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Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process

Author

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  • Morgan, Joshua C.
  • Chinen, Anderson Soares
  • Anderson-Cook, Christine
  • Tong, Charles
  • Carroll, John
  • Saha, Chiranjib
  • Omell, Benjamin
  • Bhattacharyya, Debangsu
  • Matuszewski, Michael
  • Bhat, K. Sham
  • Miller, David C.

Abstract

In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis.

Suggested Citation

  • Morgan, Joshua C. & Chinen, Anderson Soares & Anderson-Cook, Christine & Tong, Charles & Carroll, John & Saha, Chiranjib & Omell, Benjamin & Bhattacharyya, Debangsu & Matuszewski, Michael & Bhat, K. S, 2020. "Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process," Applied Energy, Elsevier, vol. 262(C).
  • Handle: RePEc:eee:appene:v:262:y:2020:i:c:s0306261920300453
    DOI: 10.1016/j.apenergy.2020.114533
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    References listed on IDEAS

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    1. Rubén M. Montañés & Nina E. Flø & Lars O. Nord, 2017. "Dynamic Process Model Validation and Control of the Amine Plant at CO 2 Technology Centre Mongstad," Energies, MDPI, vol. 10(10), pages 1-36, October.
    2. Sipöcz, Nikolett & Tobiesen, Finn Andrew & Assadi, Mohsen, 2011. "The use of Artificial Neural Network models for CO2 capture plants," Applied Energy, Elsevier, vol. 88(7), pages 2368-2376, July.
    3. Kim, Youngmin & Jang, Hochang & Kim, Junggyun & Lee, Jeonghwan, 2017. "Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network," Applied Energy, Elsevier, vol. 185(P1), pages 916-928.
    4. repec:dau:papers:123456789/3549 is not listed on IDEAS
    5. Ryan, Elizabeth G. & Drovandi, Christopher C. & Pettitt, Anthony N., 2015. "Simulation-based fully Bayesian experimental design for mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 26-39.
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    Cited by:

    1. Kim, Jeongnam & Na, Jonggeol & Kim, Kyeongsu & Bak, Ji Hyun & Lee, Hyunjoo & Lee, Ung, 2021. "Learning the properties of a water-lean amine solvent from carbon capture pilot experiments," Applied Energy, Elsevier, vol. 283(C).
    2. Yang, Qiulian & Li, Haitao & Wang, Dong & Zhang, Xiaochun & Guo, Xiangqian & Pu, Shaochen & Guo, Ruixin & Chen, Jianqiu, 2020. "Utilization of chemical wastewater for CO2 emission reduction: Purified terephthalic acid (PTA) wastewater-mediated culture of microalgae for CO2 bio-capture," Applied Energy, Elsevier, vol. 276(C).

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