Correlation Assessment of Environmental and Energy Features of Wastewater Treatment Plants Towards Circular Economy
Bibliographic record
Abstract
Wastewater treatment plays an important role in ensuring ecological safety for water bodies. The achievement of the standard quality of wastewater treatment is being fulfilled through the introduction of the best available techniques. However, the process of wastewater treatment and sludge utilization itself is quite energy intensive. In the course of investment program planning and implementation, corporate managers and local authorities often focus on individual tasks such as how to improve the wastewater treatment quality or to improve energy efficiency; these factors are, however, however interrelated. Achieving the standard quality of wastewater treatment through the introduction of best available techniques and an increase in the number of technological stages usually leads to an increase in the overall energy consumption of the facilities. The hypothesis of the research suggests that the performance assessment of wastewater treatment plants needs mutual accounting of two main factors: wastewater treatment quality and energy efficiency. The main purpose of this work is to develop a methodological approach for assessing the effectiveness of wastewater treatment plants on the way towards the implementation of circular economy principles, followed by its trial application. The authors proposed a methodological framework that consists of three stages: (1) assessing the quality of wastewater treatment, (2) assessing energy efficiency, and (3) constructing a sustainability index on a correlation matrix. The outputs of the operation activities of wastewater treatment plants in Yekaterinburg for 2015-2018 were used for methodological framework approbation. The results of the investigation develop existing knowledge regarding the assessment of the effectiveness of management at treatment facilities and confirm the research hypothesis: the obtained values of the sustainable index correlate with the technical condition of the facilities both in terms of the achievement of the standard treatment quality the energy efficiency of the technological process. The practical significance of the study lies in the creation of a convenient and simple management tool for assessing the current success of wastewater treatment plants and progress in implementing successful circular economy practices. The proposed approach can be used as an element of environmental and economic assessment in the energy sector.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".