Computational fluid dynamic (CFD) modelling in anaerobic digestion: General application and recent advances
Bibliographic record
Abstract
Nowadays, thanks to the enhancement in computational power and software development, advanced mathematical modelling based on computational fluid dynamics (CFD) allows us to represent almost any system. Anaerobic bioreactors correspond to a complex biosystem where multiple reactions, in parallel and/or in series, take place. Besides this biological complexity, the actual digester operation increases the system's complexity given the number of transport phenomena that also occur; therefore, few real applications may be found in which mathematical models are properly harnessed. This review presents a general assessment of the CFD applications that have been applied in anaerobic digestion processes starting with the model set-up up to the post-processing of the results. In regards to the model pre-processing and setup, the generation and evaluation of the mesh and the model specifications such as multiphase flow, turbulence regimen, rheology characterization and impeller motion, are addressed. Due to the importance of the evaluation of the model's outcome, topics such as error assessment and origin, model calibration and validation are discussed. The current challenges and future perspectives of this type of mathematical modeling are also addressed, with particular emphasis on the integration of biological reactions with the conventional fluid dynamic modeling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".