Modeling and optimization of bioproduct formation with purple phototrophic bacteria using machine learning
Bibliographic record
Abstract
Municipal and industrial wastewater, along with organic waste, can be transformed into valuable bioproducts using purple phototrophic bacteria. This study compares the performance of three machine learning models (Random Forest, XGBoost, and CatBoost) in predicting and optimizing the formation of key bioproducts: polyhydroxybutyrate, polyhydroxyvalerate, 5-aminolevulinic acid, coenzyme Q10, carotenoids, bacteriochlorophylls, and biomass. The models were trained on a dataset compiled from previous studies, using input variables such as reaction time, concentration of organic matter, ethanol, bicarbonate, levulinic acid, ferric citrate, mineral medium, and N, C/N ratio, illumination conditions (continuous or intermittent), operation mode (batch or semicontinuous), and volume exchange percentage. Bayesian optimization was applied to train and tune the models. Performance was assessed using R 2 , Pearson correlation, RMSE, and MAPE. CatBoost outperformed the others, showing higher predictive correlation and lower error. It was subsequently used for further optimization. Feature importance analysis identified reaction time, mineral medium concentration, and volume exchange percentage as key drivers of bioproduct synthesis. The Particle Swarm Optimization algorithm was applied to determine optimal conditions for each target compound. Under the conditions studied, predicted maximum yields were: 569 mg polyhydroxybutyrate/L, 45 mg polyhydroxyvalerate/L, 79 µmol 5-aminolevulinic acid/L, 13 mg coenzyme Q10/g dw, 7 mg carotenoids/g dw, 17 mg bacteriochlorophylls/g dw, and 2040 mg biomass/L. Optimization suggests that operating as a sequencing batch reactor and employing discontinuous illumination for most targets, along with a reduced mineral medium concentration, is beneficial. Results highlight that each bioproduct requires distinct operational settings, supporting the idea of clustering target compounds.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".