Towards a Circular Urban Metabolism with Sewer Wastewater Heat Recovery Systems (SWWHRS): Introducing a SWWHRS Planning Decision Support System
Bibliographic record
Abstract
In this paper I describe how cities can reduce their dependence on fossil fuels for space and water heating by utilizing sewer wastewater heat as a low carbon energy source. I introduce the first stage of a planning decision support system for implementing sewer wastewater heat recovery systems. The model decision support system is intended for community energy planners and other relevant stakeholders to identify locations for matching sewer wastewater heat with appropriate thermal energy demand. This project demonstrates how ideal locations of sewer wastewater heat supply from municipal sewers can be matched with space/water heating demand using spatial analysis techniques and geographic information systems. This first proposed stage of a decision support system utilizes GIS to perform a site suitability analysis that can be used as the basis for further feasibility assessments in the planning of a sewer wastewater heat recovery system. Guelph, Ontario, Canada is used as a case study area. I go on to demonstrate the potential for reducing fossil fuel use in Guelph by identifying the volume of heat that can be recovered from each sewer segment and selecting several ideal locations that warrant further investigation into the feasibility of implementing a sewer wastewater heat recovery system. This proposed planning tool has potential for identifying significant carbon emission reduction opportunities in Ontario due to the large volume of natural gas consumed for space and water heating in the province`s urban residential and commercial zones and the prevalence of extensive sewer networks in all major urban areas. The decision support tool presented in this paper should however be utilized by a community energy planner in conjunction with other approaches for assessing how to reduce natural gas use for heating, as wastewater heat recovery is but one possible solution. Discussion of other approaches is beyond the scope of this research paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".