Responsive CO2 capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply

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.