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Record W4404874361 · doi:10.1016/j.indcrop.2024.120134

Insight into the production factors influencing the physicochemical properties of densified briquettes comprising wood shavings and rice husk

2024· article· en· W4404874361 on OpenAlexafffund
Brice Martial Kamdem, R. Lemaire, Josiane Nikiema

Bibliographic record

VenueIndustrial Crops and Products · 2024
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHuskBriquettePulp and paper industryBioenergyProduction (economics)ChemistryBiofuelEnvironmental scienceAgronomyWaste managementBotanyBiologyEngineering

Abstract

fetched live from OpenAlex

The present work aims at elucidating the impact of various densification parameters on the physicochemical and combustion properties of fuel briquettes issued from the co-processing of wood shavings (W) and rice husks (RH) using a mechanical piston press machine. To that end, a design of experiments integrating three feeding speeds (15.76, 18.56, and 21.73 mm·s −1 ), two wood particle sizes (< 7 mm and between 7 and 10 mm) and six different RH contents ranging from 0 to 100 wt% was built. The obtained results showed that all the above operating factors influence the apparent density of the produced briquettes. Values ranging from 1143 to 1247 kg·m −3 were notably measured, with the highest one determined when considering a low feeding speed, small wood particles and an RH proportion of 80 wt%. While increasing the RH content led to an increase in the briquette density, the obtained results also showed that the higher the RH content, the lower the water resistance index. Measured values indeed went from 94 % on average for pure wood to ⁓85 % for pure RH, attesting to the potential challenge associated with the storage of briquettes containing high RH contents. As for the net calorific value, it was shown to rise from ⁓11 to ⁓ 16 MJ·kg −1 when varying the proportion of wood between 0 and 100 wt%. This trend was especially traced to an increase of the wt% of volatile matters in the produced briquettes accompanied by a decrease of their ash content. Combustion tests performed with different briquette samples then allowed inferring burning rates between 10.9 and 13.4 g·min −1 , specific fuel consumptions ranging from 115.8 to 138.4 g·l −1 and combustion efficiencies of around 12 %. As highlights, these tests demonstrated that the higher the wood content, the higher the burning rate, the lower the specific fuel consumption and the higher the combustion efficiency. Finally, two tested briquette formulations containing 80 and 100 wt% of wood were shown to have better combustion properties than a commercial firewood used for comparison. Total greenhouse gas (CO 2 and CH 4 ) emissions were even found to be reduced by 9.4 % when burning the RH-containing sample instead of firewood, while providing the same amount of sensible heat to a 3-l volume of water. These findings thus highlight the potential interest of beneficiating biomass wastes into briquettes for heat generation, further noting that the development of this type of alternative energy carrier offers multiple advantages in terms of waste management, reduction of the deforestation induced by the intensive use of firewood and mitigation of climate change through a potential reduction greenhouse gas emissions. • Strengths and weaknesses of briquettes produced from wood and rice husk are analyzed. • The impact of the densification parameters on the briquette quality is characterized. • Rice husk increases the briquette density but reduces the water resistance index. • High wood proportions foster the production of more reactive and easily ignited fuels. • GHG emissions can be reduced when burning the produced briquettes instead of firewood.

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 score0.013
Threshold uncertainty score0.379

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 head0.030
GPT teacher head0.209
Teacher spread0.179 · 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

Citations14
Published2024
Admission routes2
Has abstractyes

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