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The scale up of chemical processes directly from lab experiments to manufacturing scale holds inherent risks for process safety, product quality and process performance. Whereas we as chemists understand the influence of a chemical transformation very well, we are lacking reliable predictive tools to describe the influence from the chosen equipment on our manufacturing process. This is especially the case, when the process is sensible to mass transfer, heat transfer, stirring/mixing effects or phase changes.
The use of lab generated data and digital twins in predictive computer simulations is a first step to make processes more robust against scale and equipment changes, but has some restrictions.
Hence we developed our "Siegfried Toolboxes" for common chemical unit operations which combine smart experimental setups in dedicated scale down reactors, data acquisition used in computational programs and data driven knowledge of the performance of the manufacturing equipment. This enables us to make predictions about the performance of a chemical or physical manufacturing process in dedicated equipment to define the optimal process parameters on scale.
Michael Levis
Head Process Technologies, Siegfried
Michael Levis has been a member of Siegfried since 1995, bringing over 25 years of expertise in process optimization, particle technologies, and R&D leadership. He began as Manager of Analytical Development and steadily advanced to Head of Process Optimization, adding responsibility for Process Safety. From 2010 to 2018, Michael served as a Principal Scientist for Particle Technologies. Currently, he leads Process Technology R&D, overseeing the Process Safety Lab while continuing his role as Principal Scientist for Particles. Michael holds a Diploma in Chemistry from the Albert Ludwigs University Freiburg and a PhD from the University of Freiburg, Germany.
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