Analyzing Power Consumption in a Coaxial Bioreactor Using Machine Learning Techniques with Computational Fluid Dynamics
Why this work is in the frame
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Bibliographic record
Abstract
Agitated bioreactors are the subject of many studies regarding their design and scale-up to enhance the productivity in various chemical and biochemical industries. In this regard, accurately predicting their power consumption is very important, because it influences the mass transfer rate and flow uniformity inside the bioreactor. A literature review revealed that no study has been conducted to investigate the performance of coaxial bioreactors in terms of their power consumption using a machine learning method. In this study, a computational fluid dynamics (CFD) model was developed and validated against experimental data. Subsequently, 500 simulations at different aeration rates (2–6 L/min), anchor impeller speeds (3.5–9.5 rpm), central impeller speeds (60–150 rpm), and rotating modes (co-rotating and counter-rotating) were conducted. The data from these simulations were utilized to train and test various machine learning models. Initially, the k-nearest neighbor (KNN) classification model was employed to categorize the coaxial bioreactors into different rotating modes. It was found that with just the torque value and central impeller speed, the model achieved successful classification. In addition, various regression models, including multi-layer perceptron (MLP), KNN, and random forest, were developed to predict the torque that would be produced by the coaxial bioreactor. For all models, the hyperparameter tuning and cross-validations were performed. The mean squared error (MSE) evaluation showed that the random forest model had superior performance compared to its counterparts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it