A cloud-enabled digital twin platform for upstream processing in biotechnology: Integrating CFD, compartmentalization, and reaction kinetics
Year: 2025
Type: article
Abstract: Biotechnology process modeling has become an essential tool in both research and industrial settings, offering a cost-effective and efficient alternative to extensive physical experimentation. It enables the simulation and analysis of complex biological systems, supporting faster development cycles and better decision-making. To foster collaboration and knowledge sharing in this domain, we present an open source cloud-enabled platform for upstream biotechnology process modeling. The platform provides an integrated environment where users can combine computational fluid dynamics (CFD), compartmental models, and kinetic simulations within a unified and modular interface. Each component of the model operates independently, but can be seamlessly coupled through a standardized API. The system is designed to support both research and educational use cases, with an emphasis on accessibility, extensibility, and reproducibility. This paper outlines the platform architecture and implementation, highlights the technical challenges addressed in model integration, and discusses opportunities for future development. By lowering technical barriers and encouraging community-driven innovation, the platform aims to advance digital twin applications in biotechnology upstream processing.
Source: Computers & Chemical Engineering
Authors: Xiyan Li, Sebastian L. Jensen, Noah B. Christiansen, Elham Ramin, Johan le Nepvou de Carfort, Johannes Schmölder, Eric von Lieres, Krist V. Gernaey
Institutions: Technical University of Denmark, Forschungszentrum Jülich
Language: English
Topic: Fluid Dynamics and Mixing
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,050
Award: G3656982092
OpenAlex ID: W7108479271