Creation of Zero Carbon Emissions Wastewater Treatment Plants - A Case Study in Crete, Greece
Bibliographic record
Abstract
Wastewater treatment plants use energy intensive processes for removing pollutants, consuming large amounts of electricity and emitting greenhouse gases. The possibility of zeroing carbon emissions due to energy use in these plants has been investigated in the current work with reference to the municipal treatment plant of Chania on the island of Crete, Greece. The sewage treatment plant processes 19,400 M3 daily consuming 0.543 KWh per M3 or 3840 MWh annually. The use of locally available renewable energies has been proposed for electricity generation combined with co-generation of heat and power from the biogas already produced in the plant with sludge digestion. Installation of solar-PV systems and wind turbines in the plant could generate electricity, each equal to 25 % of the annual electricity consumption in the plant. Additionally, biogas use can cover all the heating needs in the plant and can generate electricity corresponding at 20% of the total annual grid electricity use. Creation of a tree plantation, irrigated by the treated effluent, of 118.4 hectares, has been proposed which could annually offset carbon emissions due to the remaining grid electricity use. Creation of the tree plantation will create additional benefits, due to existing land desertification in Crete, additionally to carbon sequestration. The size of the required solar-PV and wind turbine systems has been estimated at 640 KWp and 391 KW and their cost at 0.832 mil € and 0.430 mil € correspondingly. Current work indicates that the combined use of solar energy, wind energy, biogas and carbon sequestration with tree plantations could zero carbon emissions in the municipal sewage treatment plant of Chania, Crete.
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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 | 0.002 | 0.000 |
| 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".