Industrial Cell Culture Seed Train Data for Dynamic Parameter Estimation

A dataset from Ostwestfalen-Lippe University of Applied Sciences and Arts supports research on dynamic parameter estimation for bioprocess modeling. The data likely contains measurements from an industrial cell culture seed train across three consecutive cultivation scales from 40 to 2,160 liters. It was used to compare a weighted least squares estimation with a moving horizon estimation technique that incorporates prior knowledge.

Use Cases

• Comparing parameter estimation techniques like weighted least squares and moving horizon estimation based on described model performance.
• Developing workflows for dynamic parameter re-estimation in bioprocesses based on the described methodology.
• Training models to predict future process states for operating decisions based on the described use of bioprocess modeling.
• Investigating the robustness of estimation methods to low sampling frequencies and measurement errors based on the described challenges.

Strengths

• Data spans three consecutive cultivation scales (40, 2,160 L), providing multi-scale insights.
• Description details a specific methodological comparison between two estimation techniques.
• The dataset is associated with a published academic contribution, suggesting a research context.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Ostwestfalen-Lippe University of Applied Sciences and Arts

Collection Method

Likely contains experimental data from an industrial cell culture seed train process.