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Mechanistic Mathematical Models as a Basis for Digital Twins.
André Moser1, Christian Appl1,2, Simone Brüning3
1Faculty of Medical and Life Sciences, Furtwangen University, Villingen-Schwenningen, Germany.
Advances in Biochemical Engineering/Biotechnology
|November 18, 2020
Summary
Digital Twins, detailed virtual models of bioprocesses, enhance biotechnology by enabling process optimization and control. Mathematical process models are key for accurate predictions and developing effective control strategies.
Area of Science:
- Biotechnology
- Process Engineering
- Computational Modeling
Background:
- Digital Twins offer a future-oriented approach for bioprocess development, optimization, and manufacturing.
- These virtual representations provide predictive capabilities crucial for monitoring and controlling bioprocesses.
- Mathematical process models are essential components of Digital Twins, ensuring high-fidelity predictions.
Purpose of the Study:
- To outline the requirements for process models used in Digital Twins and process optimization.
- To describe different types of models, including mechanistic and compartmentalized approaches.
- To explain the application of these models in Digital Twins and for process optimization.
Main Methods:
- Review of requirements for process models in Digital Twins and optimization.
- Description and comparison of mechanistic and compartmentalized modeling techniques.
- Highlighting a specific structured, compartmentalized model developed for optimization and Digital Twin applications.
Main Results:
- Mechanistic and compartmentalized models are suitable for Digital Twins and process control.
- Process models must meet specific criteria for effective use in optimization and Digital Twins.
- A structured, compartmentalized model has been successfully implemented in Digital Twins for optimization.
Conclusions:
- Mathematical process models are fundamental to the success of Digital Twins in biotechnology.
- The selection and design of process models directly impact the efficacy of optimization and control strategies.
- Structured, compartmentalized models represent a promising approach for advanced bioprocess management using Digital Twins.
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