Methodology for fast development of digital solutions in integrated continuous downstream processing

Area of Science:

• Biotechnology
• Process Engineering
• Automation

Background:

• The biopharmaceutical industry is transitioning from traditional batch manufacturing to integrated continuous production.
• This shift aims to enhance efficiency, reduce footprint, and improve product quality through process intensification and automation.
• Complex continuous processes necessitate sophisticated digital solutions for monitoring and control.

Purpose of the Study:

• To present a platform for rapid development, advanced studies, and validation of digital solutions for integrated continuous downstream bioprocessing.
• To enable high levels of automation and remote operation for bioprocesses.
• To facilitate the integration of digital tools early in the process development lifecycle.

Main Methods:

• Development of a flexible, extendable real-time supervisory controller named Orbit, built in Python.
• Utilizing a network of Orbit controllers for synchronizing parallel operations in complex process systems.
• Implementing digital twin applications with computational extensions in Orbit for model-based monitoring and control.

Main Results:

• Orbit enables real-time communication and execution across diverse physical setups.
• The platform supports efficient data handling, storage, and analysis of heterogeneous, asynchronous data.
• Demonstrated novel digital solutions including automatic parameter optimization, Kalman filter monitoring, and model-based batch-to-batch control.

Conclusions:

• The presented platform accelerates the development and validation of digital solutions for continuous bioprocessing.
• It supports advanced automation, remote operation, and digital twin applications in complex downstream processes.
• This approach is crucial for realizing the full potential of integrated continuous manufacturing in the biopharmaceutical industry.