In Figure , a scenario is depicted in which for polymers P6, P7 and P4 a series of particle sizes is targeted. Targets were varied per polymer to give an overview of the versatility and general applicability of the BO algorithm. As a cost and time saving measure, in these optimizations, the preceding optimization data was always used as starting set for the following. In this way, in principle each following optimization becomes faster and more efficient, since the algorithm builds on a much more advanced surrogate model. The system achieves optimal process conditions with an accuracy of 5 %, which is challenging if not possible to achieve by human intervention only. Generally, the algorithm takes up to 20 iterations to find the first target point successfully, but as in cases as P7 can sometimes also be faster. The exact number of iterations required is partially serendipitous, and this is reflected in the Figure Self-optimization progress for optimization of a) P6 in THF at concentration of 1 mg/ml, b) P4 in THF at 1.21 mg/ml and c) P7 in THF at 1 mg/ml using volume fraction and total flow rate as variables to optimize. For the optimizations, different target sizes were programmed whereby the preceding optimization is used as starting dataset for the following optimization. data. As seen in the spread of data, the variation of obtained particle sizes is much larger than the difference of the various target sizes. This is not an expression of failure of the algorithm, but more a consequence of the partially steep changes in synthesis outcome after only small variation of reaction conditions, as is seen in the 3D plots of Figure . That in fact in most cases the optimization is not random, can be seen in the fact that following target optimizations are typically fast. From the first optimization already, enough data was collected to allow for relatively precise predictions of following synthesis conditions. In all optimizations that we have tested (not all depicted), the trend was confirmed that as long as the first target wasn't identified by finding a local minimum or a random correct guess of the algorithm, successive target optimizations almost always took less than 10 iterations to be successful. This finding was confirmed by repeating the same optimization sets, but with the variation that the BO algorithm 'forgot' the previous dataset with each starting optimization, and indeed, in that case optimizations took generally around 20-25 iterations per target.