Process‐aware data‐driven modelling and model predictive control of bioreactor for the production of monoclonal antibodies
Bibliographic record
Abstract
Abstract This manuscript addresses the problem of controlling a bioreactor to maximize the production of a desired product while respecting the constraints imposed by the nature of the bio‐process. The approach is demonstrated by first building a data‐driven model and then formulating a model predictive controller (MPC) with the results illustrated by implementing a detailed monoclonal antibody production model (the test bed) created by Sartorius Inc. In particular, a recently developed data‐driven modelling approach using an adaptation of subspace identification techniques is utilized that enables the incorporation of known physical relationships in the data‐driven model development (constrained subspace model identification), making the data‐driven model process aware. The resultant controller implementation demonstrates a significant improvement in production compared to the existing proportional integral (PI) controller strategy used in the monoclonal antibody production. Simulation results also demonstrate the superiority of the process‐aware or constrained subspace MPC compared to traditional subspace MPC. Finally, the robustness of the controller design is illustrated via the implementation of a model developed using data from a test bed with a different set of parameters, thus showing the ability of the controller design to maintain good performance in the event of changes such as a different cell line or feed characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".