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

• Published in: Industrial & engineering chemistry research 2024-05, Vol.63 (23), p.10162-10171
• Main Authors: Guo, Jiapeng, Luo, Guihua, Chai, Kejie, Su, Weike, Su, An
• Format: Article
• Language: English
• Subjects: Kinetics, Catalysis, and Reaction Engineering
• ISSN: 0888-5885; 1520-5045
• DOI: 10.1021/acs.iecr.4c01003

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.