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Record W4318820215 · doi:10.1002/cjce.24863

Biodiesel production from <scp> <i>Nannochloropsis</i> </scp> microalgal biomass‐derived oil: An experimental and theoretical study using <scp>the RSM‐CCD</scp> approach

2023· article· en· W4318820215 on OpenAlexvenueno aff
Kimia Karimi, Majid Saidi, Pantea Moradi, Ali Taheri Najafabadi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBiodieselBiodiesel productionBiomass (ecology)Response surface methodologyExtraction (chemistry)Raw materialNannochloropsisBiofuelChemistryPulp and paper industryYield (engineering)Algae fuelMethanolFood scienceBotanyChromatographyBiotechnologyMaterials scienceOrganic chemistryAlgaeBiologyAgronomyCatalysis

Abstract

fetched live from OpenAlex

Abstract Biodiesel production from Nannochloropsis is investigated in the current study. Based on the extraction studies, the used biomass is composed of 50% saponifiable lipids, which turns this species to a vulnerable feedstock for biodiesel production. It should also be noted that the acidity of the obtained crude bio‐oil is higher than 2 mg KOH/g, hence it was attempted to survey the biodiesel production from the obtained oil through the esterification reaction with the aid of response surface methodology (RSM). According to the data, the highest biodiesel yield (99.5%) from the bio‐oil is obtained at a reaction temperature of 69°C, a reaction time of 30 min, a methanol to oil molar ratio of 9:1, and an H 2 SO 4 concentration of 0.13 wt.%. In the next step, the in‐situ extraction and esterification of Nannochloropsis were investigated at the observed optimum reaction conditions. Based on the analysis, biodiesel yield from the dry cell weight of the biomass is considered to be 16%, which accounts for 99% conversion of bio‐oil to biodiesel.

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 score0.072
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.216
Teacher spread0.199 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

Explore more

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