Neural ordinary differential equation‐based model predictive controller for regulating glucose concentration in a fed‐batch <scp>CHO</scp> cell bioreactor
Bibliographic record
Abstract
Abstract Chinese hamster ovary (CHO) cells are widely used in the biopharmaceutical industry to produce recombinant proteins. Effective process control is crucial for managing biomanufactured product production in response to increasing market demand. Model predictive control (MPC) is an advanced controller compared to the traditional proportional integral derivative (PID) controller for handling complex nonlinear systems. However, existing MPC controllers fail to address challenges related to control accuracy, model plant mismatch (MPM), and computational load simultaneously. Neural ordinary differential equation (ODE), capable of effectively modelling dynamics within complex systems at high computational efficiency, has the potential to tackle these limitations. This study developed a neural ODE model‐based MPC to dynamically maintain glucose concentration in a fed‐batch CHO cell bioreactor simulation system. Additionally, benchmark studies were conducted to compare the control performance of neural ODE‐MPC with neural network (NN)‐based MPC and long short‐term memory (LSTM)‐based MPC. The results demonstrate that neural ODE‐MPC can provide reliable control performance in managing glucose concentration with lower control errors, small MPM, and higher computational efficiency compared to the benchmark systems. In conclusion, neural ODE‐MPC has the potential to address MPC challenges and enhance production efficiency in future industrial applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".