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Anaerobic Digestion of Tannery Solid Waste for Biogas Production: The case of Modjo Tannery, Modjo; Ethiopia

2016· article· en· W2480213655 on OpenAlexvenueno aff
Dejene Tsegaye, Mekibib Dawit, Khwairakpam Gajananda

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2016
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
FundersStyrelsen för Internationellt Utvecklingssamarbete
KeywordsBiogasAnaerobic digestionBiogas productionRaw materialBioenergyTotal dissolved solidsCow dungMunicipal solid wasteChemistryCarbon-to-nitrogen ratioAnimal scienceMethaneVolume (thermodynamics)Pulp and paper industryEnvironmental scienceWaste managementNitrogenBiofuelEnvironmental engineeringBiologyFertilizerEngineering

Abstract

fetched live from OpenAlex

The present study characterized the physical property, total solids (TS), volatile solids (VS) and Carbon to Nitrogen ratio (C/N ratio) of tannery solid waste (TSW). Five different combinations with or without cow dung (CD) were assessed for their biogas production suitability in triplicate batch digesters (D-1, D-2, D-3, D-4, and D-5) with a total volume of 2.8L. The results showed that TS, VS and C/N ratio of wastes were 56.37%, 76.34% and 29.05%, respectively. The results also suggested that the highest volume of biogas (4,756 ml) with a methane content of 60.37% was produced by the digester containing 75% TSW and 25% CD and the lowest biogas (2,539 ml) with quality of 68.06% was produced by digester containing 100% CD. The average methane contents of different digesters were D-1 (100% TSW) 53.23%, D-2 (75% TSW: 25% CD) 60.37%, D-3 (50% TSW: 50% CD) 58.78%, D-4 (25% TSW: 75% CD) 57.66% and D-5 (100% CD) 67.31%. Total and volatile solid removal efficiency of all digesters was in the range of 42.27-76.34% and 47.16-79.23%. The study concluded that TSW is a good feedstock for biogas production by utilizing agro-industrial based organic solid waste for bioenergy production.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations4
Published2016
Admission routes1
Has abstractyes

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