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Enregistrement W1509858209 · doi:10.2172/1015131

Investigation of the Potential for Biofuel Blends in Residual Oil-Fired Power Generation Units as an Emissions Reduction Strategy for New York State

2009· report· en· W1509858209 sur OpenAlexaboutno aff
C.R. Krishna, RM McDonald

Notice bibliographique

Revuenon disponible
Typereport
Langueen
DomaineEngineering
ThématiqueVehicle emissions and performance
Établissements canadiensnon disponible
Organismes subventionnairesBrookhaven National LaboratoryNew York State Energy Research and Development AuthorityU.S. Department of Energy
Mots-clésBiofuelFuel oilResidual oilWaste managementEnvironmental scienceBiodieselDiesel fuelRenewable energyNOxDistillationFossil fuelEngineeringChemistryCombustionPetroleum engineering

Résumé

récupéré en direct d'OpenAlex

There is a significant amount of oil, about 12.6 million barrels per year, used for power generation in New York State. The majority of it is residual oil. The primary reason for using residual oil probably is economic, as these fuels are cheaper than distillates. However, the stack emissions from the use of such fuels, especially in densely populated urban areas, can be a cause for concern. The emissions of concern include sulfur and nitrogen oxides and particulates, particularly PM 2.5. Blending with distillate (ASTM No.2) fuels may not reduce some or all of these emissions. Hence, a case can be made for blending with biofuels, such as biodiesel, as they tend to have very little fuel bound sulfur and nitrogen and have been shown in prior work at Brookhaven National Laboratory (BNL) to reduce NOx emissions as well in small boilers. Some of the research carried out at CANMET in Canada has shown potential reductions in PM with blending of biodiesel in distillate oil. There is also the benefit obtaining from the renewable nature of biofuels in reducing the net carbon dioxide emitted thus contributing to the reduction of green house gases that would otherwise be emitted to the atmosphere. The present project was conceived to examine the potential for such benefits of blending biofuels with residual oil. A collaboration was developed with personnel at the New York City Poletti Power Plant of the New York Power Authority. Their interest arose from an 800 MW power plant that was using residual oil and which was mandated to be shut down in 2010 because of environmental concerns. A blend of 20% biodiesel in residual oil had also been tested for a short period of about two days in that boiler a couple of years back. In this project, emission measurements including particulate measurements of PM2.5 were made in the commercial boiler test facility at BNL described below. Baseline tests were done using biodiesel as the blending biofuel. Biodiesel is currently and probably in the foreseeable future more expensive than residual fuel. So, another task was to explore potential alternative biofuels that might confer emission benefits similar to those of biodiesel, while being potentially significantly cheaper. Of course, for power plant use, availability in the required quantities is also a significant criterion. A subsidiary study to determine the effect of the temperature of the filter used to collect and measure the PM 2.5 emissions was conducted. This was done for reasons of accuracy in a residential boiler using distillate fuel blends. The present report details the results obtained in these tests with the baseline ASTM No. 6 fuel and blends of biodiesel with it as well as the results of the filter temperature study. The search for the alternative 'cheaper' biofuel identified a potential candidate, but difficulties encountered with the equipment during the testing prevented testing of the alternative biofuel.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,219
Score d'incertitude au seuil0,801

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,072
Tête enseignante GPT0,287
Écart entre enseignants0,215 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2009
Routes d'admission1
Résumé présentoui

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