Additionally, successful uses of ANNs to predict adsorption process performance exist in literature to also support this choice, as discussed in the ESI Section 2.1. The ANNs were implemented in Python using TensorFlow. Firstly, the dataset was split randomly into a training and test set in a ratio. The test set was defined as 10% of the total dataset, whilst the training set is the other 90%. Then we normalised the training set using Z-score normalisation, which involves subtracting the mean value from each input and output before dividing by its standard deviation. The ANN applied was a feedforward network with dense layers and no dropout. The activation unit used for each node is the rectified linear unit (ReLU), and the optimiser used for training is the Adam optimiser with a learning rate of 0.0005. Early stopping was applied as a regularisation strategy with a stopping patience of 100 epochs and a validation dataset of 5% of the training dataset.