A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics
Dogancan Karan, Guoying Chen, Nicholas A. Jose, Jiaru Bai, Paul McDaid, Alexei A. Lapkin
Abstract
An automated flow chemistry platform was designed to collect data for a lithium-halogen exchange reaction. The data was used to train a Bayesian multi-objective optimization algorithm to optimize the process parameters and build process knowledge.
Topics & Concepts
Computer scienceBayesian optimizationProcess (computing)Flow chemistryProcess optimizationHalogenFlow (mathematics)Machine learningArtificial intelligenceChemistryContinuous flowBiochemical engineeringChemical engineeringEngineeringMathematicsProgramming languageOrganic chemistryAlkylGeometryInnovative Microfluidic and Catalytic Techniques InnovationAdvanced Control Systems OptimizationProcess Optimization and Integration