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Record W3196716206 · doi:10.3390/en14185666

Anaerobic Digestion of Blood from Slaughtered Livestock: A Review

2021· review· en· W3196716206 on OpenAlexaff
Tasnia Hassan Nazifa, Noori M. Cata Saady, Carlos Bazán, Sohrab Zendehboudi, Adnan Aftab, Talib M. Albayati

Bibliographic record

VenueEnergies · 2021
Typereview
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAnaerobic digestionDigestion (alchemy)Anaerobic exerciseRaw materialPulp and paper industryFood scienceBiogasBiotechnologyLivestockChemistryBiologyMethaneMicrobiologyWaste managementChromatographyPhysiologyEcologyEngineering

Abstract

fetched live from OpenAlex

Blood from livestock slaughtering imposes a high organic pollution load and risks. If it is discharged untreated to sewer systems, it increases the organic pollution load on wastewater treatment plants by 35–50%. This paper reviews blood anaerobic digestion. It analyzes the quantities, composition, methane potential reported, microbiology, biochemical pathways of blood protein degradation, environmental and health issues, and strategies suggested to manage them during livestock blood anaerobic digestion. Although challenging, anaerobic digestion of blood as a mono-substrate is possible if the culture-reactor system is controlled based on a complete characterization and understanding of the microbial community and its metabolic activities. Co-digestion of blood and other feedstock proceeds well if the mixtures are well designed. Generally, the specific methane yield from digesting blood alone ranges between zero and 0.45 m3 kg−1 protein, whereas for co-digesting blood and other substrates, the yield varies between 0.1 and 0.7 m3 kg−1 volatile solids. More research is required for microbiology and kinetics, the role of adsorbents, reactor configuration, and culture adaptation during anaerobic digestion of blood to better control the process.

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)0.000
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 score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.263
Teacher spread0.238 · 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

Citations43
Published2021
Admission routes1
Has abstractyes

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