Flow chemistry for process optimisation using design of experiments

Abstract

Implementing statistical training into undergraduate or postgraduate chemistry courses can provide high-impact learning experiences for students. However, the opportunity to reinforce this training with a combined laboratory practical can significantly enhance learning outcomes by providing a practical bolstering of the concepts. This paper outlines a flow chemistry laboratory practical for integrating design of experiments optimisation techniques into an organic chemistry laboratory session in which students construct a simple flow reactor and perform a structured series of experiments followed by computational processing and analysis of the results.

Continuous Hydrogenation: Triphasic System Optimization at Kilo Lab Scale Using a Slurry Solution

“…It was set in four phases: 1) Auxiliary selection. 2) Different sets of conditions were tested empirically to identify key parameters (44 experiments) 3) A DoE approach was used to assess the critical parameters, allowing further study toward the optimal conditions (Taylor et al, 2021) 4) Lastly, a variability analysis on three parameters was carried out to assess the relative impact of each factor on the final result.…”

Comparison of Derivative‐Free Algorithms for their Applicability in Self‐Optimization of Chemical Processes

“…The optimization of chemical processes has traditionally been carried out using trial‐and‐error laboratory work, for example, the one‐variable‐at‐a‐time (OVAT) approach [1,2] . In recent times, however, more advanced strategies, such as Design of Experiments (DoE) and even more complex machine‐learning algorithms have been reported in literature [3–5] . This step from OVAT to multidimensional optimization has been greatly aided by the increasing automation of laboratory equipment, especially in the context of continuous flow processing [6–8] .…”

An automated computational approach to kinetic model discrimination and parameter estimation

“…). Statistical techniques are commonly used to map the design space during process development in modern industrial labs typically through a design of experiments (DoE) approach, 6–8 with closed loop algorithm methods also becoming more popular. 9–11 However, a more robust description of the process can be determined by developing an accurate mechanistic rate model of the chemical reaction steps.” 12,13 …”