The United States Department of Energy’s Carbon Capture Simulation for Industry Impact
The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) is a collaboration among national laboratories, industrial partners, and academic institutions focused on applications of a suite of computational tools and models developed as a part of its precursor program, the Carbon Capture Simulation Initiative (CCSI). The main goal of CCSI2 is to apply the CCSI Toolset for accelerating the development, deployment, and scale-up of novel CO2 capture technologies in projects with specific industrial partners.
The CCSI2 technical team is spread over multiple universities and national laboratories. The specific team members who were involved in the sDoE project and their respective affiliations are:
- National Energy Technology Laboratory: Joshua Morgan, Benjamin Omell, Michael Matuszewski, and David C. Miller
- Los Alamos National Laboratory: Christine Anderson-Cook, Towfiq Ahmed
- Lawrence Livermore National Laboratory: Charles Tong, Brenda Ng
- West Virginia University: Debangsu Bhattacharyya
Why participate in testing at TCM?
The CCSI2 team is developing a framework for performing test campaigns using a sequential design of experiments (SDoE) methodology that aims to maximize learning during pilot scale tests. An initial study with aqueous MEA was performed at the National Carbon Capture Center (NCCC), a 0.5 MWe test facility in Alabama, USA, and it was desired to extend the methodology to the larger scale facility at TCM and further validate the MEA process models developed by the CCSI2 team. The large scale of TCM, in addition to its robust operability over a wide range of process conditions and its accommodating staff, made it an excellent venue for this project.
Description of project at TCM
The objective of testing at TCM was to extend the CCSI2 methodology for SDoE for solvent-based CO2 capture. In this approach, an existing stochastic process model, in which a subset of its underlying parameters is characterized by probability distributions that represent the uncertainty in the parameter values; these parameter distributions are propagated through the process model, resulting in uncertainty distributions of key output variables of interest. The SDoE is general enough to allow for criteria specific to the goal of the experiment to be selected for optimization. In the TCM experiment the objective was to simultaneously select test conditions for which the estimated output uncertainty is large and to spread the conditions throughout the input space of interest. Data are incorporated into a Bayesian inference framework which updates the model parameter distributions as information is gained throughout the campaign. The output uncertainty for the input space of interest is updated simultaneously with the model parameter distributions, thus resulting in an iterative process which systematically refines the models and improves the test plan as the campaign progresses. The CCSI2 team has implemented this methodology while collecting data at TCM, resulting in an average (considering the entire input space) reduction in estimated model uncertainty of about 60%; capture predictions improved from +/- ~10% to +/- ~4% across the operating range studied. In addition to the SDoE procedure, additional plant data have been gathered to support optimization of the economic objective function under uncertainty.
TCM’s importance to results
The collaboration with TCM has been valuable for demonstrating the CCSI2 SDoE methodology for solvent-based CO2 capture for many reasons, and a few are highlighted here. Due to the iterative nature of the SDoE procedure, this project could not have been successful without the flexibility and responsiveness of the engineering and operating staff at TCM. Furthermore, a wide range of capture plant operability was also essential for SDoE, and made possible by TCM’s willingness to explore simultaneous changes in many operating variables, including flowrates of circulating solvent, flue gas, and reboiler steam as well as flue gas CO2 content. Finally, the value of this collaboration was greatly enhanced by TCM’s offer to extend the test campaign from three to five weeks in order to allow more detailed study of variation in process configurations, such as absorber bed height. These additional data provide valuable information regarding the robustness of CCSI2’s existing process model for future optimization and scale-up studies.