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Record W2319661197 · doi:10.1021/ie500769s

Leaching Characteristics of Inorganic Constituents from Oil Palm Residues by Water

2014· article· en· W2319661197 on OpenAlexafffund
Pak Yiu Lam, C. Jim Lim, Shahab Sokhansanj, Pak Sui Lam, J.D. Stephen, Amadeus Pribowo, Warren Mabee

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsQueen's UniversityUniversity of British Columbia
FundersBiomass ProgramNatural Sciences and Engineering Research Council of Canada
KeywordsLeaching (pedology)Pulp and paper industryFoulingChemistryPalm kernelRaw materialPotassiumWater contentWood ashPalm oilEnvironmental scienceMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Oil palm residues are not currently suitable as feedstock for thermal energy generation because their high ash content can cause slagging, corrosion, and fouling. A water leaching treatment is a potential strategy to reduce the ash content in these residues. This study evaluates the effects of the duration and temperature of water leaching on two types of oil palm residues, namely, empty fruit bunches (EFBs) and palm kernel shells (PKSs). The optimum process duration for ash removal from EFBs was found to be 5 min, as the effect of convection on scrubbing was observed to remove substantial ash from the substrate during this period. A cross-flow model with estimated kinetic parameters of water leaching for EFB and PKS was developed and showed that three leaching stages of EFB achieved the greatest ash reduction from 5.47% to 2.63%. A low ash content of PKS showed no value for ash removal in any leaching process. Although there was no significance in the total ash reduction due to temperature effects, the leaching treatment was found to be most effective in reducing potassium, from 2.42% to 0.69% and 0.36% at 25 and 55 °C, respectively.

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)0.001
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 score0.006
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.028
GPT teacher head0.249
Teacher spread0.222 · 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

Citations26
Published2014
Admission routes2
Has abstractyes

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