The sample starts at a high pH > 12 and upon a small random addition of acid (here = 55 𝜇𝐿), the pH does not decrease much. Therefore, at the next step, the model thinks it needs to add lots of acid. Here a constraint is activated, and in this case, an upper bound of 1 mL of citric acid is added. This causes the pH to overshoot the set point and drop to close to 4.5. From here on sodium hydroxide is added back as a correction, but the strong base is reacting with the citric acid to convert some of it into its conjugate base and thereby form a citrate buffer system which is making the whole formulation challenging to titrate. In reality, a buffer system so resistant to pH change is formed, that more than 1 mL of base needs to be added back and we would not return to our starting pH; however, the Bayesian method does not know this. The surrogate model still thinks that at -55 𝜇𝐿 the pH will be ~12 and so we see it takes several iterations (12 are presented in Figure ) where the suggested experiments creep back to the starting point. However, the model cannot go there as it falsely predicts an almost instantaneous jump to the pH point from the initialisation. The model is not smart. In fact, this experiment continued for a further 4 iterations until the GP was unable to model the discontinuous jump and finally failed after 16 iterations.