Dynamic-model-based artificial neural network for H2 recovery and CO2 capture from hydrogen tail gas

Nguyen Dat Vo, Dong Hoon Oh, Jun-Ho Kang, Min Oh and Chang-Ha Lee

Applied Energy, 2020, vol. 273, issue C, No S0306261920307753

Abstract: Herein, we developed an integrated process for H2 recovery and CO2 capture from the tail gas of hydrogen plants. The front-sector system (cryogenic, membrane, and compressor units) involved CO2 capture and supply of H2-rich gas to the rear-sector system (heat exchanger (HX) and pressure swing adsorption (PSA) unit) for H2 recovery. The developed dynamic model of the integrated process was validated through reference data. The parametric study highlighted the potential of the developed process for high-purity H2 recovery and CO2 capture. Owing to the complexity of the interconnections, a dynamic-model-based artificial neural network (ANN) for the integrated process was developed to optimize the process performance. The synthetic datasets for the ANN were analyzed by singular value decomposition, and the ANN models for the cryogenic, membrane, and PSA units were trained and tested within a marginal error (<2%). Subsequently, a process-driven model (the integration of the ANN models with the algebraic equations (compressor, HX, and economic evaluation)) was validated through minute deviations from the reference data. The optimization, formulated based on the process-driven model, was conducted using differential evolution. The optimum cost (2.045 $/kg) of recovered H2 (99.99%) was economically comparable to the reference values for H2 production from natural gas. Furthermore, the cost was covered for 91% CO2 capture with 98.6 vol.% CO2. Thus, the result can bridge the gaps in research, development, and implementation and between fossil and renewable energy. Dynamic-model-based ANN can precisely predict the dynamic behavior and optimum performance of an integrated process at a low computational cost.

Keywords: Integrated process; Dynamic-model-based ANN; Optimization-based ANN; CO2 capture; H2 recovery; Hydrogen plant tail gas (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920307753
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:273:y:2020:i:c:s0306261920307753

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.115263

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().