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Enregistrement W6990191656

DERIVATION OF FIRST-ORDER DECAY HEAT GENERATION FUNCTION AND PREDICTION OF THERMAL ENERGY POTENTIAL FOR A MUNICIPAL SOLID WASTE LANDFILL

2021· dissertation· en· W6990191656 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2021
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueLandfill Environmental Impact Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLandfill gasMethaneHeat generationWork (physics)Municipal solid wasteThermal energyElectricity generationThermal
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

As the consequences of green house gas production at landfills become more apparent to both the public and private sector, work has been performed at many landfills over the last two decades to explore the mechanisms controlling gas and heat generation within buried solid waste. Mechanisms and numerical models of the physical, chemical, and biological processes have been studied in order to better predict the conditions within the waste fill and the rates of gas and heat production. These models are useful tools for operators and designers to develop plans for mitigating some negative environmental impacts of landfilling, by collecting and using the recoverable natural gas or thermal energy to supplement conventional energy sources.\nThe Northern Landfill near Saskatoon, SK is a private landfill where the methane and thermal energy potential of the site is of interest. The landfill has been in operation since 1987 and contains approximately 2.5 megatonnes of waste. Vertical temperature distribution within the buried waste was measured using thermistors installed in boreholes, which were advanced using a sonic drill rig. Transient temperature data was collected from four locations across the top of the landfill, with two of the locations providing daily average temperatures with depth over a period of 800 days (2.2 yr). A 1D heat transport model was developed to compare calculated outputs to in-situ site temperature data over a 1-year period. The model was also used to simulate cell construction, waste placement, and heat generation over the life of the landfill.\nThe background and theory describing anaerobic landfill gas generation available in the literature was reviewed. Research completed to date in the literature predicting or estimating heat generation and transport within landfills was also reviewed. In the literature, heat generation is stated to be related to gas generation through anaerobic digestion, though no exact conversion factor was agreed upon. Empirically derived equations that define transient heat generation were reviewed however it was found that the variables and methodology did not relate heat generation to gas generation or degradable organic matter of the waste. Climatic factors of annual precipitation and average annual temperature were two of the variables governing the empirical heat generation function, however the climate experienced by the Northern Landfill did not produce a useable curve. Therefore, a first-order decay function was derived to represent the transient heat generation rate associated with the anaerobic digestion of organic matter in the landfill environment. This offers a mechanistic approach to defining heat generation in landfills, as opposed to empirical definitions which are available in the literature. The two variables defining the function are biochemical heat potential (BHPULT), comparable to biochemical methane potential (BMP or L0) in the gas generation literature, and a decay rate k.\nThe results of the 1D heat transport model which used a first-order decay function for heat generation suggest that a single k value representing the average decay rate poorly defined the dependency of heat generation to microbial populations and environmental conditions within the landfill. As a result, heat generation rates predicted by the derived function over the 2018 to 2019 monitoring period were significantly higher than those estimated through model calibration. Nonetheless, the model was able to simulate waste placement and the accumulation of thermal energy at the Northern Landfill, reaching temperatures at depth equivalent to those measured in the field in the year 2019. Two locations were modelled within the core of the landfill. BHPULT was predicted to be between 115 and 240 MJ per cubic metre of waste (MSW). BMP and equivalent cellulose content (Ceq) of the MSW was calculated from BHPULT, resulting in ranges of 19 to 120 LCH4/kgMSW and 4 to 27 % weight respectively. Peak heat generation rates from the first-order decay function were between 0.13 and 0.28 W/m3. The lower limits of the ranges results from the location within older average MSW age (16.2 y) and the higher limits from the younger location (6.6 y). Calibrated present-day heat generation rates were between 0.020 and 0.148 W/m3 at the older location and 0.009 and 0.205 W/m3 at the younger location.\nIt is recommended that an improvement to the first-order decay function be implemented which incorporates a stepwise function governing the value of k, dependent on the temperature of the surrounding waste. The k value should be limited by a maximum potential decay rate of 0.12 y-1 (3.3 x 10-4 d-1) at temperature values reported in the literature optimal for mesophilic microbial activity (20 to 45 °C). The k value should decrease until a threshold temperature reported in the literature at which no methanogenesis takes place (a k value of zero). A dependency of the decay rate to moisture availability should also be included, as well as the inclusion of updated modelling parameters or waste layer geometries as they are investigated further.

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 candidatesMéta-épidémiologie (sens strict)
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 enseignants0,494
Score d'incertitude au seuil1,000

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 savante0,0000,001
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 Opus0,007
Tête enseignante GPT0,162
Écart entre enseignants0,155 · 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.

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
Publié2021
Routes d'admission1
Résumé présentoui

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