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Control of parallelized bioreactors I: dynamic scheduling software for efficient bioprocess management in
Lukas Bromig1, Nikolas von den Eichen1, Dirk Weuster-Botz2
1Chair of Biochemical Engineering, Technical University of Munich, Boltzmannstraße 15, 85748, Garching, Germany.
Bioprocess and Biosystems Engineering
|October 18, 2022
Summary
Industrial biotechnology demands more automation. This study introduces scheduling software for liquid handling stations, enabling dynamic control of mL-scale bioreactors for improved bioprocess development and resource allocation.
Area of Science:
- Industrial Biotechnology
- Bioprocess Engineering
- Automation and Control Systems
Background:
- High-throughput technologies and automation are crucial in industrial biotechnology R&D.
- Miniaturization and parallelization in bioprocess development necessitate advanced automation and digital control.
- mL-scale parallel bioreactor systems require integration with liquid handling stations for tasks like substrate addition and sampling.
Purpose of the Study:
- To present a novel scheduling software for liquid handling stations (LHS).
- To enable full automation and improved process control for mL-scale parallel bioreactor systems.
- To overcome limitations of fixed sequential execution in LHS for precise bioprocess management.
Main Methods:
- Development of a scheduling software for dynamic task orchestration in LHS.
- Implementation of data-driven real-time prioritization and user-defined constraints for process control.
- Utilizing the SiLA 2 standard for integrating a commercial 48-parallel bioreactor system and sensor equipment.
Main Results:
- The proposed software allows dynamic scheduling, overcoming fixed sequential order limitations in LHS.
- Enables precise control over critical bioprocess operations like feeding and pH control.
- SiLA 2 standard simplifies integration efforts for bioreactor systems and sensor equipment.
Conclusions:
- The developed scheduling software enhances bioprocess control and resource allocation in automated systems.
- Dynamic scheduling provides a more robust and adaptable approach to managing parallel bioreactor experiments.
- The integration framework using SiLA 2 facilitates the adoption of advanced automation in bioprocess development.
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