The modelling approach employed here combined detailed process modelling with machine learning and global sensitivity analysis. This approach has not been used for direct air capture adsorption processes before and was needed to fulfil this study's objectives: a global sensitivity analysis is required because local sensitivity analyses may not do justice to the complexities and non-linearity of adsorption processes (confirmed by the global sensitivity analysis results in Section 4.2). However, to vary many (here 15 or 16) input parameters of DAC processes, a million or more simulations are needed, which would take years when running a detailed adsorption model on a desktop computer. Therefore, we trained machine learning models on a training set generated with our detailed adsorption model and used the machine learning models for the global sensitivity analysis.