Enabling CO2 capture and storage using AI

国际能源署（IEA）已将深层地质地层中的碳捕获与封存（CCS）确定为减少二氧化碳排放的关键方法之一。CCS是一种结合了从大型排放工业中捕获二氧化碳和在深层地质构造中储存二氧化碳的技术，以防止其释放回大气。目前,有关键壁垒大规模CCS技术的广泛采用,如(一)二氧化碳捕获的高成本,是一个能源密集型的化学过程,(b)地下二氧化碳存储的高成本尤其是在早期阶段的选址和描述安全存储网站,和(c)的不确定性CCS项目提供资金和技术创新之间的相互作用和政策干预市场上的二氧化碳减排目标。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。第一个目标是加速利用液体溶剂（一种用于溶解二氧化碳的液体）高效捕获二氧化碳的材料的发现。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的昂贵的预测方法（使用分子动力学模拟）。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，可能的溶剂的搜索将加速。对于二氧化碳储存地点的选择，数值模拟提供了一个途径来了解注入二氧化碳的长期命运和泄漏回大气的风险。然而，标准数值模拟成本高，不能将小尺度流动特征的流动信息传播到大尺度流动特征，并且通常低估了地质不确定性。在这项任务中，人工智能将通过快速数字双胞胎来模拟地下流动，以帮助设计和管理二氧化碳储存，并具有连接尺度和包括所有不确定性来源的能力。最近，我们开发了一种新的，潜在的革命性的人工智能方法，使用重新利用的人工智能软件库来实现一些应用于计算物理代码的标准数值方法，以获得具有更高性能的平台无关代码。此外，人工智能库更容易配对，并允许我们有效地跨尺度连接信息。为CCS项目融资需要政策干预。我们运用网络科学和新颖的预测方法来研究和理解创新速度、政策和二氧化碳市场对CCS技术采用的复杂相互作用。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集溶剂来降低CCS项目的成本。

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.