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）已将深层地质构造中的碳捕获和储存（CCS）确定为减少二氧化碳排放的关键方法之一。CCS是从排放量大的工业中捕获CO2和将CO2储存在深层地质构造中以防止其释放回大气的技术的结合。目前，在大规模广泛采用CCS方面存在着一些关键障碍，例如（a）作为一种能源密集型化学过程的CO2捕集成本高，（B）地下CO2储存成本高，特别是在选址和安全储存地点定性的早期阶段，以及（c）如何为CCS项目融资的不确定性，以及技术创新与对CO2市场和排放目标的政策干预之间的相互作用。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。第一个是加速材料发现，以使用液体溶剂（一种用于溶解CO2的液体）进行节能CO2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取代标准昂贵的预测方法（使用分子动态模拟）。此外，通过使用人工智能社区开发的有效工具进行高维优化和控制，将加速对可能溶剂的搜索。对于CO2储存地点的选择，数值模拟提供了一种途径，以了解注入的CO2的长期命运和泄漏回大气的风险。然而，标准的数值模拟是昂贵的，无法传播流动信息从小规模到大规模的流动功能，一般低估了地质的不确定性。在这项任务中，人工智能将用于通过快速数字孪生模型对地下流动进行建模，以帮助设计和管理二氧化碳储存，并能够链接规模并包括所有不确定性来源。最近，我们开发了一种新的，潜在的革命性的人工智能方法，使用重新设计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，以获得性能更高的平台无关代码。此外，人工智能库更容易耦合，使我们能够有效地跨尺度连接信息。我们采用网络科学和新的预测方法来研究和理解创新率，政策和二氧化碳市场对采用CCS技术的复杂相互作用。总之，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。