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Record W4415030851 · doi:10.1016/j.indcrop.2025.121869

Fuel briquettes produced via the co-treatment of wood sawdust, miscanthus and wheat straw: Physicochemical properties

2025· article· en· W4415030851 on OpenAlexafffund
Brice Martial Kamdem, R. Lemaire, Josiane Nikiema

Bibliographic record

VenueIndustrial Crops and Products · 2025
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaÉcole de technologie supérieure
KeywordsBriquetteMiscanthusBiomass (ecology)SawdustRaw materialBioenergyStrawBiofuelCompaction

Abstract

fetched live from OpenAlex

Beneficiating biomass wastes and energy crops to produce densified fuels represents an interesting alternative to the use of conventional fossil-based energy carriers. Identifying the best-suited operating parameters and biomass resources, which translate into high quality fuels, is far from trivial, however, especially when it comes to co-processing different biomass types in order to combine their respective strengths. To promote the development of innovative, efficient and eco-friendly biomass-based densified fuels, this paper examines the physicochemical properties of briquettes obtained via the co-treatment of wood sawdust (S), miscanthus (M), and wheat straw (WS), using a hydraulic press machine. It aims firstly to elucidate the impact of the feedstock properties and compaction force (CF) on the main features of the fuels obtained. A design of experiments was built considering two CF (225 and 450 kN), two biomass particle sizes (PS) (less than 1.25 mm and between 1.25 and 2.5 mm), and 5 mixing ratios. The latter comprised proportions of each biomass ranging from 0 % to 100 % in S/M, S/WS, and M/WS blends. Tests were then carried out to produce multilayer briquettes with interesting physical and combustion characteristics. The results obtained revealed that all the operating factors influence the density as well as the impact and water resistance indexes (IRI and WRI) of the briquettes. It was found that the higher the CF, the finer the particles, and that the higher the proportion of S, the higher the density, whose values were found to range between 512 and 1121 kg·m −3 . Moreover, higher M and WS contents were associated with lower IRI and WRI. In line with expectations, the type of feedstock mixtures was found to impact the briquette ash and volatile matter contents (AC and VMC). Raising the S proportion decreases the AC and increases the VMC, hence increasing the net calorific values up to 19.12 MJ·kg −1 . As for the ignition time (IT), the higher the CF and the lower the S content, the higher the IT. Alternatively, increasing the WS content increases the IT and the burning time. Finally, the multilayer briquettes produced using S in the outer sheets and high M contents in the central layer were found to exhibit better performances in terms of density, IRI, IT, and combustion time as compared to their conventional single-layer counterparts. While the multilayer briquette production method shows considerable promise, its long-term adoption will require addressing technical challenges, such as those related to the consistent preparation and effective mixing of raw materials. • Briquettes produced from blends of wood, miscanthus and wheat straw are characterized. • Operating factors affecting the fuel quality are studied using design of experiments. • High wood contents increase the fuel density and water/impact resistance indexes. • Increasing the wood content yields reactive fuels that ignite and burn off readily. • A new concept of multilayer briquettes is proposed to improve combustion features.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.025
GPT teacher head0.223
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2025
Admission routes2
Has abstractyes

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