Automated kinetic model identification via cloud services using model-based design of experiments

Abstract

Industry 4.0 has birthed a new era for the chemical manufacturing sector, transforming reactor design and integrating digital twin into process control. To bridge the gap between autonomous chemistry development, on-demand manufacturing and real-time optimization, we developed a cloud-based platform driven by model-based design of experiment (MBDoE) algorithms integrated in a simulation software for model identification (SimBot) to remotely coordinate a smart flow reactor, also known as the LabBot, sited in a different location. With real-time data and setpoints synchronization, MBDoE was able to identify kinetic models using a limited number of experimental runs. Within this platform, two pharmaceutically relevant syntheses were investigated as case studies: amide formation and nucleophilic aromatic substitution. A new kinetic model providing statistically adequate data description within the whole investigated experimental design space was identified for the amide formation reaction. The model for the nucleophilic aromatic substitution with a well-known but complex mechanism was accurately identified ensuring a statistically precise estimation of kinetic parameters.