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Record W6929932533 · doi:10.5281/zenodo.11092574

DESIGN OF STOVE FUELED BY USED LUBRICATING OIL FOR INDUSTRIAL SALT DRYING

2024· other· en· W6929932533 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typeother
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsStovePelletSalt (chemistry)CombustorFuel oilLubricantKilnFuel efficiency

Abstract

fetched live from OpenAlex

Salt as a source of important minerals is much needed by society and industry. In the salt-making process, salt must go through a drying phase. Salt drying at the Badan Riset Nasional (BRIN)’s workshop that uses wood pellet as the fuel is considered not optimal because of waste in waiting and processing time. Other alternative fuels are needed to maximize the salt production process, one of which is using used lubricating oil. Utilizing used lubricating oil as fuel for the salt drying process requires the conversion of burner or stove. This research aims to design a stove fueled by used lubricant oil for drying industrial salt. French method is used to design the stove. The stove that has been built iwas compared with wood pellet stove in terms of waiting time to reach the desired salt drying temperature, fuel operational costs, and capacity of the salt produced. The ST-44 steel oil stove with a diameter of 17 cm and a height of 13 cm could reach the drying temperature 15 minutes faster than a wood pellet stove without the need of supervising the feeding process. Daily operational costs for fuel consumption using used lubricating oil were also more economical with 25% more dry salt produced than using a wood pellet stove.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.086
GPT teacher head0.304
Teacher spread0.218 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicInsurance, Mortality, Demography, Risk ManagementFrench-language works237,207