The performance of pH adjustment can vary significantly, depending on the complexity of the system response towards the addition of a titrating agent. To demonstrate the broad applicability of the pH adjustment strategy, we tested our approach on a variety of different chemical systems. 18 experimentally generated datasets of binary buffered mixtures, containing the acid/base volume addition as the input and the measured pH value as the output were used, see Table . Based on the existence of this experimental data, simulated closed-loop optimization was conducted. The majority of the datapoints were held out and only a randomly selected batch of datapoints for initializing the model was used. The strategy (Figure ) was applied, and the target pH was set to pH 6, with an acceptable deviation of a pH value of ± 0.2. For mixture 6 the initial pH of the sample was already within the target pH margin so the objective was set to pH 8. This workflow was conducted 10 times for each dataset and the mean/standard deviation of the number of iterations needed to achieve the target pH were calculated. A broad comparison of the 18 buffer systems and the choice of the ML model was conducted to assess the required number of iterations to conduct pH adjustment (Figure ). Chemical systems with multiple protons tend to have more linear areas whereas a system comprised of two single chemicals (e.g., ammonium, acetate) tends to be less smooth, see SI Figure .