Enabling CO2 capture and storage using AI

使用人工智能实现二氧化碳捕获和存储

• 批准号: EP/Y006143/1
• 负责人: Ahmed Elsheikh
• 金额: $ 228.16万
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY006143%2F1
• 关键词: Enabling; CO2; capture; storage; using

The International Energy Agency (IEA) has identified Carbon Capture and Storage (CCS) in deep geological formation as one of the key approaches to reduce CO2 emissions. CCS is a combination of technologies for CO2 capture from large emitter industries and CO2 storage in deep geological formations, preventing its release back into the atmosphere. Currently, there are key barriers for the wide adoption of CCS on a large scale, such as (a) the high cost of CO2 capture that is an energy intensive chemical process, (b) the high cost of subsurface CO2 storage especially at the early stages of site selection and characterization of safe storage sites, and (c) the uncertainties in how CCS projects are financed and the interplay between technological innovation and policy intervention on the CO2 market and emission targets.In this project, we aim to utilize our expertise in AI to address these barriers. The first is to accelerate material discovery for energy efficient CO2 capture using liquid solvent (a type of liquid that serves to dissolve CO2). In this task, AI aims to replace standard expensive predictive methods (using molecular dynamic simulations) with fast and robust tools using machine learning. Further, the search of possible solvents will be accelerated by using effective tools developed by the AI community for high dimensional optimisation and control. For the CO2 storage site selection, numerical simulations provide a pathway to understand the long-term fate of injected CO2 and risks of leakage back to the atmosphere. However, standard numerical simulations are expensive, fail to propagate flow information from the small-scale to the large-scale flow features and generally underestimates the geological uncertainty. In this task, AI will be used to model flow in the subsurface by fast digital twins to help design and manage CO2 storage with an ability to link scales and include all sources of uncertainty. Recently, we have developed a new, and potentially revolutionary, AI methods using repurposed AI software libraries to implement some of the standard numerical methods applied in computational physics codes to gain platform-independent codes with increased performance. Further, AI libraries are much easier to couple and allows us to bridge information across-scales effectively.Financing CCS projects necessitate policy intervention. We employ network sciences and novel forecasting methods to study and understand the complex interaction of the rate of innovation, policy and CO2 markets on adoption of CCS technologies. In summary, we will develop AI techniques to decrease the cost of CCS projects via advance simulation techniques, better financial modelling and discovery of new energy efficient capture solvents.

国际能源机构（IEA）已将深层地质形成的碳捕获和储存（CCS）确定为减少二氧化碳排放的关键方法之一。 CCS是从大型发射极产业中捕获二氧化碳的技术和深层地质地层中的二氧化碳储存的组合，从而阻止了其释放到大气中。目前，大规模采用CC的主要障碍，例如（a）高度捕获的高成本是能源密集型化学物质过程，（b）地下CO2存储的高成本，尤其是在现场选择的早期阶段，尤其是在安全存储站点的早期阶段，以及在安全存储站点的表征，以及（c）CCS项目之间的不确定性以及CCS项目之间的不确定性范围内的不确定性。项目，我们旨在利用我们在AI中的专业知识来解决这些障碍。首先是使用液体溶剂（一种用于溶解二氧化碳的液体）加速材料发现，以捕获节能二氧化碳。在此任务中，AI旨在用使用机器学习的快速，健壮的工具替换标准昂贵的预测方法（使用分子动态模拟）。此外，通过使用AI社区开发的有效工具来加快对可能溶剂的搜索，以进行高维优化和控制。对于二氧化碳存储位点的选择，数值模拟提供了一种途径，以了解注射二氧化碳的长期命运以及泄漏回大气的风险。但是，标准的数值模拟昂贵，无法传播从小规模到大规模流量特征的流量信息，并且通常低估了地质不确定性。在此任务中，AI将使用快速数字双胞胎在地下中的地下进行建模，以帮助设计和管理具有链接量表并包含所有不确定性来源的CO2存储。最近，我们使用重新利用的AI软件库开发了一种新的，潜在的革命性AI方法，以实现在计算物理代码中应用的一些标准数值方法，以获取与平台无关的代码，并且性能提高。此外，AI库更容易夫妻，并允许我们有效地跨越范围的信息。为CCS项目进行政策干预。我们采用网络科学和新颖的预测方法来研究和了解采用CCS技术的创新，政策和二氧化碳市场的复杂相互作用。总而言之，我们将开发AI技术，以通过预先模拟技术，更好的财务建模和新的能源有效捕获溶剂的发现来降低CCS项目的成本。