Bibliographic record
Abstract
The extraction and purification technologies of landfill gas (LFG) from municipal waste continue to generate strong environmental concerns. Historically, the focus of these concerns was an odour in the immediate region of the landfill and the risk of explosions in structures caused by the movement of LFG through soil. While these are still important environmental issues, health risks associated with volatile organic compounds in LFG and damage to the atmosphere through the emission of greenhouse and ozone depleting gases, have also become prominent issues. The primary objective of this project is to study and examine LFG generation, extraction and purification technologies. Composition of LFG and gas extraction processes is analyzed. Comprehensive literature review of different models for LFG generation rate is provided. The study of LFG extraction and collection systems including design considerations and gas capture schemes are examined. Complete analysis of current purification processes of LFG along with upgrading techniques of methane to bio-methane are carried out. Discussion and recommendation on gas purification methods are conducted relevantly with certain type of LFG composition, level treatment required, quality of LFG anticipated, and its final application. Accurate portrayals of prior and current LFG extraction and purification technologies will advance the knowledge used to select appropriate waste management and reduction strategies in future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".