Investigation and Fuzzy Regime for Biogas Transport in Hydrophobic Permeable Polymer
Bibliographic record
Abstract
This research aims to control methane and carbon dioxide by a new intelligent approach in landfills. This intelligent approach entails a new configuration of the gas collection system, new permeable hydrophobic polymer medium for gas collection, and an intelligent fuzzy system for modeling and controlling gas transport in the system. The new configuration of the gas collection system consisted of permeable hydrophobic polymer material for better collection. For the investigation of gas transport within polymer, diffusion and convection flow tests were conducted. For gas diffusion through the system, a simulation was done using a box filled with polymer material with multi-ports for gas entrance and sampling. Sensors and a data acquisition system collected information about gas concentration with time and distance through the polymer. For gas convection through the system, a bottle filled with polymer medium was used. An airflow, manometer and flowmeter were used to characterize gas convection and medium permeability. Information collected about temperature variations were recorded to obtain their effect on gas convective flow, and gas diffusive flux through the permeable polymer medium. The fuzzy logic used obtained information to model and control the biogas transport in the new system to wider range conditions
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".