Modeling of biogas generation in bioreactor landfills using neuro-fuzzy system
Bibliographic record
Abstract
Biogas generation in anaerobic bioreactor landfills is modeled using the neuro-fuzzy system. The implemented inference system was an adaptive neuro-fuzzy inference system (ANFIS). The fuzzy logic controller featured a Multi-Input-Single-Output (MISO) structure in which time, leachate recirculation, and sludge addition were set as the controlled input variables. Biogas generation was the only manipulated output variable. The experimental data used in the study were obtained from earlier publications that involved lab scale anaerobic bioreactors operated under different rates of leachate recirculation and sludge addition. The selected data sets were employed in training, verifying, and validating the neuro-fuzzy inference system. The model simulated the actual experimental data quite successfully; however, some differences occurred in the validation process. The model achieved acceptable statistical measures which attested its potentials in predicting biogas generation in bioreactor landfills.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".