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Assessment of separation energy for bio-based chemicals in the IDAES framework
Bio-based chemicals are one promising carbon source for the future chemical industry. However, it is hard to identify the best candidate due to many options. Tools to quickly screen flowsheets could help. In this thesis, you will develop a tool that allows for the fast screening of flowsheets.
Keywords: Process design
Bio-based chemicals
Chemical engineering
Process optimization
Bio-based chemicals are renewable carbon sources for future chemical-industry, and fermentation processes seem especially suitable for producing bio-based chemicals. For production via fermentation, many different chemicals are being discussed as promising candidates for future bio-based platform chemicals. However, the product of a fermentation reactor is often highly diluted in water, and thus, significant energy effort is required for separation. Thus determining the energy demand of separation is crucial in order to identify the best molecule.
However, combining the many possible bio-based chemicals with the many possible separation sequences makes a full-scale process design for each separation and candidate option infeasible/challenging. To overcome this challenge, the EPSE group has developed a framework that combines quantum chemical property prediction with thermodynamically consistent shortcut process models, allowing an efficient evaluation of thousands of process configurations and molecules/solvent candidates.
So far, the EPSE group's framework requires expert knowledge to expand the process flowsheet, preventing a straightforward development of process flowsheets and the framework's broader application. Thus, we want to migrate our methods into the IDAES framework , a flowsheeting framework in Python developed by a consortium of US National Labs. However, the integration of our framework and the IDEAS framework has still to be tested.
Bio-based chemicals are renewable carbon sources for future chemical-industry, and fermentation processes seem especially suitable for producing bio-based chemicals. For production via fermentation, many different chemicals are being discussed as promising candidates for future bio-based platform chemicals. However, the product of a fermentation reactor is often highly diluted in water, and thus, significant energy effort is required for separation. Thus determining the energy demand of separation is crucial in order to identify the best molecule. However, combining the many possible bio-based chemicals with the many possible separation sequences makes a full-scale process design for each separation and candidate option infeasible/challenging. To overcome this challenge, the EPSE group has developed a framework that combines quantum chemical property prediction with thermodynamically consistent shortcut process models, allowing an efficient evaluation of thousands of process configurations and molecules/solvent candidates. So far, the EPSE group's framework requires expert knowledge to expand the process flowsheet, preventing a straightforward development of process flowsheets and the framework's broader application. Thus, we want to migrate our methods into the IDAES framework , a flowsheeting framework in Python developed by a consortium of US National Labs. However, the integration of our framework and the IDEAS framework has still to be tested.
Your task will be to implement the separation of multiple bio-based chemicals from water into the IDAES framework. For this purpose, you have to build an interface between our existing property prediction methods and the IDAES framework. Furthermore, you will look into the IDEAS framework's capability to include black-box models and try to integrate the shortcut process models currently used in the EPSE framework into the IDAES framework. Finally, you will use the modified IDEAS framework to evaluate a larger number of bio-based chemicals based on their separation energy demand.
What you need
• Good understanding of thermodynamics
• Above-average grades
• Programing experience: Python, C (optional)
What do we offer
In this master thesis, you have the opportunity to learn about the separation challenges involved in the production of bio-based chemicals and predictive thermodynamics, get familiar with a new framework for process design, and gather experience with different types of optimization.
If you are interested in working in a new and growing dynamic team on process design, please get in touch with us.
Your task will be to implement the separation of multiple bio-based chemicals from water into the IDAES framework. For this purpose, you have to build an interface between our existing property prediction methods and the IDAES framework. Furthermore, you will look into the IDEAS framework's capability to include black-box models and try to integrate the shortcut process models currently used in the EPSE framework into the IDAES framework. Finally, you will use the modified IDEAS framework to evaluate a larger number of bio-based chemicals based on their separation energy demand. What you need • Good understanding of thermodynamics • Above-average grades • Programing experience: Python, C (optional) What do we offer In this master thesis, you have the opportunity to learn about the separation challenges involved in the production of bio-based chemicals and predictive thermodynamics, get familiar with a new framework for process design, and gather experience with different types of optimization. If you are interested in working in a new and growing dynamic team on process design, please get in touch with us.