Handling of corn stover bales for combustion in small and large furnaces
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".