Modelling of Diffusion and Reaction of Carbon Dioxide and Nutrients in Biofilm for Optimal Design and Operation of Emerging Membrane Carbonated Microalgal Biofilm Photobioreactors
Bibliographic record
Abstract
The biological performance and carbon dioxide (CO2) flux of the novel and emerging concept of a membrane carbonated microalgal biofilm photobioreactor (MC-MBPBR) for wastewater treatment were investigated using mathematical modelling in conjunction with the finite-difference method. A set of differential equations was established to model the performance of an MC-MBPBR. The impacts of CO2 partial pressure, wastewater characteristics, and biofilm thickness on the concentration profiles and fluxes of CO2 and nutrients (N and P) to the biofilm of the MC-MBPBR were systematically studied. The modelling results showed profound impacts of these parameters on process efficiency (CO2 transfer and N and P removals) and the existence of an optimal biofilm thickness for maximum CO2, N, and P fluxes into the biofilm. Penetration of CO2 through the biofilm into the bulk water phase might occur under certain conditions. An increase in gaseous CO2 and increased influent N and P concentrations led to higher CO2, N, and P fluxes. The optimal biofilm thickness varied with the change in wastewater characteristics and gaseous CO2 concentration. The modelling results were in relatively good agreement with experimental results from the literature. The proposed mathematical models can be used as a powerful tool to optimize the design and operation of the novel MC-MBPBR for wastewater treatment and microalgae cultivation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".