| Autor: |
Mohammad Reza Ketabchi, Shervan Babamohammadi, William George Davies, Mikhail Gorbounov, Salman Masoudi Soltani |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Carbon Capture Science & Technology, Vol 6, Iss , Pp 100087- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2772-6568 |
| DOI: |
10.1016/j.ccst.2022.100087 |
| Popis: |
Effective decarbonisation is key to ensuring the temperature rise does not exceed the 2 °C set by the Paris accords. Adsorption is identified as a key technology for post-combustion carbon capture. This rise in prominence of such processes is owed to the fact that application of solid sorbents does not lead to the generation of secondary waste streams. In fact, sorbents can be produced from waste material (e.g. bio-based sorbents). Bio-based sorbents have become an increasingly attractive option; food waste, agricultural and municipal sources can be employed as precursors. These sorbents can be physically and chemically activated and then further modified to produce sorbents that can capture CO2 effectively. The employment of these types of sorbents, however, often entails geological and operational challenges. Understanding how these sorbents can be deployed at scale and the geological challenges associated with bio-based sorbents are key research areas that must be further investigated. Process modelling and machine learning can provide insights into these challenges especially within optimization of adsorption processes and sorbent development. This paper aims to provide a state-of-the-art review of the synthesis of bio-based sorbents and their application within post-combustion carbon capture processes as well as the recent trends of utilizing machine learning for the development of these sorbents, and the design of the corresponding adsorption processes alike. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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