Machine learning for modelling and control of direct air capture systems

EPSRC：Stefan Radic Webster：EP/S022937/1安置项目将研究使用机器学习对海上风能直接空气捕获(DAC)系统进行建模和控制。DAC从注入深海水库的大气中去除二氧化碳(CO2)，是一项关键的负排放技术，作为实现《巴黎协定》目标的气候变化缓解战略，这项技术正变得越来越重要。由于风电系统运行的复杂性和间歇性，给DAC系统的控制带来很大困难。控制DAC系统的传统方法使用数学建模，这是耗时的，需要专业专家知识，并且执行的计算成本很高。该项目将利用深度学习和强化学习等机器学习的最新发展，使用数据驱动的方法来模拟和控制发声控制系统。其目的是展示机器学习在这种设置下的可行性，并实现一种自适应的、基于学习的控制器，以最大化二氧化碳捕获率。

EPSRC : Stefan Radic Webster : EP/S022937/1The placement project will investigate the use of machine learning for modelling and control of an offshore wind powered direct air capture (DAC) system. DAC remove carbon dioxide (CO2) from the atmosphere which is injected into deep-sea reservoirs and are a key negative emission technology, which are becoming increasingly vital as a climate change mitigation strategy to achieve the targets of the Paris Agreement. The problem of controlling DAC systems is difficult due to the complex operation and the intermittency of the wind power supply. A traditional approach for controlling DAC systems uses mathematical modelling, which is time consuming, requires specialist expert knowledge and is computationally expensive to execute. The project will use a data-driven approach to modelling and controlling DAC systems by drawing on the latest developments in machine learning such as deep learning and reinforcement leaning. The objective is to show the feasibility of machine learning in this setting and implement an adaptive, learning-based controller that maximises the CO2 capture rate.