Comparison of pretreatment methods that enhance biomethane production from crop residues - a systematic review
Bibliographic record
Abstract
A systematic literature review was conducted to compare the efficacy of biological, chemical, physical, and combined pretreatments in enhancing biomethane production from crop residues (CR). Three electronic databases viz., Science Direct, EBSCOhost, and PubMed were used to identify the studies in literature. The pretreatment methods were compared in terms of their advantages and disadvantages with reference to techno-economic aspects. The techno-economic aspects considered included rate of hydrolysis, energy use, effectiveness, cost, and formation of toxic compounds. A total of 3167 studies, covering the period 2014 - 2018, were screened for relevance to the study. Forty-four records (n=44) consisting of 36 research papers (n=36) and eight narrative reviews (n=8) met the inclusion criteria. The results show that physical and chemical methods are the most effective and fastest. These methods have limited utility due to high cost of resources, operation, and energy as well as formation of inhibitory by-products. Despite generation of toxic compounds, combined methods are regarded as fast and costeffective. Biological method is inexpensive, eco-friendly, and low energy-consuming. However, it is a nascent technology that is still developing. A combination of trends in research and development provide the best pretreatment alternative to improve the biomethane production from CR.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".