Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

12 Pages Posted: 26 Mar 2021

See all articles by Joshua Morgan

Joshua Morgan

National Energy Technology Laboratory (NETL); NETL Support Contractor

Benjamin Omell

National Energy Technology Laboratory

Michael Matuszewski

Government of the United States of America - National Energy Technology Laboratory (NETL)

David Miller

affiliation not provided to SSRN

Muhammad Ismail Shah

Technology Centre Mongstad

Christophe Benquet

Technology Centre Mongstad

Anette Beate Nesse Knarvik

Technology Centre Mongstad; Equinor ASA

Thomas de Cazenove

Technology Centre Mongstad

Christine M. Anderson-Cook

affiliation not provided to SSRN

Towfiq Ahmed

affiliation not provided to SSRN

Charles Tong

affiliation not provided to SSRN

Brenda Ng

affiliation not provided to SSRN

Debangsu Bhattacharyya

West Virginia University

Date Written: March 24, 2021

Abstract

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design.

The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

Suggested Citation

Morgan, Joshua and Omell, Benjamin and Matuszewski, Michael and Miller, David and Ismail Shah, Muhammad and Benquet, Christophe and Knarvik, Anette Beate Nesse and de Cazenove, Thomas and Anderson-Cook, Christine M. and Ahmed, Towfiq and Tong, Charles and Ng, Brenda and Bhattacharyya, Debangsu, Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM) (March 24, 2021). Proceedings of the 15th Greenhouse Gas Control Technologies Conference 15-18 March 2021, Available at SSRN: https://ssrn.com/abstract=3811695 or http://dx.doi.org/10.2139/ssrn.3811695

Joshua Morgan (Contact Author)

National Energy Technology Laboratory (NETL) ( email )

626 Cochrans Mill Road
Pittsburgh, PA 15236
United States

NETL Support Contractor ( email )

626 Cochrans Mill Road
Pittsburgh, PA 15236
United States

Benjamin Omell

National Energy Technology Laboratory

3610 Collins Ferry Rd
Morgantown, WV 26507
United States

Michael Matuszewski

Government of the United States of America - National Energy Technology Laboratory (NETL)

Pittsburgh, PA
United States

David Miller

affiliation not provided to SSRN

Muhammad Ismail Shah

Technology Centre Mongstad

Mongstad, 5954
Norway

Christophe Benquet

Technology Centre Mongstad

Mongstad, 5954
Norway

Anette Beate Nesse Knarvik

Technology Centre Mongstad

Mongstad, 5954
Norway

Equinor ASA

Stavanger
Norway

Thomas De Cazenove

Technology Centre Mongstad

Mongstad, 5954
Norway

Christine M. Anderson-Cook

affiliation not provided to SSRN

Towfiq Ahmed

affiliation not provided to SSRN

Charles Tong

affiliation not provided to SSRN

Brenda Ng

affiliation not provided to SSRN

Debangsu Bhattacharyya

West Virginia University

PO Box 6025
Morgantown, WV 26506
United States

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