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.

国际能源署(IEA)已将深部地质地层中的碳捕获和封存(CCS)确定为减少二氧化碳排放的关键途径之一。CCS是一种从大型排放器行业捕获二氧化碳的技术与在深层地质地层中储存二氧化碳的技术相结合，以防止其释放回大气中。目前，大规模采用CCS存在一些关键障碍，例如(A)二氧化碳捕集的高成本，这是一个能源密集型的化学过程；(B)地下二氧化碳储存的高成本，特别是在安全储存点的选址和表征的早期阶段；以及(C)CCS项目融资方式的不确定性，以及技术创新和对二氧化碳市场和排放目标的政策干预之间的相互影响。在这个项目中，我们的目标是利用我们在人工智能方面的专业知识来解决这些障碍。第一种是使用液体溶剂(一种用于溶解二氧化碳的液体)加速材料的发现，以实现高效的二氧化碳捕获。在这项任务中，人工智能的目标是用使用机器学习的快速和强大的工具来取代标准的昂贵的预测方法(使用分子动态模拟)。此外，通过使用人工智能社区开发的用于高维优化和控制的有效工具，将加快可能溶剂的搜索速度。对于二氧化碳储存点的选择，数值模拟提供了一条途径，以了解注入的二氧化碳的长期命运和泄漏回大气的风险。然而，标准的数值模拟代价昂贵，不能将流动信息从小尺度传播到大尺度流动特征，而且普遍低估了地质不确定性。在这项任务中，人工智能将被快速数字双胞胎用来模拟地下的流动，以帮助设计和管理二氧化碳储存，并能够将尺度联系起来，并包括所有不确定源。最近，我们开发了一种新的、可能是革命性的人工智能方法，使用重新定位的人工智能软件库来实现计算物理代码中应用的一些标准数值方法，以获得性能更高的与平台无关的代码。此外，人工智能库更容易耦合，并允许我们有效地跨规模连接信息。资助CCS项目需要政策干预。我们使用网络科学和新的预测方法来研究和理解创新速度、政策和二氧化碳市场在采用CCS技术方面的复杂交互作用。总而言之，我们将开发人工智能技术，通过先进的模拟技术、更好的财务建模和发现新的节能捕获溶剂来降低CCS项目的成本。