Kinetics model with experimental validation for optimal microalgae generation in double-skin façades
Bibliographic record
Abstract
Microalgae photobioreactors integrated into double-skin façades in cold climates enhance growth conditions for biomass generation, CO 2 fixation, and O 2 production, while reducing thermal loads. However, shading imposed by double-skin façade's assembly limits light interception for the photobioreactors. This study employs kinetics and shading models to estimate variations in biomass concentration within the light-constrained photobioreactor. To validate the accuracy and reliability of the model, the results were compared to experimental data collected by the authors from a full-scale constructed system. Biomass concentration was monitored using a developed RGB image-based monitoring technique. Direct search methods were employed to optimize the design and operational variables of the microalgae photobioreactors, with the goal of maximizing annual areal and volumetric productivities across 14 Canadian cities under various climatic conditions. Hooke-Jeeves algorithm was 65.6 % faster than Powell's conjugate direction method. This showed the system's adaptability by generating 1.3–3.0 kg/m 2 .year of biomass in different climates. The study established a framework for optimizing microalgae photobioreactors into façades to maximize biomass harvest. • Kinetics models for microalgae photobioreactors integrated into buildings. • Microalgae photobioreactors are adaptable to cold climatic regions when optimized. • Hooke-Jeeves algorithm is faster compared to Powell's conjugate direction method. • Location has a greater impact on biomass productivity than the microalgae strain.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".