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Title: Sequential Design of Experiments to Maximize Learning from Carbon Capture Pilot Plant Testing

Abstract

Pilot plant test campaigns can be expensive and time-consuming. Therefore, it is of interest to maximize the amount of learning and the efficiency of the test campaign given the limited number of experiments that can be conducted. This work investigates the use of sequential design of experiments (SDOE) to overcome these challenges by demonstrating its usefulness for a recent solvent-based CO2 capture plant test campaign. Unlike traditional design of experiments methods, SDOE regularly uses information from ongoing experiments to determine the optimum locations in the design space for subsequent runs within the same experiment. However, there are challenges that need to be addressed, including reducing the high computational burden to efficiently update the model, and the need to incorporate the methodology into a computational tool. We address these challenges by applying SDOE in combination with a software tool, the Framework for Optimization, Quantification of Uncertainty and Surrogates (FOQUS) (Miller et al., 2014a, 2016, 2017). The results of applying SDOE on a pilot plant test campaign for CO2 capture suggests that relative to traditional design of experiments methods, SDOE can more effectively reduce the uncertainty of the model, thus decreasing technical risk. Future work includes integrating SDOE into FOQUS and usingmore » SDOE to support additional large-scale pilot plant test campaigns.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). In-house Research
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1422539
Report Number(s):
NETL-PUB-21602
Resource Type:
Journal Article
Resource Relation:
Journal Name: Proceedings of the 13th International Symposium on Process Systems Engineering - PSE 2018; Conference: Proceedings of the 13th International Symposium on Process Systems Engineering; PSE 2018; July 1-5, 2018; San Diego, California, USA
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; 42 ENGINEERING; 97 MATHEMATICS AND COMPUTING; Bayesian method; computational tool; space-filling design; minimizing uncertainty

Citation Formats

Soepyan, Frits B., Morgan, Joshua C., Omell, Benjamin P., Zamarripa-Perez, Miguel A., Matuszewski, Michael S., and Miller, David C.. Sequential Design of Experiments to Maximize Learning from Carbon Capture Pilot Plant Testing. United States: N. p., 2018. Web.
Soepyan, Frits B., Morgan, Joshua C., Omell, Benjamin P., Zamarripa-Perez, Miguel A., Matuszewski, Michael S., & Miller, David C.. Sequential Design of Experiments to Maximize Learning from Carbon Capture Pilot Plant Testing. United States.
Soepyan, Frits B., Morgan, Joshua C., Omell, Benjamin P., Zamarripa-Perez, Miguel A., Matuszewski, Michael S., and Miller, David C.. Tue . "Sequential Design of Experiments to Maximize Learning from Carbon Capture Pilot Plant Testing". United States. doi:. https://www.osti.gov/servlets/purl/1422539.
@article{osti_1422539,
title = {Sequential Design of Experiments to Maximize Learning from Carbon Capture Pilot Plant Testing},
author = {Soepyan, Frits B. and Morgan, Joshua C. and Omell, Benjamin P. and Zamarripa-Perez, Miguel A. and Matuszewski, Michael S. and Miller, David C.},
abstractNote = {Pilot plant test campaigns can be expensive and time-consuming. Therefore, it is of interest to maximize the amount of learning and the efficiency of the test campaign given the limited number of experiments that can be conducted. This work investigates the use of sequential design of experiments (SDOE) to overcome these challenges by demonstrating its usefulness for a recent solvent-based CO2 capture plant test campaign. Unlike traditional design of experiments methods, SDOE regularly uses information from ongoing experiments to determine the optimum locations in the design space for subsequent runs within the same experiment. However, there are challenges that need to be addressed, including reducing the high computational burden to efficiently update the model, and the need to incorporate the methodology into a computational tool. We address these challenges by applying SDOE in combination with a software tool, the Framework for Optimization, Quantification of Uncertainty and Surrogates (FOQUS) (Miller et al., 2014a, 2016, 2017). The results of applying SDOE on a pilot plant test campaign for CO2 capture suggests that relative to traditional design of experiments methods, SDOE can more effectively reduce the uncertainty of the model, thus decreasing technical risk. Future work includes integrating SDOE into FOQUS and using SDOE to support additional large-scale pilot plant test campaigns.},
doi = {},
journal = {Proceedings of the 13th International Symposium on Process Systems Engineering - PSE 2018},
number = ,
volume = ,
place = {United States},
year = {Tue Feb 06 00:00:00 EST 2018},
month = {Tue Feb 06 00:00:00 EST 2018}
}