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Record W4406860399 · doi:10.4271/2024-01-5257

Processing and Characteristics Study of Hydrogen from Sewage and Waste Municipal Water via Gasification Process

2025· article· en· W4406860399 on OpenAlexaff
Melvin Victor De Poures, R. Venkatesh, N. Karthikeyan, S. Manivannan, Mannan Boopathi, Balakrishnan Baranitharan, S. Madhu, Gopal Kaliyaperumal, V. Sakthi Murugan

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsWaste managementProcess (computing)Environmental scienceHydrogenWastewaterMunicipal solid wasteProcess engineeringComputer scienceEngineeringChemistryOperating system

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">In recent trends, renewable energy has gained significance in worldwide applications due to avail from nature, low cost, and pollution-free. Based on the world population, a large volume of municipal and sewage water waste affects the environmental water sources, resulting in pollution. To save the earth and maintain a green environment, the present investigation aims to produce bio-hydrogen from municipal and sewage waste through a gasification process with a pyrolysis reactor. The temperature and time of the gasification process were varied by 600-900°C and 60 min. The impact of gasification temperature (600-900°C) and 60 min on molar fraction, gas yield, and gasification efficiency behaviour has to be investigated, and higher temperature (900°) with 60 min gasification process showed the superior molar fraction with 18.4 mol/kg hydrogen yield and improved gasification efficiency of 72%. The gained bio-hydrogen suggested energy storage applications.</div></div>

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.013
GPT teacher head0.267
Teacher spread0.253 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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