Dynamic scheduling and control of a single column chromatography process for integrated continuous bioprocessing
Bibliographic record
Abstract
• Developed a feedforward rolling horizon control system for ICB. • Dynamic optimization formulation that includes discrete decisions. • MINLP manipulates chromatography stage decisions and loading flow rate. • Dynamic harvest case studies demonstrate the controller’s capabilities. Integrated continuous bioprocesses (ICBs) offer significant advantages for biotherapeutic manufacturing, including enhanced efficiency, reduced costs, and improved product accessibility. However, the adoption of ICBs is hindered by challenges in maintaining robust operation of the downstream processes amidst upstream variability. This study presents a mixed-integer nonlinear programming (MINLP) formulation for adaptive scheduling and control of a single-column bind-elute chromatography process that is integrated with a time-varying bioreactor harvest and a surge vessel. The control system leverages dynamic models, a rolling horizon implementation and a feedforward harvest forecast to optimize the chromatography loading flow rate and duration while ensuring compliance with critical process constraints. Case studies demonstrate the controller's ability to adapt the chromatography process and maintain robust operation under static and dynamic upstream harvest conditions. This framework represents a significant step toward the broader adoption of ICBs by utilizing the surge vessel as a degree of freedom to implement a variable loading flow rate strategy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".