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Enregistrement W6986356966

Pesticides from fast pyrolysis of agricultural and forestry residues

2008· article· en· W6986356966 sur OpenAlexaboutno aff

Notice bibliographique

RevueScholarship@Western (Western University) · 2008
Typearticle
Langueen
DomaineNeuroscience
ThématiqueTactile and Sensory Interactions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPyrolysisResidence time (fluid dynamics)CharPesticideFraction (chemistry)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This thesis investigates the production of pesticides through the fast pyrolysis of three biomass feedstocks: tobacco leaves, dried coffee grounds and pinewood killed by beetles. It was observed that a significant fraction of the bio-oil escaped the condensing train as a fine mist; this is a common problem in pyrolysis operations. An electrostatic demister was, therefore, developed to recover this mist. Tobacco leaves were pyrolyzed to produce bio-oil. Pyrolysis was carried out at six different temperatures from 350°C to 600°C and at three different vapor residence times (5, 10 and 17 s), to study the effect of operating conditions on the bio-oil yield. The trends clearly indicated a strong effect of temperature and vapor residence time on the bio-oil yield. A temperature of 500°C was then selected and the pyrolyzer was operated again at the lowest vapor residence time of 5 s to obtain accurate liquid, gas and char yields. Tobacco bio-oils produced at different temperatures (350-600oC) and at a vapor residence time of 5 s were tested for their bactericidal, fungicidal and insecticidal activities against pests found on plants in Canada that currently require improved control options. A significant finding of the research was that even fractions containing no nicotine had significant activity towards the aforementioned pests. Dried coffee grounds were pyrolyzed to produce bio-oil. Pyrolysis was carried out at five different temperatures from 400°C to 600°C and at a vapor residence time of 5 s, to study the effect of temperature on the bio-oil yield. The trends clearly indicated a strong effect of temperature on the bio-oil yield. Coffee grounds bio-oils produced at different temperatures (400-600oC) were tested for their bactericidal and insecticidal activities against pests found on plants in Canada that currently require improved control options. While some compounds in the bio-oil, such as phenols, were active against both beetles and bacteria, the coffee bio-oil contained in chemicals that provided additional insecticidal activity but had no bactericidal activity. Single-stage and two-stage tubular electrostatic precipitators were designed for the recovery of bio-oil mist. Because of concerns regarding bio-oil stability, an inert fogging oil was used for the development of the demisters. A nitrogen stream containing very fine droplets of fogging oil was forced through the electrostatic precipitator chamber. It was found that 98.6 wt% of the oil droplets present in the turbulent jet were mechanically collected on the inner walls of the test chamber. When the electrode was energized at 13 K V, 92.37wt%ofthedropletsthathadnot been mechanically separated were collected in single-stage mode. The collection efficiency was increased to 93.18 wt%, when the electrostatic precipitator was operated in two-stage mode. Voltage-current (V-I) characteristics of the singlestage and two-stage electrostatic precipitators were studied in detail for different test conditions. Nitrogen impurities played a major role in determining the V-I characteristics. They became less relevant with the introduction of mist in the nitrogen stream, presumably due to the presence of water vapor in the gas. The two- stage tubular electrostatic precipitator was scaled up and tested on a fluidized bed pilot plant used for the pyrolysis of biomass. A droplet collection efficiency of 95 wt% was observed. Such demisters will extend, to the product recovery train, the process intensification gains of short residence time processes such as fast pyrolysis.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

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)0,0010,000

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 Opus0,102
Tête enseignante GPT0,297
Écart entre enseignants0,195 · 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 source (Gemma direct ou Codex distillé), 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
Publié2008
Routes d'admission1
Résumé présentoui

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