A chosen surrogate model is trained to map the input data (features) to the correlated output data (labels) -using a model-dependent data-processing architecture -by iteratively optimizing the model, i.e., minimizing the loss for all fitted datapoints. The predictive model performance is subsequentially evaluated on a held-out test dataset -the model predictions are compared to the true values and the deviation is typically quantified via the residuals metric of root mean squared error (RMSE). By applying this strategy in an iterative manner, it can be used to navigate through the parameter space, efficiently searching for desired conditions. In the case of pH adjustment this refers to the amount of acid or base to achieve a target pH. Algorithm 1 illustrates the control code used for automated pH adjustment.