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Dynamic Process Model Validation and Control of the Amine Plant at CO 2 Technology Centre Mongstad

Author

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  • Rubén M. Montañés

    (Department of Energy and Process Engineering, NTNU—Norwegian University of Science and Technology, NO-7491 Trondheim, Norway)

  • Nina E. Flø

    (CO 2 Technology Center Mongstad, NO-5954 Mongstad, Norway)

  • Lars O. Nord

    (Department of Energy and Process Engineering, NTNU—Norwegian University of Science and Technology, NO-7491 Trondheim, Norway)

Abstract

This paper presents a set of steady-state and transient data for dynamic process model validation of the chemical absorption process with monoethanolamine (MEA) for post-combustion CO 2 capture of exhaust gas from a natural gas-fired power plant. The data selection includes a wide range of steady-state operating conditions and transient tests. A dynamic process model developed in the open physical modeling language Modelica is validated. The model is utilized to evaluate the open-loop transient performance at different loads of the plant, showing that pilot plant main process variables respond more slowly at lower operating loads of the plant, to step changes in main process inputs and disturbances. The performance of four decentralized control structures is evaluated, for fast load change transient events. Manipulation of reboiler duty to control CO 2 capture ratio at the absorber’s inlet and rich solvent flow rate to control the stripper bottom solvent temperature showed the best performance.

Suggested Citation

  • Rubén M. Montañés & Nina E. Flø & Lars O. Nord, 2017. "Dynamic Process Model Validation and Control of the Amine Plant at CO 2 Technology Centre Mongstad," Energies, MDPI, vol. 10(10), pages 1-36, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1527-:d:113914
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    References listed on IDEAS

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    1. Dutta, Rohan & Nord, Lars O. & Bolland, Olav, 2017. "Selection and design of post-combustion CO2 capture process for 600 MW natural gas fueled thermal power plant based on operability," Energy, Elsevier, vol. 121(C), pages 643-656.
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    Cited by:

    1. Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(PA).
    2. Morgan, Joshua C. & Chinen, Anderson Soares & Anderson-Cook, Christine & Tong, Charles & Carroll, John & Saha, Chiranjib & Omell, Benjamin & Bhattacharyya, Debangsu & Matuszewski, Michael & Bhat, K. S, 2020. "Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process," Applied Energy, Elsevier, vol. 262(C).
    3. Wilkes, Mathew Dennis & Mukherjee, Sanjay & Brown, Solomon, 2021. "Transient CO2 capture for open-cycle gas turbines in future energy systems," Energy, Elsevier, vol. 216(C).
    4. Beiron, Johanna & Montañés, Rubén M. & Normann, Fredrik & Johnsson, Filip, 2020. "Flexible operation of a combined cycle cogeneration plant – A techno-economic assessment," Applied Energy, Elsevier, vol. 278(C).

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