DescriptionOptimizing chemical processes has traditionally been carried out using trial-and-error laboratory work, for example the one-variable-at-a-time (OVAT) approach. Recently, however, more advanced strategies, such as the Design of Experiments (DoE) approach and even more complex machine-learning algorithms have been reported in literature. Combining these advanced optimization strategies with automated continuous processes and in-line analytics has led to the so-called field of self-optimization of chemical processes. The focus of the optimization algorithms employed in the field is to find sets of process parameters to optimize a specific process step outcome, such as the yield of chemical reactions. Currently, two of the most widely used methods found in literature are the Nelder-Mead Simplex Algorithm and the Stable Noisy Optimization by Branch and FIT (SNOBFIT) method.
While the extant literature in the field of self-optimization has shown the successful application of both mentioned methods, a wide variety of conceivably suitable algorithms exist in computational sciences, that have not yet been used in the self-optimization of chemical process steps.
In this work, 16 derivative-free optimization algorithm implementations were compared both theoretically and via a real-life model reaction. A benchmarking procedure was carried out, meaning that the algorithms were applied on a set of test functions or test problems. All algorithms were tested in three categories: single-optimum test problems with and without noise, as well as multi-optimum test problems without noise. After the benchmarking, all algorithms were then compared via a Suzuki-Miyaura cross-coupling reaction in continuous flow. A fully automated flow setup was developed capable of performing experiments and analyzing the reaction yield in real-time. This was combined with in-house developed Python script, capable of hosting the different optimization algorithms on one platform
Our results show that multiple novel algorithm implementations outperformed the Nelder-Mead and SNOBFIT algorithms in both the theoretical and practical cases, using average number of iterations needed and percentage of successful optimizations as ranking criteria.
|Period||24 Aug 2022|
|Event title||ACHEMA 2022|
|Degree of Recognition||International|
- flow chemistry
- Self Optimisation