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Record W1765020586 · doi:10.1002/cjce.22368

Conversion of waste‐soap and soap‐like materials into diesel and gasoline by catalytic pyrolysis using virgin soap as model

2015· article· en· W1765020586 on OpenAlexvenueno aff
Zahid Hussain, Shaharin Anwar Sulaıman, Humaira Gul, Sana Farooq, Khalid M. Khan, Hussain Gulab, Muhammad Yasin Naz

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGasolineKeroseneDiesel fuelSOAPCatalysisPyrolysisChemistryFraction (chemistry)Waste managementBoiling pointOrganic chemistryFluid catalytic crackingChemical engineering

Abstract

fetched live from OpenAlex

Abstract In this work, soap was converted into a hydrocarbon‐rich product through catalytic pyrolysis using cement as a catalyst. The intention was to develop a new economical and effective method for preparing diesel and gasoline from soap waste in food processing. The optimum conditions for pyrolysis were investigated in a batch process under static conditions at different reactor temperatures, catalyst amounts, and reaction durations. The resulting oil product was analyzed using GC‐FID, GC‐MS, and FTIR. The best yield occurred at a reaction temperature of 400 °C for 60 min with 0.3 g/g (30 mass%) catalyst in soap waste, which resulted in 0.71 g/g (71 mass%) hydrocarbons with small fractions of pyran, ketone, and oxygenated compounds of mixed functional groups. The resulting liquid pyrolysate mainly consisted of diesel‐like oil with a small fraction of gasoline and some oils that had boiling‐point temperature ranges similar to kerosene.

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 score0.243
Threshold uncertainty score0.436

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 head0.013
GPT teacher head0.197
Teacher spread0.184 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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