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Record W2901954229 · doi:10.5276/jswtm.2018.248

Biogas Generation Potential of Anaerobic Co-Digestion of Municipal Solid Wastes and Livestock Manures

2018· article· en· W2901954229 on OpenAlexaff
Fabíole Jordana Los Barbosa, Alexandre R. Cabral, Marlon André Capanema, Waldir Nagel Schirmer

Bibliographic record

VenueThe Journal of Solid Waste Technology and Management · 2018
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBiogasAnaerobic digestionWaste managementMunicipal solid wasteEnvironmental scienceLivestockAnimal wasteMethaneChemistryEngineeringForestryGeography

Abstract

fetched live from OpenAlex

Abstract: This study evaluated the potential of biogas generation of the fresh organic fraction of municipal solid wastes (OFMSW) inoculated with swine manure and cattle manure, based on the volatile solids content obtained from biochemical methane potential (BMP) tests. Several physico-chemical parameters (e.g. volatile solids, pH and chemical oxygen demand) were assessed in the laboratory before and after 50 days of incubation of several samples of OFMSW, swine manure and cattle manure, and mixtures thereof. During incubation, reductions in percentage of volatile solids were relatively low (from 5.5% to 11.4%), indicating the existence of substrates that can degrade after the 50-day digestion period. Among the physico-chemical parameters evaluated, pH was a limiting parameter for anaerobic digestion of OFMSW and manures. The mixture showing the best performance in terms of volume of biogas generated contained 1 gvs OFMSW: 1 gvs swine manure, which led to the production of 60.4 mL.gVS-1 or 22 mL.gOFMSW-1 biogas. The values of CH4 concentration increased throughout the incubation period, and the CH4 concentration value peaked at 80% for the mixture 1 gvs OFMSW:1 gvs swine manure. The results obtained indicate the OFMSW and manures can be effectively used for power generation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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