Responsive CO 2 capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply
Responsive CO 2 capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply
复制标题
响应式 CO 2 捕获：用于管理间歇性烟气和可再生能源供应的预测性多目标优化
DOI：
10.1039/d3re00544e
复制
发表时间：
2024
影响因子：
3.9
通讯作者：
Fisher O
中科院分区：
文献类型：
--
作者：
Fisher O
提高CO2捕集技术效率的动力在继续增长，对灵活操作以适应富CO2烟道气流速和CO2浓度的强烈波动的需要越来越重视。使用可再生能源可以提高二氧化碳捕获技术的环境效益;然而，可再生能源资源往往受到天气和季节变化造成的发电量不均匀的挑战。在这项工作中，我们的目标是通过在填充泡罩塔（PBC）反应器中用淡水增强方解石的风化来动态地自我优化可再生能源系统中的CO2捕获过程，其中来自发电厂产生的烟道气的CO2被转化为碳酸氢盐并储存在海洋中。PBC反应器的数据驱动的代理动态模型的开发，以预测反应器CO2捕获率（CR）和功耗（PC），并使用基于物理的模型生成的数据进行训练。两个深度学习模型被认为是捕获PBC反应器的动态：长短期记忆网络（LSTM）和两阶段多层感知器网络（MLP）。开发了基于LSTM的数据驱动模型，使用公开的数据集预测风能（R2：0.908）和入口烟气CO2浓度（R2：0.981）。然后应用多目标NSGA-II遗传算法，其利用入口烟道气CO2浓度和风能预测来抢先地自优化反应器工艺条件（即，表观液体流速和表观气体流速），以使碳捕获率最大化并使不可再生能源消耗最小化。结果应该是，通过使用本研究中提出的动态建模和预测多目标优化框架，PCB反应器CR在一个月的运行中平均增加了16.7%，同时将现在可再生能源消耗的比例从平均92.9%降低到平均56.6%。总的来说，这项研究证明了动态数据驱动的建模和多目标优化方法的有效性，以提高CO2捕集反应器的操作灵活性，以适应烟气和间歇性可再生能源供应的强烈波动。
The drive for efficiency improvements in CO2 capture technologies continues to grow, with increasing importance given to the need for flexible operation to adapt to the strong fluctuations in the CO2-rich flue gas flow rate and CO2 concentration. Using renewable energy can improve the environmental benefit of CO2 capture technologies; however, renewable energy resources often suffer from the challenge of non-uniform power generation as a result of weather and seasonal variations. In this work, we aimed to dynamically self-optimise the CO2 capture process in a renewable energy system via enhanced weathering of calcite with fresh water in a packed bubble column (PBC) reactor, in which CO2 from flue gas produced by a power plant is converted into bicarbonate and stored in the ocean. Data-driven surrogate dynamic models of the PBC reactor are developed to predict the reactor CO2 capture rate (CR) and power consumption (PC) and are trained using the data generated by physics-based models. Two deep learning models are considered to capture the dynamics of the PBC reactor: a long short-term memory network (LSTM); and a two-stage multilayer perceptron network (MLP). Data-driven models based on LSTM were developed to predict wind energy (R2: 0.908) and inlet flue gas CO2 concentration (R2: 0.981) using publicly available datasets. A multi-objective NSGA-II genetic algorithm is then applied that utilised the inlet flue gas CO2 concentration and wind energy predictions to pre-emptively self-optimise the reactor process conditions (i.e., superficial liquid flow rate and superficial gas flow rate) to maximise the carbon capture rate and minimise non-renewable energy consumption. The results should that by using the dynamic modelling and predictive multi-objective optimisation framework proposed within this study, the PCB reactor CR increased by an average of 16.7% over a one-month operation, whilst simultaneously reducing the proportion of now-renewable energy consumed from an average of 92.9% to an average of 56.6%. Overall, this study demonstrates the effectiveness of a dynamic data-driven modelling and multi-objective optimisation approach to increase the operational flexibility of CO2 capture reactors to adapt to strong fluctuations in flue gas and intermittent renewable energy supply.