DeePC Sensitivity for Pressure Control with Pressure-Reducing Valves (PRVs) in Water Networks.
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| Název: | DeePC Sensitivity for Pressure Control with Pressure-Reducing Valves (PRVs) in Water Networks. |
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| Autoři: | Davda, Jason, Ostfeld, Avi |
| Zdroj: | Water (20734441); Jan2026, Vol. 18 Issue 2, p253, 22p |
| Témata: | PRESSURE control, SENSITIVITY analysis, RELIEF valves, DEVIATION (Statistics), MATHEMATICAL optimization, ENVIRONMENTAL infrastructure, FLUID dynamics |
| Geografický termín: | MODENA (Italy) |
| Abstrakt: | This study provides a practice-oriented sensitivity analysis of DeePC for pressure management in water distribution systems. Two public benchmark systems were used, Fossolo (simpler) and Modena (more complex). Each run fixed a monitored node and pressure reference, applied the same randomized identification phase followed by closed-loop control, and quantified performance by the mean absolute error (MAE) of the node pressure relative to the reference value. To better characterize closed-loop behavior beyond MAE, we additionally report (i) the maximum deviation from the reference over the control window and (ii) a valve actuation effort metric, normalized to enable fair comparison across different numbers of valves and, where relevant, different control update rates. Motivated by the need for practical guidance on how hydraulic boundary conditions and algorithmic choices shape DeePC performance in complex water networks, we examined four factors: (1) placement of an additional internal PRV, supplementing the reservoir-outlet PRVs; (2) the control time step (Δ t) ; (3) a uniform reservoir-head offset (Δ h) ; and (4) DeePC regularization weights (λ g , λ u , λ y) . Results show strong location sensitivity, in Fossolo, topologically closer placements tended to lower MAE, with exceptions; the baseline MAE with only the inlet PRV was 3.35 [m], defined as a DeePC run with no additions, no extra valve, and no changes to reservoir head, time step, or regularization weights. Several added-valve locations improved the MAE (i.e., reduced it) below this level, whereas poor choices increased the error up to ~8.5 [m]. In Modena, 54 candidate pipes were tested, the baseline MAE was 2.19 [m], and the best candidate (Pipe 312) achieved 2.02 [m], while pipes adjacent to the monitored node did not outperform the baseline. Decreasing Δ t across nine tested values consistently reduced MAE, with an approximately linear trend over the tested range, maximum deviation was unchanged (7.8 [m]) across all Δ t cases, and actuation effort decreased with shorter steps after normalization. Changing reservoir head had a pronounced effect: positive offsets improved tracking toward a floor of ≈0.49 [m] around Δ h ≈ +30 [m], whereas negative offsets (below the reference) degraded performance. Tuning of regularization weights produced a modest spread (≈0.1 [m]) relative to other factors, and the best tested combination (λy, λg, λu) = (102, 10−3, 10−2) yielded MAE ≈ 2.11 [m], while actuation effort was more sensitive to the regularization choice than MAE/max deviation. We conclude that baseline system calibration, especially reservoir heads, is essential before running DeePC to avoid biased or artificially bounded outcomes, and that for large systems an external optimization (e.g., a genetic-algorithm search) is advisable to identify beneficial PRV locations. [ABSTRACT FROM AUTHOR] |
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| Databáze: | Biomedical Index |
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