Multi-objective Bayesian optimisation using <i>q</i>-noisy expected hypervolume improvement (<i>q</i>NEHVI) for the Schotten–Baumann reaction

Zhang, Jiyizhe; Sugisawa, Naoto; Felton, Kobi C.; Fuse, Shinichiro; Lapkin, Alexei A.

doi:10.1039/d3re00502j

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…The q-noisy expected hypervolume improvement (qNEHVI) function was superior to other existing acquisition functions for MOBO, for example, it enabled one-step hypervolume maximization in both noisy and noise-free environments, via Equation 5: [41,42] https://doi.org/ (5)…”

Section: Concentration Determinationmentioning

confidence: 99%

Optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective Bayesian optimization and kinetic modeling

Chai,

Xia,

Shen

et al. 2024

Preprint

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The heterogeneous continuous flow hydrogenation is pivotal in chemical research and production, yet its reaction optimization has historically been intricate and labor-intensive. This study introduces a heterogeneous continuous flow hydrogenation system specifically designed for the preparation of 2-amino-3-methylbenzoic acid (AMA), employing FTIR inline analysis coupled with an artificial neural network model for monitoring. We explored two distinct reaction optimization strategies: multi-objective Bayesian optimization (MOBO) and intrinsic kinetic modeling, executed in parallel to optimize the reaction conditions. Remarkably, the MOBO approach achieved an optimal AMA yield of 99% and a productivity of 0.64 g/hour within a limited number of iterations. Conversely, despite requiring extensive experimental data collection and equation fitting, the intrinsic kinetic modeling approach yielded a similar optimal AMA yield but a higher productivity of 1.13 g/hour, attributed to increased catalyst usage. Our findings indicate that while MOBO offers a more efficient route with fewer required experiments, kinetic modeling provides deeper insights into the reaction optimization landscape but is limited by its assumptions.

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Section: Concentration Determinationmentioning

confidence: 99%

Optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective Bayesian optimization and kinetic modeling

Chai,

Xia,

Shen

et al. 2024

Preprint

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“…In recent years, the application of machine learning, particularly Bayesian optimization, has significantly advanced the optimization of continuous flow reactions. − This method enables the rapid identification of optimal reaction conditions with a minimal number of experiments. − For instance, Fuse et al utilized Bayesian optimization to efficiently navigate a search space of 10,500 potential reaction condition combinations, successfully identifying the desired conditions for unsymmetrical sulfamide synthesis in just 29 experiments . The efficiency of Bayesian optimization is notably enhanced when integrated with automated platforms.…”

Section: Introductionmentioning

confidence: 99%

Continuous Flow Synthesis of N,O-Dimethyl-N′-nitroisourea Monitored by Inline Fourier Transform Infrared Spectroscopy: Bayesian Optimization and Kinetic Modeling

Guo,

Luo,

Chai

et al. 2024

Ind. Eng. Chem. Res.

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The synthesis of N,O-dimethyl-N′-nitroisourea, crucial intermediates in pesticide manufacturing, was explored through a substitution reaction between O-methyl-N-nitroisourea and methylamine within a novel continuous flow microreactor system, featuring Fourier transform infrared (FTIR) in-line analysis for real-time monitoring. In this paper, the reaction is investigated using two optimization methods: the contemporary machine learning-based Bayesian optimization and the traditional kinetic modeling. Remarkably, both strategies obtained a similar yield of approximately 83% under equivalent reaction parametersspecifically, an initial reactant concentration of 0.2 mol/L, a reaction temperature of 40 °C, a molar ratio of reactants at 5:1, and a residence time of 240 min. The Bayesian optimization method demonstrated a notable efficiency, achieving optimal conditions within a mere 20 experiments, in contrast to the kinetic modeling approach, which required a more laborious effort for model formulation and validation. However, kinetic modeling allows for a more comprehensive understanding of the reaction, and the two optimization methods fully demonstrate their respective strengths and weaknesses. This study not only highlights the potential of integrating advanced machine learning methods into chemical process optimization but also sets the stage for further exploration into efficient, data-driven approaches in chemical synthesis.

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“…15–22 However, more recent demonstrations have showcased the use of sophisticated operations, algorithms, and multifaceted objective functions. 12,23–33 To further expand this methodology in chemical synthesis research, similar approaches are required for reactions where batch operations are preferred.…”

Section: Introductionmentioning

confidence: 99%

Accelerating reaction optimization through data-rich experimentation and machine-assisted process development

McMullen,

Jurica

2024

React. Chem. Eng.

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Multi-objective Bayesian optimisation using q-noisy expected hypervolume improvement (qNEHVI) for the Schotten–Baumann reaction

Abstract: Multi-objective Bayesian optimisation allows for finding trade-off solutions of the Schotten–Baumann reaction in a continuous flow. The effect of mixing efficiency on the fast reaction results in the complexity of the reaction space.

Cited by 6 publications

References 43 publications

Optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective Bayesian optimization and kinetic modeling

Optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective Bayesian optimization and kinetic modeling

Continuous Flow Synthesis of N,O-Dimethyl-N′-nitroisourea Monitored by Inline Fourier Transform Infrared Spectroscopy: Bayesian Optimization and Kinetic Modeling

Accelerating reaction optimization through data-rich experimentation and machine-assisted process development

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