Tim Forster
Tim Forster
Student / Programme Doctorate at D-CHAB
ETH Zürich
Additional information
Research area
We focus on optimization- and hybrid modelling techniques that can be applied for the development and/or optimization of production processes of chemicals and biologics. For this, we combine mechanistic modelling approaches with optimization- and machine learning algorithms. In more recent works, we focus on surrogate modeling of processes combined with surrogate-based optimization methods to identify optimal process conditions in case of complex and computationally expensive systems.
Key topics:
- Surrogate/hybrid modelling and simulation
- Optimization and machine learning
Work Experience
Apr 2018 - Apr 2020 Production Chemist Bioconjucation
Protein Conjugation Plant
Lonza AG Pharma & Biotech Visp, Switzerland
Jul 2017 - Dec 2017 Research Assistant
Group of Prof. Dr. M. Morbidelli
Department of Chemistry and Applied Biosciences
ETH Zurich, Switzerland
Jan 2015 - Feb2015 Research Assistant
Group of Prof. Dr. A. D. Schlüter
Department of Materials
ETH Zurich, Switzerland
Education
Jun 2020 - Ongoing Doctoral Degree
Group of Prof. Dr. G. Guillén Gosálbez
Department of Chemistry and Applied Biosciences
ETH Zurich, Switzerland
Sep 2016 - Feb 2018 Master of Science in Chemical and Bioengineering
Department of Chemistry and Applied Biosciences
ETH Zurich, Switzerland
Sep 2013 - Sep 2016 Bachelor of Science in Chemical Engineering
Department of Chemistry and Applied Biosciences
ETH Zurich, Switzerland
Publications
T. Forster, D. Vázquez, I. Fons Moreno-Palancas, G. Guillén-Gosálbez. Algebraic surrogate-based flexibility analysis of process units with complicating process constraints. Computers & Chemical Engineering. 2024. Linkcall_made
T. Forster, D. Vázquez, M.N.Cruz-Bournazou, A. Butté, G. Guillén-Gosálbez. Modeling of bioprocesses via MINLP-based symbolic regression of S-system formalisms. Computers & Chemical Engineering. 2023. Linkcall_made
T. Forster, D. Vázquez, G. Guillén‐Gosálbez. Algebraic surrogate‐based process optimization using Bayesian symbolic learning. AIChE Journal. 2023. Linkcall_made
T. Forster, D. Vázquez, G. Guillén-Gosálbez. Global optimization of symbolic surrogate process models based on Bayesian learning. Computer Aided Chemical Engineering. 2023. Linkcall_made