A Study of Theoretical Analysis and Modelling of Microalgal Membrane Photobioreactors for Microalgal Biomass Production and Nutrient Removal
Bibliographic record
Abstract
This study presents a theoretical and mathematical analysis and modelling of the emerging microalgal membrane photobioreactors (M-MPBRs) for wastewater treatment. A set of mathematical models was developed to predict the biological performances of M-MPBRs. The model takes into account the effects of hydraulic retention time (HRT), solid retention time (SRT), and the N/P ratio of influent on the biological performance of M-MPBRs, such as microalgal biomass production and nutrient (N and P) removals. The model was calibrated and validated using experimental data from the literature. This modelling study explained that prolonged SRT could promote biomass production and nutrient removal, while prolonging HRT exhibited a negative effect. Furthermore, biomass production could be improved by augmenting nutrient loading, and nutrient removal would be limited under insufficient conditions. The modelling results demonstrated that the best performance was achieved at HRT = 1 d and SRT = 40 d for typical municipal wastewater with an influent N concentration = 40 mg/L. The modelling results are in good agreement with the experimental results from the literature. The findings suggest that the proposed models can be used as a powerful mathematical tool to optimize these parameters to improve the removal of nutrients (N and P), as well as the productivity of biomass in M-MPBRs. This study provides new insights into the use of mathematical models for the optimal design and operation of the emerging M-MPBRs for sustainable wastewater treatment.
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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 | 0.001 |
| 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 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".