Wood Pellet Production in the Southern United States: A Qualitative Economic Assessment and Experiment to Determine the Production Factors Influencing Self Heating During Storage
Notice bibliographique
Résumé
Wood pellet production in the southern United States has more than doubled in the past three years, surpassing western Canada as the region in North America with the greatest production. Most of this increase is caused by a few large plants being built specifically for export to Europe where the pellets are burned for electricity. The economics for producers in the region are helped by the decline in manufacture of traditional wood products including structural panels, lumber, and pulp and paper. Demand for wood pellets is set to continue its rapid rise though some of the traditional wood products are also set to rebound. Of concern for pellet producers is both U.S. made pellets? future position in the world energy market as well as their place in the fiber market of the southern United States. In addition, when pellets are stored in large volumes, there is a heating effect. This effect is exacerbated by a hot and humid subtropical climate as well as the feedstock of choice of large producers (Southern Yellow Pine). This heating can cause great expense to producers who are shipping overseas as bulk carriers and European buyers usually have a threshold temperature for biological materials shipped overseas. In addition, this heating often exacerbates convection currents and water deposition inside storage piles before loading. This water can quickly degrade pellets. The first chapter of this thesis looks at the pellet markets worldwide as well as the state of the wood fiber markets in the southern U.S. which made it possible for wood pellet production to get a foothold in the region. It is concluded that use of wood pellets worldwide will grow, with most growth localized in northern Europe and North America. Production in the Southeast will continue to expand, taking much of the fiber that would have been taken by the now shrinking pulp and paper industries though also utilizing residues from sawmilling and possibly harvest residues. The second chapter is a factorial analysis in which production factors such as drying temperature and aging are varied between different production runs at the plant of a large wood pellet producer. Quality attributes such as bulk density, durability, and moisture content of pellets going into storage were also monitored. It was then assessed whether these factors had any effect on the temperatures attained in storage. It was found that the most significant factor in the self heating of pellets was the starting temperature of pellets. Therefore, wood pellet producers may do well by investing in consistent and effective methods of cooling pellets after the production runs. Drying temperature also seemed to have a negative correlation to temperature increase though more research is needed as to whether this effect remains when controlling for start temperature.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».