Abstract
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.