Benchmarking the Sustainability of Sludge Handling Systems in Small Wastewater Treatment Plants in Ontario
Notice bibliographique
Résumé
This project quantitatively benchmarked all aspects of sludge handling in a cross-section of small wastewater treatment plants across Ontario. Using plant operational data and on-site measurements, a variety of sustainability metrics were evaluated: energy consumption, chemical use, biosolids disposition, biosolids quality, and greenhouse gas emissions. In addition, a desktop analysis was conducted to determine the sustainability impact of incorporating innovative technologies into facilities with conventional processes. Parameters from select new technologies within the study sample were applied to plants within the sample that employed conventional processes, and the impact on greenhouse gas (GHG) emissions was calculated. Overall electricity consumption for sludge handling ranged from 0.9 – 3.9 kWh per dry kg of raw sludge. The thermo-alkali hydrolysis and auto-thermal thermophilic aerobic digestion (ATAD) processes consumed the least (0.3 kWh/dry kg) and most (3.8 kWh/dry kg) amount of electricity for stabilization, respectively. Mechanical dewatering processes consumed minor amounts of electricity (2 – 5% of total sludge handling draw), however, associated polymer dosages were found to be higher than literature values in some cases. The disposition fuel requirements for plants with dewatering were up to 85% lower than facilities without dewatering. Biosolids contaminant (pathogen/metals) contents were observed to be substantially below Non-Agricultural Source Material (NASM) requirements. The copper content of the hauled biosolids exhibited the highest concentration relative to the NASM limit among all plants studied, ranging from 14 – 37% among facilities practicing land application of biosolids. Four plants generated a product that met Class A requirements for 𝐸. 𝑐𝑜𝑙𝑖 content, including one facility that generated it through a long-term storage approach (GeoTube™). Carbon emissions ranged from -119 to 299 kg CO2 equivalents per dry tonne of raw sludge. Six of the eight facilities that practiced land application of biosolids exhibited net-negative GHG emissions, as the carbon credits gained from fertilizer production avoidance outweighed the emissions associated with sludge processing and transportation operations. Of these six plants, five employed sludge treatment configurations that are common in Ontario. Given that land application is the most common disposal practice among small treatment plants in Ontario, the findings indicate that current conventional practices can be sustainable with respect to GHG emissions. The innovative technology assessment revealed that existing trucking requirements and polymer dosage are the primary factors that determine whether new technology implementation would improve environmental sustainability. The benchmarking approach developed and information gathered is of value to plant owners and operators who seek to better understand how their utility is performing relative to peers, identify areas of need and further investigation, and improve the long-term sustainability of their operations.
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 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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».