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Record W2760092147 · doi:10.5539/jsd.v10n5p61

Are Peat and Sawdust Truly Improve Quality of Briquettes as Fuel Alternative?

2017· article· en· W2760092147 on OpenAlexvenueno aff
Andi Bustan, Muhammad Arsyad

Bibliographic record

VenueJournal of Sustainable Development · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsBriquetteSawdustPeatEnvironmental scienceWaste managementStoveCarbonizationKeroseneWood fuelPulp and paper industryBusinessCoalEngineeringChemistryAdsorption

Abstract

fetched live from OpenAlex

The availability of energy and fuel is always a critical issue, and currently the increasing scarcity and price of kerosene is causing problems for both households and businesses. For example, chicken farmers in Central Kalimantan need to maintain the room temperature when nursing chicks up to 12 days old, and currently have few alternatives to kerosene stoves. Non-carbonized briquettes made from a mix of peat and sawdust can provide an alternative fuel source. The sawdust is available from local sawmills, which is otherwise an unutilized waste product that is burnt off, so adding to local smoke pollution. This study was conducted to determine the optimal composition and manufacturing process to produce bricks that have a maximal calorific content whilst maintaining a long burning time and a reduced tendency to break. Analyses in the laboratory showed that the highest calorific content obtainable was 19 020.63 kJ/kg with a peat/sawdust ratio of 2:1 (20 kg of peat and 10 kg of sawdust). These briquettes had a production efficiency of 83.26%, and calculations showed the overall production cost of the finished product to be around 40% lower than that of kerosene. The results indicate that a peat soil and sawdust mix produce a briquette that is viable as a fuel alternative, and based on this the researcher recommends that it be further developed commercially to meet the additional energy demands at lower cost of people in rural areas and industrial sectors.

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)0.001
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 score0.082
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

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