Wastewater Treatment Plant Network Design Using a Multiscale Two-Stage Mixed Integer Stochastic Model
Bibliographic record
Abstract
Abstract Design of wastewater treatment plant (WWTP) networks can be complicated by the existence of various uncertainties and multiscale nature of the planning process. This article presented a multiscale two-stage mixed integer stochastic (MSTMIS) model for optimal design of WWTP networks under uncertainty. The model was first formulated by a general two-stage stochastic nonlinear programming problem and solved by genetic algorithm to obtain the deterministic single nominal scenario and fix the first-stage long-term decisions. A sensitivity analysis was then used to select the most influential parameters, from which second-stage short-term decisions were finalized by generating stochastic scenarios. A real-world case study on development of a WWTP network in the metropolitan area of St. John's, Canada was conducted to examine the efficacy of the proposed model. Optimization results indicated that the total cost over a 20-year span was optimized at $8.28 × 10 7 by the MSTMIS model, which is lower than that optimized by the traditional one-stage solution algorithm. The proposed MSTMIS model can simultaneously address the challenges posed by uncertainty and multiscale nature and, thus, provide the decision makers more confidence in making economic decisions related to WWTP network design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".