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Record W46684845

Handling of corn stover bales for combustion in small and large furnaces

2010· article· en· W46684845 on OpenAlexaboutno aff
René Morissette, Philippe Savoie, Joey Villeneuve

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCorn stoverCombustionStoverWaste managementEnvironmental sciencePelletsPulp and paper industryChemistryMaterials scienceAgronomyEngineeringBiofuelComposite material
DOInot available

Abstract

fetched live from OpenAlex

Corn stover was harvested in the spring when it is very dry (moisture content below 10%). Two bale formats were considered for direct combustion in two furnaces: small square bales (0.35 m x 0.45 m x 0.60 m; 10 kg) and large square bales (0.8 m x 0.9 m x 1.8 m; 200 kg). A small 500,000 BTU/h dual chamber log wood furnace was located at a hay growing farm (Neuville, Quebec) where the heat was initially transferred to a hot water pipe system and then transferred to a hot air exchanger to dry hay bales. The small stover bales were placed directly into the combustion furnace. The low density of the bales compared to log wood, required filling up to 8 times more frequently. Stover bales produced an average of 6.4% ash on a DM basis and would require an automated system of ash removal. Combustion gas contained levels of particulate matter of more than 1417 mg/m3, above the local acceptable maximum of 600 mg/m3 for combustion furnaces. Corn stover bales cannot be used directly without improving combustion or using an exhaust gas filtering system. The second combustion unit was a high capacity 12.5 million BTU/h single chamber furnace used to generate steam for a feed pellet mill (Saint-Philippe-de-Neri, Quebec). Large corn stover bales were broken up and fed on a conveyor and through a screw auger to the furnace. Again, the stover was light compared to the wood chips used in this furnace (46 vs. 163 kg DM/m3 for bulk density). The stover could not be fed continuously to the furnace mainly for mechanical reasons: roll up of stover on the walking floor, auger plugging and bridge over the auger. Only a small quantity of stover was actually fed in the furnace, and no significant combustion data could be collected. Stover cannot therefore be used directly in furnaces designed for traditional wood logs or wood chips. Either the stover should be transformed into a suitable physical form (dense pellets, cubes or logs) or the furnace must be modified and adapted for the fuel.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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 designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

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