Design of an innovative hydrogen ecosystem integrating renewable energy options with wastewater management for a sustainable city
Bibliographic record
Abstract
This study introduces a newly designed hybrid multigeneration system that integrates anaerobic digestion, solar photovoltaic panels, and bioelectrochemical cells to convert wastewater into electricity, hydrogen and domestic hot water in order to achieve sustainable cities. The system is designed to potentially consider the Ashbridges Bay Wastewater Treatment Plant in Toronto, using both solar and biogas resources for energy production. Key system components include a steam Rankine cycle, an organic Rankine cycle, and a microbial fuel cell-microbial electrolysis cell unit, which together support simultaneous waste treatment and clean energy generation. The designed system has an overall energy efficiency of 38.88 % and exergy efficiency of 31.36 %. The system achieves a net electrical output of 13.66 MW, while producing 0.07 kg/s of hydrogen and two streams of thermal energy at different temperatures to meet residential and industrial demands. The generated hydrogen is then liquefied and stored at a nearby refueling station located at the Toronto port, where it is utilized to fuel marine vessels such as boats and ships. The parametric studies demonstrate that boiler efficiency, biogas yield, and reference temperature significantly affect system performance. The proposed configuration offers a scalable solution for integrating renewable energy with wastewater treatment and hydrogen infrastructure for sustainable urban applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".