Techno-Economic Feasibility of Wastewater Energy Transfer (WET) Systems for Cold Climates
Bibliographic record
Abstract
Wastewater is an abundant and underutilized source of thermal energy, that when coupled with heat exchangers and heat pumps, can provide heating and cooling to buildings. These Wastewater Energy Transfer (WET) systems can result in significant greenhouse gas (GHG) reductions compared to the conventional alternatives. The Ryerson University campus buildings are used as a case study and are simulated using weather data for 6 major Canadian cities. A detailed energy model was developed that compared the performance of a conventional HVAC system to the WET system. Using local GHG emissions factors and utility rates, the model calculated the energy consumption, operating costs and GHG emission for both systems. A detailed financial model was developed to calculate the project internal rate of return (IRR) and equity returns. The financial results were used to determine the optimal size of the WET system for each city. For 5 of the 6 major Canadian cities considered, the WET system was able to achieve a project IRR of greater than 8%, which was considered to be the minimum acceptable rate of return for a financeable project. The WET systems resulted in annual savings ranging from $128,544 in Calgary up to $2,057,855 in Montreal with an average of $852,196. Annual GHG emissions reductions ranged from 5,464 tonnes in Winnipeg to 21 tonnes in Montreal, with an average of 2,235 tonnes. A multi-unit residential building in Toronto, Canada was used as the case study to evaluate the viability of integrated WET systems with ambient-loop (AL) HVAC systems consisting of a series of dispersed heat pumps. An energy model was developed that calculated the performance of supplying the AL with conventional boilers and cooling towers, and compared it to supplying the AL with the WET system. The model calculated the annual energy consumption, operating costs and GHG emissions of both systems. Lastly, a financial model was developed to determine the project and equity returns of the WET AL system. The WET based AL system reduced electricity consumption by 8.2%, offset 516,410 m3 of natural gas and 4,655 m3 of water annually from evaporative cooling towers. The year 1 operating savings of the WET AL system were $156,911 and resulted in a project IRR of 9.10%. The annual GHG emissions reduction amounts to 991 tonnes, or approximately 89% of the emissions attributed to the HVAC system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".