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Record W2409214792 · doi:10.5539/eer.v6n1p44

Analyses of Anaerobic Batch Digestion of Municipal Solid Waste in the Production of Biogas Using Mathematical Models

2016· article· en· W2409214792 on OpenAlexvenueno aff
A. H. Igoni

Bibliographic record

VenueEnergy and Environment Research · 2016
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
Fundersnot available
KeywordsBiogasAnaerobic digestionBiogas productionMunicipal solid wasteBioreactorEnvironmental sciencePulp and paper industryProcess engineeringWaste managementProcess (computing)Batch processingMethaneComputer scienceChemistryEngineering

Abstract

fetched live from OpenAlex

The process dynamics of anaerobic digestion of municipal solid waste (MSW) in a batch bioreactor for the production of biogas has been analysed. An anaerobic batch digester was designed for the treatment of MSW in Port Harcourt metropolis, Nigeria, while at the same time generate biogas as a useful by-product. In the course of the design, the biochemical behaviour of the MSW in batch processing was investigated and analysed. Mathematical models were developed to describe the behaviour of the waste using material balance analysis. The models were validated by the formulation of a Microsoft Visual Basic Version 6.0 programme to simulate the digestion process for a fractional conversion of 0.2-0.8 and Total solids (TS) concentration of 4-30%. The results were analysed using Microsoft Chart Editor and showed that the fractional conversion has various levels of effect on other process parameters like the mean cell residence time, substrate and microbial concentrations and the volume of biogas/methane produced.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.162

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.102
GPT teacher head0.326
Teacher spread0.224 · 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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