Robust model for estimating pumping station characteristics and sewer flows from standard pumping station data
Flow data represent crucial input for reliable diagnostics of sewer functions and identification of potential problems such as unwanted inflow and infiltration. Flow estimates from pumping stations, which are an integral part of most separate sewer systems, might help in this regard. A robust model...
| Veröffentlicht in: | Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 79(2019), 9 vom: 26. Mai, Seite 1739-1745 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2019
|
| Zugriff auf das übergeordnete Werk: | Water science and technology : a journal of the International Association on Water Pollution Research |
| Schlagworte: | Journal Article Sewage Water 059QF0KO0R |
| Zusammenfassung: | Flow data represent crucial input for reliable diagnostics of sewer functions and identification of potential problems such as unwanted inflow and infiltration. Flow estimates from pumping stations, which are an integral part of most separate sewer systems, might help in this regard. A robust model and an associated optimization procedure is proposed for estimating inflow to a pumping station using only registered water levels in the pump sump and power consumption. The model was successfully tested on one month of data from a single upstream station. The model is suitable for identification of pump capacity and volume thresholds for switching the pump on and off. These are parameters which are required for flow estimation during periods with high inflows or during periods with flow conditions triggering pump switching on and off at frequencies close to the temporal resolution of monitored data. The model is, however, sensitive within the transition states between emptying and filling to observation errors in volume and on inflow/outflow variability |
|---|---|
| Beschreibung: | Date Completed 13.08.2019 Date Revised 15.12.2020 published: Print Citation Status MEDLINE |
| ISSN: | 0273-1223 |
| DOI: | 10.2166/wst.2019.176 |