Techno-economic assessment of different renewable energy integration scenarios in a cold climate wastewater treatment plant
Bibliographic record
Abstract
Energy demand of wastewater treatment plants (WWTPs), situated in cold climates, primarily pertain to leachate heating and air blowers essential for oxygenation of the aeration pools. Few studies have explored integration of passive and/or renewable energy sources to mitigate energy consumption of WWTPs' energy systems. Yet, the optimum integration scenario remains indeterminate. To address this, techno-economic assessments of diverse integration options, including solar PV panels + electric batteries, solar-assisted ground-source heat pump (SAGSHP), and high pressure underground compressed air storage tanks (UCASTs) are undertaken. The influence of the heat pump's coefficient of performance (COP), and stored air pressure in the high-pressure tanks are discussed for a medium size WWTP with an average effluent capacity of 300 m 3 day −1 , located in Quebec, Canada. The results reveal that solar PV panel + lithium-ion batteries are a better option in comparison to the UCASTs in case of passive aeration during a power outage in winter. Also, the all-included holistic scenario of integrating renewable solar energy (both PV and thermal), SAGSHP, and electric batteries results in a payback period of 6.3 years with an internal rate of return and total annual cost of 15.5 % and 269 k$, respectively. This scenario can be considered as the best energy integration scenario. • Different renewable energy integration scenarios are evaluated tecno-economically. • Feasibility of passive aeration via underground compressed air tanks is scrutinized. • Heat pumping for leachate heating saves required energy cost up to 80 %. • Lithium-ion batteries are the best solution for aeration during winter power outage.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".