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
parametersspecifically, 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.