Thermodynamic Analysis of a Self-Sufficient and Nearly Emission Free Wastewater Treatment Plant
Bibliographic record
Abstract
Abstract Effluent standards in wastewater treatment plants have been very strict due to a dramatic increase in the amount of contaminants of household and industrial wastewater. For this reason, wastewater treatment plants consume a significant amount of power in order to treat wastewater throughout the world. Since most of the power in the United States is produced by petroleum products and natural gas, greenhouse gas emission has been a severe problem. In this study, a multigeneration energy system has been developed numerically to create an energetically self-sufficient wastewater treatment plants (WWTP) with zero emission. Since the WWTP part has already been investigated in another study [1], only the oxy-fuel power generation system, which is a combination of an oxy-fuel combustion cycle with a Rankine cycle, has been modeled. In the oxy-fuel combustor, the biogas generated by anaerobic digestion process is combusted with pure oxygen and recycled CO2 to produce hot exhaust gas. The system has been developed and modelled using Engineering Equation Solver and several parameters in the power cycle have been varied so as to investigate their effects on thermodynamic efficiencies and self-sufficiency ratio. The parameters in this sensitivity study are compression ratio, turbine inlet temperature, CO2 return ratio, recycled CO2 temperature, and temperature difference in the regenerator. Energy and exergy efficiencies of the parametric study have been calculated along with the self-sufficiency ratio. The maximum results for energy and exergy efficiencies shown to be 53.7% and 52.2%, and for self-sufficiency ratio to be 130.5%. The results show that this concept has a significant potential for future deployment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".