New BIGCCS PhD – Nina Enaasen Flø

Nina Enaasen Flø defended her doctorate thesis titled “Post-combustion absorption-based CO2 capture: modeling, validation and analysis of process dynamics”, on September 4, 2015. BIGCCS congratulates!

PhD Nina Enaasen Flø

PhD Nina Enaasen Flø

Nina has developed a dynamic process model of the complete post-combustion CO2 capture process based on chemical absorption. She has also conducted a pilot plant campaign with specific focus on process dynamics for generation of experimental data for model validation. Data from three other scale pilot plants are also utilized for model validation. Further, Nina has utilized the developed and validated process model to evaluate various model approaches and model complexities and to investigate relevant process dynamics. Typical time constants for responses at different locations of the process are studied in order to determine the main inertia of the process. Various modes of flexible operation are also evaluated based on hypothetical scenarios of varying electricity demand and prices.

Department and Supervisors:

The doctoral work has been carried out at the Department of Chemical Engineering, NTNU, Trondheim. Professor Magne Hillestad has been the candidate’s supervisor. Dr Hanne Kvamsdal, SINTEF Materials and Chemistry, has been the co-supervisor.

The Assessment Committee consisted of:

  • Professor Meihong Wang, School of Engineering, University of Hull, United Kingdom of Great Britain and Northern Ireland
  • Dr Peter Singstad, managing Director at Cybernetica AS, Trondheim
  • Professor Øyvind Weiby Gregersen, Department of Chemical Engineering, NTNU

From left: Hanne Kvamsdal, Øyvind Weiby Gregersen, Nina Enaasen Flø, Magne Hillestad, Meihong Wang, and Peter Singstad

Abstract of the thesis:

Meeting the future energy demand, while at the same time reducing carbon emissions to a sustainable level, is one of the main global challenges in the years to come. Several technologies are suggested to reduce our carbon footprint; however, CCS is the only option where significant reductions can be achieved with the continued use of coal and other fossil fuels. Post-combustion CO2 capture by chemical absorption is the most mature CCS technology and it has been demonstrated in laboratory and pilot plant scale for many years. Extensive modeling and simulation studies have also been performed in order to optimize the process design, improve the solvent systems and minimize the energy penalty related to solvent regeneration. Most of these studies are performed using steady state models that assume integrated power plants operating at a base load. However, operational issues and the performance of the capture process in a flexible perspective can only be evaluated using dynamic process models.

In the present work, a rigorous dynamic process model of post-combustion amine-based CO2 absorption is developed in MATLAB. The process model contains dynamic unit models developed from first principle conservation laws, basic PI-controllers and sub-models for physical, chemical and thermodynamic properties for the H2O-CO2-MEA system. A similar process model is also developed in the K-Spice general simulation tool using the embedded library of process units. Thermodynamic data are generated in Multiflash and imported as data tables.

Various approaches for modeling the effects of mass transfer and chemical reaction rates are assessed and it is concluded that the rate-based approach is preferred for accurate steady-state predictions of the absorber temperature and concentration profiles. A simplified equilibrium-stage model gives similar transient results and is considered adequate for prediction of transient responses of the process; however, the absorber profiles are less accurate. Two different correlation sets for mass transfer and column hydraulics are also implemented. The Billet correlations underpredict the effective interface area and must be adjusted by a factor of 2.25 – 2.5 in order to meet the desired absorption rates. The correlations by Rocha give adequate predictions without any adjustments.

The availability of specific dynamic test data in the literature has been scarce, thus proper model validation has remain problematic. A pilot plant campaign with specific focus on process dynamics was therefore planned and executed in the Gløshaugen pilot plant as part of this project. A considerable amount of both steady state and dynamic data sets are obtained, and a very good steady state CO2 mass balance supports their reliability. CO2 loading is successfully correlated with online density measurements to provide a detailed time description of the solvent dynamics.

The developed process models are validated against pilot plant data from the Gløshaugen pilot plant and three larger CO2 capture pilot plants. This gives adequate confidence in the process models, and it is concluded that the simulated transient responses in absorption/desorption rates are in general good agreement with the experimental results. Some stationary deviations are observed, which are believed to be related to inaccuracies in the empirical models used for prediction of mass transfer and effective interface area. However, given the experimental measurement uncertainties, it is concluded that both process models provide satisfactory predictions.

The validated process models are utilized for investigations of process dynamics. The MATLAB model of the Tiller pilot plant is used to investigate time constants at different locations in the process in order to determine the dominant dynamics. It is concluded that considerable stabilization times are required for operational changes that affect the solvent CO2 loading due to the effect of recirculating the solvent on a closed loop. The overall stabilization time of the process is therefore up to 6 hours, even though the total solvent residence time is only 2 – 3 hours. The transient effects of chemical reaction and heat and mass transfer rates in the absorber column are believed to be very limited.

The K-Spice model of the Brindisi pilot plant is utilized to simulate four different modes of flexible operation based on hypothetical scenarios with varying electricity demand and prices. The CO2 capture process reacts quickly to power plant load changes, and it is able to stabilize at both part and full load operation. The operating mode of solvent storage gives satisfactory results when it comes to instantaneous and time average capture rate and energy performance in a varying electricity market. However, considerable investments are required for solvent storage tanks and additional operating solvent. The modes of exhaust gas venting and varying solvent regeneration seem to work satisfactory and are easy to conduct without major process modifications. However, these modes rely on the possibility of relaxing the constraint of 90% capture rate for short periods and later increasing it above 90% in order to obtain a 90% time average capture rate.