Designing learning curves for carbon capture based on chemical absorption according to the minimum work of separation

Pedro R.R. Rochedo and Alexandre Szklo

Applied Energy, 2013, vol. 108, issue C, 383-391

Abstract: Carbon capture is one of the most important alternatives for mitigating greenhouse gas emissions in energy facilities. The post-combustion route based on chemical absorption with amine solvents is the most feasible alternative for the short term. However, this route implies in huge energy penalties, mainly related to the solvent regeneration. By defining the minimum work of separation (MWS), this study estimated the minimum energy required to capture the CO2 emitted by coal-fired thermal power plants. Then, by evaluating solvents and processes and comparing it to the MWS, it proposes the learning model with the best fit for the post-combustion chemical absorption of CO2. Learning models are based on earnings from experience, which can include the intensity of research and development. In this study, three models are tested: Wright, DeJong and D&L. Findings of the thermochemical analysis indicated a MWS of 0.158GJ/t for post-combustion. Conventional solvents currently present an energy penalty eight times the MWS. By using the MWS as a constraint, this study found that the D&L provided the best fit to the available data of chemical solvents and absorption plants. The learning rate determined through this model is very similar to the ones found in the literature.

Keywords: Carbon capture; Chemical absorption; Learning curve; Exergy analysis (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261913001931
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:108:y:2013:i:c:p:383-391

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.2013.03.007

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 ().