Kinetic model, recycling, regeneration, and reusing of tri-phase catalytic nucleophilic substitution esterification

Hsu, Hao-Lin; Yang, Chih-Chiang; Chiu, Wei-Cheng; Hou, Shuhn-Shyurng; Lin, Chun-Yu; Lin, Chong

doi:10.1016/j.mcat.2022.112657

Cited by 2 publications

(2 citation statements)

References 56 publications

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“…Their work successfully predicted the optimal conditions for effectively scaling up this reaction. 22 While kinetic modeling has been applied to nucleophilic substitution reactions involving aromatic compounds [23][24][25] , its application to reactions concerning aliphatic compounds remains relatively underexplored.…”

Section: Introductionmentioning

confidence: 99%

Continuous flow synthesis of N,O-dimethyl-N'-nitroisourea monitored by inline FTIR: comparing machine learning and kinetic modeling for optimization

Guo,

Luo,

Chai

et al. 2024

Preprint

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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 FTIR inline analysis for real-time monitoring. This study embarked on a comparative analysis between two optimization approaches: 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 minutes. 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. Despite the long-standing reliance on kinetic modeling for its detailed insights into reaction dynamics, our findings suggest its relative inefficiency in optimization tasks compared to the machine learning-based alternative. 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.

show abstract

Section: Introductionmentioning

confidence: 99%

Continuous flow synthesis of N,O-dimethyl-N'-nitroisourea monitored by inline FTIR: comparing machine learning and kinetic modeling for optimization

Guo,

Luo,

Chai

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Their work successfully predicted the optimal conditions for effectively scaling up this reaction . While kinetic modeling has been applied to nucleophilic substitution reactions involving aromatic compounds, − its application to reactions concerning aliphatic compounds remains relatively underexplored.…”

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.

View full text Add to dashboard Cite

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.

show abstract

Kinetic model, recycling, regeneration, and reusing of tri-phase catalytic nucleophilic substitution esterification

Cited by 2 publications

References 56 publications

Continuous flow synthesis of N,O-dimethyl-N'-nitroisourea monitored by inline FTIR: comparing machine learning and kinetic modeling for optimization

Continuous flow synthesis of N,O-dimethyl-N'-nitroisourea monitored by inline FTIR: comparing machine learning and kinetic modeling for optimization

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

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