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Record W3029051169 · doi:10.5539/mas.v14n6p90

Recent Advance in Anaerobic Co-digestion Technology: A Review

2020· review· en· W3029051169 on OpenAlexvenueno aff
Andriamahefasoa Rajaonison, Ives Abel Fetra Andriatsitohaina Rabesahala, Hery Tiana Rakotondramiarana

Bibliographic record

VenueModern Applied Science · 2020
Typereview
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
Fundersnot available
KeywordsAnaerobic digestionBiogasBiochemical engineeringEnvironmentally friendlyDigestion (alchemy)Environmental scienceBiofuelRenewable energyBiodegradationProcess engineeringProcess (computing)Renewable resourceWaste managementPulp and paper industryFossil fuelComputer scienceChemistryMethaneEngineeringBiologyEcology

Abstract

fetched live from OpenAlex

Due to their polluting characteristics, fossil fuels presently tend to be replaced by renewable energy resources. Anaerobic digestion is a recent technology for producing biofuels. As a biochemical method to produce biogas, it is more environmentally friendly than other processes and is almost non-polluting. The anaerobic co-digestion is an upgrade of the mono-digestion since some limitations of the single-substrate degradation were solved by digesting two or more substrates. The present review gives an overview of the progress made in the anaerobic co-digestion technology. Appearing as a complex technology, lots of factors can affect its operation. Those factors include the choice and the composition of the substrates, the ratio between the substrates and the inoculum, the pretreatment, the environmental conditions, the operational parameters, and the post-treatment at the end of the process. Analytical and empirical tools for the assessment of the whole system in terms of biodegradability and synergy of the substrates, the efficiency of the process, and the identification of the operation inhibitors are also presented in this paper.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.296
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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