The increase in global carbon dioxide emission has raised concerns about climate change. This has caused nations to consider different carbon dioxide mitigation pathways to reduce emissions. The iron and steel industry contributes to approximately 30% of total global CO2 direct emission in the industrial sector. It is an energy intense industry. Many steel mills are operating close to thermodynamic limits in efficiency. Therefore decarbonising the steel industry through process improvements is limited. Breakthrough technologies such as carbon capture and storage (CCS) is an alternative and attractive solution. In this research I have explored the application of a retrofit carbon capture technology to an existing steel mill. The steel mill chosen, combusts gases arising from the steel making processes. Different locations within the steel mill were analysed, the in-house power station and the turbo blower house were chosen for retrofit post-combustion carbon capture. Two different separation technologies were process modelled to capture the carbon dioxide from the flue gas of the in-house power station and the turbo blower house. The technologies were chemical absorption and adsorption. The two technologies were techno-economically studied. Chemical absorption, with solvent MEA, showed capability of recovering 86% of CO2 with a purity of more than 99 mol%. Adsorption using sorbent zeolite 13X was able to achieve 82% recovery with purity of 96 mol%. Sorbent activated carbon showed a capability of recovering 67% of carbon dioxide with a purity of 95 mol%. The cost of CO2 avoidance for the process using chemical absorption (MEA) was equal to $44.92/tonne CO2. For the process using adsorption (zeolite 13X) the CO2 avoided cost was equal to $44.90/tonne of CO2. Activated carbon was the most expensive capture process, out of the three processes studied. It costs $45.81/tonne of CO2 avoidance.
Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:689800 |
Date | January 2016 |
Creators | Duwahir, Zahras Mohamed |
Publisher | University of Nottingham |
Source Sets | Ethos UK |
Detected Language | English |
Type | Electronic Thesis or Dissertation |
Source | http://eprints.nottingham.ac.uk/32403/ |
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