Machine learning based techno-economic process optimisation for CO₂ capture via enhanced weathering

<p>This work evaluated the practicability and economy of the enhanced weathering (EW)-based CO₂ capture in series packed bubble column (S-PBC) contactors operated with different process configurations and conditions. The S-PBC contactors are designed to fully use the advantages of abundant seawater and highly efficient freshwater through a holistic M⁴ model, including multi-physics, machine learning, multi-variable and multi-objective optimisation. An economic analysis is then performed to investigate the cost of different S-PBC configurations. A data-driven surrogate model based on a novel machine learning algorithm, extended adaptive hybrid functions (E-AHF), is implemented and trained by the data generated by the physics-based models. GA and NSGA-II are applied to perform single- and multi-objective optimisation to achieve maximum CO₂ capture rate (CR) and minimum energy consumption (EC) with the optimal values of eight design variables. The R² for the prediction of CR and EC is higher than 0.96 and the relative errors are lower than 5%. The M⁴ model has proven to be an efficient way to perform multi-variable and multi-objective optimisation, that significantly reduces computational time and resources while maintaining high prediction accuracy. The trade-off of the maximum CR and minimum EC is presented by the Pareto front, with the optimal values of 0.1014 kg h⁻¹ for CR and 6.1855 MJ kg⁻¹CO₂ for EC. The calculated net cost of the most promising S-PBC configuration is around 400 $ t⁻¹CO₂, which is about 100 $ t⁻¹CO₂ lower than the net cost of current direct air capture (DAC), but compromised by slower CO₂ capture rate.</p>