Comparison of Derivative‐Free Algorithms for their Applicability in Self‐Optimization of Chemical Processes. Issue 5 (15th February 2022)

• Record Type: Journal Article
• Authors: Soritz, Sebastian; Moser, Daniel; Gruber‐Wölfler, Heidrun
• Is Part Of: Chemistry methods. Volume 2:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-15
• Subjects: algorithms -- automation -- cross-coupling -- flow chemistry -- self-optimization; Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1002/cmtd.202100091
• Languages: English
• ISSNs: 2628-9725

Abstract

Abstract: In this work, several implementations of different derivative‐free optimization algorithms are compared for the usage in chemical process optimization. As such, a benchmarking process is carried out, using optimization problems of different types to compare reliability, accuracy, and performance. Finally, using an automated reaction setup and a bespoke Python‐based script featuring a graphical user interface, all algorithms are tested in an optimization of a Suzuki‐Miyaura cross‐coupling reaction in continuous flow. To increase the scope of comparison, a model function based on the reaction is also used, to allow for a more in‐depth comparison without the use of physical resources. Abstract : Currently unused black box optimization algorithms are compared against two popular self‐optimization methods, the Nelder‐Mead algorithm and the SNOBFIT method. Using various test problems and a real‐life continuous flow Suzuki‐Miyaura coupling reaction, two promising methods which surpassed the predominant methods in terms of reliability and speed of convergence were identified.