The assessment of safety-related material properties plays a central role in chemical process development. Many of these properties, such as flash point, global warming potential or reactivity, can be quantified numerically, while others, such as toxicity or environmental hazards, are often only classified in stages (e.g. according to the Globally Harmonised System, GHS). The aim of this master's thesis is to develop a methodology for systematically quantifying and evaluating safety-relevant material properties. Modern simulation methods such as molecular dynamics (MD) will be used to generate data, which will be used together with existing literature data to train data-driven models. The models developed will then be applied to solvent-based azeotropic rectification to perform an automated solvent screening.
About Division 2.2 at BAM
In division 2.2, research focuses on the development of dynamic process models to describe all system states from start-up to shutdown, supported by an open library of dynamic, pressure-driven models. The division uses machine learning, uncertainty quantification and hybrid modeling methods to drive forward real-time application and plant monitoring. For the comprehensive digitalization of process engineering, the division is also researching information and data modelling as well as process models from engineering to the operation of chemical plants. This is supplemented by the development of methods for the safe and optimal transformation of chemical plants.
Tasks:
• Performing molecular dynamics simulations to determine safety-related substance parameters and validating the results using available literature data
• Developing a concept for the numerical assessment of toxicity and flammability, as well as training data-based models.
• Creating a model and performing solvent optimization for azeotropic rectification
Desirable skills:
• Experience with Python, Matlab or comparable programming languages
• Knowledge of process modeling, thermodynamics and safety engineering
• Knowledge of data driven modeling methods
Contact persons:
Lukas Scheffold, M.Sc.
lukas.scheffold@bam.de
Maria Fernanda Gutierrez Sanchez, Dr.
maria.gutierrez@bam.de