was performed in support of the National Energy Technology Laboratory’s ongoing research under the RES contract DE-FE0004000. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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  1. Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA

    Alexander W. Dowling, John P. Eason & Lorenz T. Biegler

  2. AECOM & National Energy Technology Laboratory, 3610 Collins Ferry Road, P.O. Box 880, Morgantown, WV, 26507, USA

    Jinliang Ma

  3. National Energy Technology Laboratory, 626 Cochrans Mill Road, P.O. Box 10940, Pittsburgh, PA, 15236, USA

    David C. Miller

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    Mariano Martín

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Dowling, A.W., Eason, J.P., Ma, J., Miller, D.C., Biegler, L.T. (2016). Equation-Based Design, Integration, and Optimization of Oxycombustion Power Systems. In: Martín, M. (eds) Alternative Energy Sources and Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-28752-2_5

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