Corrosion Prediction in Residual CO2 Streams: Shifting the Paradigm

残余二氧化碳流中的腐蚀预测：转变范式

• 批准号: EP/P032737/1
• 负责人: Anne Neville
• 金额: $ 7.71万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP032737%2F1
• 关键词: Corrosion; Prediction; Residual; CO2; Streams

Cost-effective Carbon Capture and Storage (CCS) offers one option for meeting the goals of the Paris Agreement, reached at COP 21 in December 2015. Whilst there have been major efforts to develop technologies for capture and storage, transportation between the two points has received less attention to date. However, there is now an increasing awareness that this is an important focus area. The behaviour of dense phase and supercritical CO2 mixtures containing largeamounts of possible impurities is not well understood. Accurate predictions of the physical properties and corrosion potential of these mixtures will be needed for the cost-effective design and operation of both capture and disposal systems. The overall goal of this project is to improve the predictive tools for both physical properties and corrosion rates and to demonstrate their accuracy against laboratory and field data in order to increase the confidence and gain industry acceptance of the predictions being made. An experimental framework will provide the necessary inputs for the corrosion related aspects of the model, whilst the phase equilibria model will enable exact conditions to be described.

具有成本效益的碳捕获和封存（CCS）为实现2015年12月COP21达成的《巴黎协定》目标提供了一种选择。虽然在开发捕获和储存技术方面作出了重大努力，但迄今为止，这两个地点之间的运输受到的关注较少。然而，现在人们越来越认识到这是一个重要的重点领域。密相和超临界CO2混合物含有大量可能的杂质的行为还没有很好的理解。要使捕获和处置系统的设计和运作具有成本效益，就需要准确预测这些混合物的物理性质和腐蚀潜力。该项目的总体目标是改进物理性能和腐蚀速率的预测工具，并根据实验室和现场数据证明其准确性，以增加信心并获得行业对预测的认可。实验框架将为模型的腐蚀相关方面提供必要的输入，而相平衡模型将能够描述精确的条件。

项目成果

Protectiveness, morphology and composition of corrosion products formed on carbon steel in the presence of Cl-, Ca2+ and Mg2+ in high pressure CO2 environments

• DOI: 10.1016/j.apsusc.2018.05.140
• 发表时间: 2018-10-15
• 期刊: APPLIED SURFACE SCIENCE
• 影响因子: 6.7
• 作者: Hua, Yong;Shamsa, Amir;Neville, Anne
• 通讯作者: Neville, Anne