Benchmarking strategies to control GHG production and emissions
Bibliographic record
Abstract
Benchmarking has been a useful tool for unbiased comparison of control strategies in wastewater treatment plants (WWTPs) in terms of effluent quality, operational cost and risk of suffering microbiology-related total suspended solids (TSS) separation problems. This chapter presents the status of extending the original Benchmark Simulation Model No 2 (BSM2) towards including greenhouse gas (GHG) emissions. A mathematical approach based on a set of comprehensive models that estimate all potential on-site and off-site sources of COinf2/inf, CHinf4/inf and Ninf2/infO is presented and discussed in detail. Based upon the assumptions built into the model structures, simulation results highlight the potential undesirable effects on increased GHG emissions when carrying out local energy optimization in the activated sludge section and/or energy recovery in the anaerobic digester. Although off-site COinf2/inf emissions may decrease in such scenarios due to either lower aeration energy requirement or higher heat and electricity production, these effects may be counterbalanced by increased Ninf2/infO emissions, especially since Ninf2/infO has a 300-fold stronger greenhouse effect than COinf2/inf. The reported results emphasize the importance of using integrated approaches when comparing and evaluating (plant-wide) control strategies in WWTPs for more informed operational decision-making.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".