Autonomous Multi‐Step and Multi‐Objective Optimization Facilitated by Real‐Time Process Analytics

Abstract

Autonomous flow reactors are becoming increasingly utilized in the synthesis of organic compounds, yet the complexity of the chemical reactions and analytical methods remains limited. The development of a modular platform which uses rapid flow NMR and FTIR measurements, combined with chemometric modeling, is presented for efficient and timely analysis of reaction outcomes. This platform is tested with a four variable single-step reaction (nucleophilic aromatic substitution), to determine the most effective optimization methodology. The self-optimization approach with minimal background knowledge proves to provide the optimal reaction parameters within the shortest operational time. The chosen approach is then applied to a seven variable two-step optimization problem (imine formation and cyclization), for the synthesis of the active pharmaceutical ingredient edaravone. Despite the exponentially increased complexity of this optimization problem, the platform achieves excellent results in a relatively small number of iterations, leading to >95% solution yield of the intermediate and up to 5.42 kg L −1 h −1 space-time yield for this pharmaceutically relevant product.

Analytical Tools Integrated in Continuous-Flow Reactors: Which One for What?

“…Such flow platforms require monitoring of the transformation at multiple points within the flow stream by suitable analytical instruments. Kappe’s group has significantly contributed to this field of research. − As an example, they integrated three different in-line/online analytical instruments within a modular microreactor system for the optimization and in-process monitoring of a multistep organolithium transformation. The two-step transformation of tert -butyl propionate 55 into aryl propionate 56 was performed in a continuous-flow Modular Microreactor System (MMRS) .…”

Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform

“…Algorithm-guided automated optimization of reaction conditions to maximize a desired objective function (e.g., yield) is another example of machine assistance in organic synthesis. ,− In this approach, an algorithm proposes reaction conditions to evaluate within a defined search space (set of values that continuous or categorical process variables can take) based on feedback and analysis of results from previous experiments. Coupling the algorithm with an automated synthesis platform and inline/online PAT tools results in closed-loop design and execution of experiments for efficient reaction development. − Prior applications of algorithmic optimization to chemistry have focused primarily on single-step transformations involving model reactions, with recent work on two-step processes. − Furthermore, only a few reports consider categorical reaction variables ,, (e.g., catalyst) and multiple objectives. , In reality, however, process development for functional organic compounds often involves multireaction pathways with categorical reagent choices and several process metrics of interest (e.g., yield, productivity).…”

Artificial neural networks and data fusion enable concentration predictions for inline process analytics

“…4 The large amount of recorded data must be stored, processed, and analyzed in a reliable automated workflow. 5 Real-time data from automated continuous flow platforms enables, amongst others, the use of dynamic experimentation, 6 automated self-optimization, 7 kinetic model building, 8 and feedback loops for process control. 9…”