Bibliographic record
Abstract
Real time control (RTC) is particularly promising in large, flat and heterogeneous sewer systems with a high in-line storage volume.For the simulation of such systems with major backwater effects the use of dynamic routing models is indicated, but for model predictive control (MPC) such models are generally regarded as infeasible because they are computationally highly demanding and thus impractical to use for receding horizon applications.This chapter focuses on the challenges and constraints of dynamic flow routing calculations for MPC.For the analysis a software framework was developed which enables MPC simulations using the dynamic sewer network model SWMM 5 (Rossman, 2008).The software provides various optimization algorithms and offers different time horizons to take into account the time span required to evaluate the optimization objectives (prediction horizon), the time span for which system input is known in advance (forecast horizon), and the time span for which control devices have to be optimized (control horizon).For the formulation of control objectives, parameters representing flow and water quality conditions can be used.In the generated MPC framework, modules for optimization and flow simulation are separate, leading to a text-based parameter optimization procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".