Results from 30 wt% MEA Performance Testing at the CO2 Technology Centre Mongstad

Gjernes, Erik; Pedersen, Steinar; Cents, Toine; Watson, Guillaume; Fostås, Berit F.; Shah, Muhammad Ismail; Lombardo, Gerard; Desvignes, Coralie; Flø, Nina Enaasen; Morken, Anne Kolstad; Cazenove, Thomas de; Faramarzi, Leila; Hamborg, Espen S.

doi:10.1016/j.egypro.2017.03.1276

Cited by 34 publications

(32 citation statements)

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“…The data for the 4 scenarios are listed in Table 1, Table 2, Table 3 and Table 4 (from Saetre, 2016). The data are from scenarios documented in and Gjernes (2017), but some of the data are converted to different units to make them suitable for input to simulation programs.…”

Section: Performance Data From Tcmmentioning

confidence: 99%

“…At CO2 Technology Centre Mongstad (TCM) there is an absorption column with a rectangular cross section of 3.55 times 2 meter which is equivalent to a packing diameter of 3 meter, and a packing height up to 24 meter. At TCM, performance tests of CO2 absorption from flue gas into 30 wt-% monoethanol amine (MEA) have been run in 2013 and in 2015 (Gjernes et al, 2017). Figure 1 shows a simplified process diagram of the amine based CO2 absorption and desorption facility at TCM.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of simulation tools to fit and predict performance data of CO2 absorption into monoethanol amine at CO2 Technology Centre Mongstad (TCM)

Øi

Sætre

Hamborg

2018

Linköping Electronic Conference Proceedings

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In this work, several sets of experimental data from the amine based CO2 capture process at CO2 Technology Centre Mongstad (TCM) have been compared with simulations of different equilibrium based models and a rate-based model. The equilibrium models (in Aspen Plus and Aspen HYSYS) were fitted by adjusting the Murphree efficiency for each stage and the rate-based model (in Aspen Plus) was fitted by adjusting the interfacial area factor. Aspen Plus (using the Electrolyte-NRTL model) and Aspen HYSYS (using Kent-Eisenberg and Li-Mather models) gave almost identical results for the capture rate and small deviations for the temperature profiles. There are however deviations both between the measured temperatures at a specified column height and between measured temperatures and the simulated temperatures. Equilibrium based models are less fundamental than rate-based models, but for the conditions in this study, the rate-based models still lack accurate input parameters like the interfacial area. The results from this study show that equilibrium and rate-based models perform equally well in both fitting performance data and in predicting performance at changed conditions.

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Section: Performance Data From Tcmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of simulation tools to fit and predict performance data of CO2 absorption into monoethanol amine at CO2 Technology Centre Mongstad (TCM)

Øi

Sætre

Hamborg

2018

Linköping Electronic Conference Proceedings

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“…The capture rate is kept constant at around 85% by varying the reboiler duty. The first series of tests during the MEA campaign were dedicated to verification of mass balances of the plant [50]. CO 2 mass balance gives results close to 100%, and Gjernes et al [50] conclude that CO 2 mass balance based on gas phase can be maintained at a level better than 100 ± 5%.…”

Section: Steady-state Operating Casesmentioning

confidence: 99%

“…The first series of tests during the MEA campaign were dedicated to verification of mass balances of the plant [50]. CO 2 mass balance gives results close to 100%, and Gjernes et al [50] conclude that CO 2 mass balance based on gas phase can be maintained at a level better than 100 ± 5%. In this work, the suggested method in [50] was used during data selection in order to ensure that the steady-state data cases presented in Table 3 have acceptable CO 2 mass balance.…”

Section: Steady-state Operating Casesmentioning

confidence: 99%

Dynamic Process Model Validation and Control of the Amine Plant at CO2 Technology Centre Mongstad

2017

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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.

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“…The absorber column at TCM is rectangular, and the diameter is presented as an equivalent diameter given the cross-sectional area of the column. Validation data for the MEA system were obtained during a 2015 campaign at TCM, and these data have previously been reported in Gjernes et al (2016). In this work, the absorber and stripper columns are simulated separately, similarly to the methodology used for the validation with data from NCCC.…”

Section: Tcm Test Campaignmentioning

confidence: 99%

Physical Property Modeling of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

Morgan¹

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in Prof. Bhattacharyya's Advanced Process and Energy Systems Engineering Group, both past and current. I have enjoyed the opportunity to get to know many of you, and to have the opportunity to discuss research work and share ideas with many of you. I maintain fond memories of all of the adventures we have had outside of the work setting, particularly on travels for AIChE conferences. I would especially like to acknowledge the honor of working directly with Anderson Soares Chinen on the development of the MEA solvent model and various other projects; without his contributions, most of the work contained herein would not be possible. Similarly, I would like to thank Ben Omell for his very valuable guidance throughout the development of these process models, and for providing the results for the validation of our model with TCM data. There are many collaborators that I have been privileged to work with, and who have contributed much to the work described in this document. I would like to thank the National Carbon Capture Center (NCCC) and all of the involved staff for providing data for the 2014 and 2017 test campaigns for the MEA solvent system. I would like to thank Charles Tong from Lawrence Livermore National Laboratory for providing much assistance and advice for the uncertainty quantification phase of this work, particularly with respect to use of the PSUADE software, and for his patience while I learned to use these tools. I have also enjoyed the opportunity to collaborate with Sham Bhat and Christine Anderson-Cook from Los Alamos National vi Laboratory on the design of experiments for the 2017 NCCC test campaign, and acknowledge their contributions to the success of this project. I would like to thank Bill Buschle from the University of Edinburgh for his work on the measurement uncertainty in the solvent at NCCC, which has yielded results that have greatly improved the comparison between our MEA model and the pilot plant data. I would like to thank Prof. Gary Rochelle from the University of Texas at Austin for sharing the "Phoenix Model", which was used as a precursor to the model developed in this work, and members of his research team, specifically Brent Sherman, for being very helpful as I was starting out in the solvent modeling work. I would like to thank all involved in the high viscosity solvent model project for the opportunity to be involved with this collaborative effort. I am grateful for the CCSI and CCSI 2 research programs, and for the leadership of David Miller and Michael Matuszewski, that have made these collaborations possible. While pursuing a Ph.D. at West Virginia University, I have been privileged to be able to supplement my research with excellent coursework from within the Department of Chemical and Biomedical Engineering (CBE) and elsewhere. From CBE, I would like to acknowledge the following professors for offering excellent courses that have contributed to my research and professional development in some way: Prof. Charter Stinespring (thermodynamics and statistical and molecular t...

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Results from 30 wt% MEA Performance Testing at the CO2 Technology Centre Mongstad

Cited by 34 publications

References 7 publications

Comparison of simulation tools to fit and predict performance data of CO2 absorption into monoethanol amine at CO2 Technology Centre Mongstad (TCM)

Comparison of simulation tools to fit and predict performance data of CO2 absorption into monoethanol amine at CO2 Technology Centre Mongstad (TCM)

Dynamic Process Model Validation and Control of the Amine Plant at CO2 Technology Centre Mongstad

Physical Property Modeling of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

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