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Digital Microfluidics for Automated Proteomic Processing
Published on: November 6, 2009
Implementing a Digital Transformation in Process Chemistry: Integrated Automation, Machine Learning, and Real-Time
Elena Braconi1, Jean-Philippe Krieger1, Thomas Vent-Schmidt1
1Syngenta (Switzerland).
Abstract:
Implementing a digital transformation in the Process Research and Development (PR&D) phase of an active ingredient offers significant opportunities to accelerate the journey from laboratory to manufacturing scale. Here, we report on three distinct initiatives undertaken at Syngenta to address concrete bottlenecks at different PR&D stages. First, Bayesian optimisation enabled efficient navigation of large reaction spaces with minimal experimental effort. Second, laboratory automation combined with multilinear calibration reduced hands-on time by ~85% and laid the foundation for autonomous closed-loop optimisation. Third, advances in Process Analytical Technology (PAT), including improved Multivariate Curve Resolution algorithms and modular Python-based pipelines, enabled real-time reaction monitoring in challenging industrial settings.
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