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Record W4285794608 · doi:10.3390/su14148818

Hydrothermal Carbonization of Spent Coffee Grounds for Producing Solid Fuel

2022· article· en· W4285794608 on OpenAlexafffund
Yulin Hu, R.K. Gallant, Shakirudeen A. Salaudeen, Aitazaz A. Farooque, Quan He

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsDalhousie UniversityUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Prince Edward Island
KeywordsHydrothermal carbonizationTorrefactionSolid fuelRaw materialPulp and paper industryBiomass (ecology)CombustionCoffee groundsWaste managementBiofuelHeat of combustionBioenergyCarbonizationEnvironmental scienceMaterials scienceChemistryChemical engineeringPyrolysisFood scienceAgronomyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Spent coffee grounds (SCG) are industrial biowaste resulting from the coffee-brewing process, and they are often underutilized and end up in landfills, thereby leading to the emission of toxic gases and environmental damage. Hydrothermal carbonization (HTC) is an attractive approach to valorize wet biomass such as SCG to valuable bioproducts (i.e., hydrochar). Thus, in this work, the HTC of SCG was carried out in a 500 L stainless steel vessel at 150, 170, 190, 210, and 230 °C for 30 min, 60 min, 90 min, and 120 min and a feedstock to water weight ratio of 1:5, 1:10, and 1:15, and the use of the resulting hydrochar as a solid fuel was evaluated. The results showed that a high energy recovery (83.93%) and HHV (23.54 MJ/kg) of hydrochar was obtained at moderate conditions (150 °C, 30 min, and feedstock to water weight ratio of 1:5) when compared with conventional approaches such as torrefaction. Following this, the surface morphology, functionality, and combustion behavior of this hydrochar were characterized by SEM, FTIR, and TGA, respectively. In short, it can be concluded that HTC is an effective approach for producing solid fuel from SCG and the resulting hydrochar has the potential to be applied either in domestic heating or large-scale co-firing plants.

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 score0.178
Threshold uncertainty score0.451

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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations27
Published2022
Admission routes2
Has abstractyes

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