Methods for determination of biomethane potential of feedstocks: a review
Bibliographic record
Abstract
Biogas produced during anaerobic digestion (AD) of biodegradable organic materials. AD is a series of biochemical reactions in which microorganisms degrade organic matter under anaerobic conditions. There are many biomass resources that can be degraded by AD to produce biogas. Biogas consists of methane, carbon dioxide, and trace amounts of other gases. The gamut of feedstocks used in AD includes animal manure, municipal solid waste, sewage sludge, and various crops. Several factors affect the potential of feedstocks for biomethane production. The factors include nutrient content, total and volatile solids (VS) content, chemical and biological oxygen demand, carbon/nitrogen ratio, and presence of inhibitory substances. The biochemical methane potential (BMP), often defined as the maximum volume of methane produced per g of VS substrate provides an indication of the biodegradability of a substrate and its potential to produce methane via AD. The BMP test is a method of establishing a baseline for performance of AD. BMP data are useful for designing AD parameters in order to optimise methane production. Several methods which include experimental and theoretical methods can be used to determine BMP. The objective of this paper is to review several methods with a special focus on their advantages and disadvantages. The review shows that experimental methods, mainly the BMP test are widely used. The BMP test is credited for its reliability and validity. There are variants of BMP assays as well. Theoretical models are alternative methods to estimate BMP. They are credited for being fast and easy to use. Spectroscopy has emerged as a new experimental tool to determine BMP. Each method has its own advantages and disadvantages with reference to efficacy, time, and ease of use. Choosing a method to use depends on various exigencies. More work needs to be continuously done in order to improve the various methods used to determine BMP.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".