Resource recovery strategies for municipal water planning and management: a system dynamics modeling approach
Notice bibliographique
Résumé
The true value of water has not yet been captured in municipal water planning and management, despite the increasing availability of technologies designed to create value from wastewater. Adapting system processes for water reclamation, energy generation, and production of other outputs (such as fertilizers) can transition wastewater treatment facilities into economically viable resource recovery facilities. Although resource recovery is an appropriate solution for municipal water management, its success depends mainly upon techno-economic feasibility, environmental sustainability, and public acceptance. Both the public and private sector face several barriers to advancing resource recovery, such as operational challenges, inappropriate regulatory frameworks, and a lack of capacity to develop or evaluate business plans pertaining to resource recovery and water reuse. A variety of possible solutions can be defined based on the principle of resource recovery, such as decentralized treatment and reuse, co-digestion with organic waste for energy, and nutrient recovery through struvite precipitation. A broad range of products can potentially be generated from wastewater at different time scales into the future. The purpose of this study is to represent and analyze the value of products that impact the feasibility of both established and emerging resource recovery strategies for municipal water planning and management. A decision support tool developed using system dynamics modeling software represents the various system components that are important to decision makers. The dynamic simulation model permits testing of the economic feasibility of specific resource recovery strategies and facilitates planning of resilient water systems. The system dynamics model represents real world processes through nonlinear feedbacks, changing variables, and delays – and is intended to increase understanding of the effects of feedbacks between resource recovery sub-systems with external drivers and variables. The model presented runs at a weekly time step and simulates the performance of user-specified resource recovery strategies into the future under various population growth, climate change, and water consumption/conservation scenarios, revealing trade-offs in the medium- to long-term. Simulating potential future scenarios by altering important input parameters helps to identify the most important variables to the economics of the specific strategies considered. The model is applied to the City of Calgary, Alberta to explore the impact to the City’s overall per capita water demand, based on the theory of diffusion of innovations framework and population served by source-separated resource recovery systems. The model was calibrated with financial data from existing utilities that have successfully integrated resource recovery strategies, in some cases recovering the entire costs of the capital investment required to upgrade in less than 3 years. This thesis includes a description of model characteristics and capabilities, as well as results that highlight the value of system dynamics modelling methods, which simulate system behaviour and shed light on cause-and-effect relationships, representing resource recovery systems in a realistic, comprehensive way. Decentralized wastewater systems with greywater reuse could help the City achieve its goal of reducing per capita water use below 350 liters with either the “intermediate” or “fast” adoption rates. The final result for the average per capita daily municipal water demand at the end of 2043 was 302, 348, and 391 liters per capita per day (Lpcd) for each of the three strategies tested (“fast”, “intermediate”, and “slow” rates of adoption). Retrofitted stormwater systems can also offset water production (up to 25 Lpcd of savings).
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».