920 resultados para RESIDENCE TIME DISTRIBUTION
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Day/night variations in the size distribution of the particulate matter >0.15 mm (PM) were studied in May 1995 during the DYNAPROC time-series cruise in the northwestern Mediterranean Sea. Data on vertical distributions of PM (>0.15 mm) and zooplankton were collected with the Underwater Video Profiler (UVP). The comparisons of the UVP data with plankton net data and POC data from water bottles indicated that more than 97% of the particles detected by the UVP were non-living particles (0.15 mm) and that the PM contributed 4-34% of the total dry weight measured on GF/F filters. Comparison of seven pairs of day and night vertical profiles performed during the cruise showed that in the upper 800 m, the mean size and the volume of particles was higher at night than during the day. During the night, the integrated volume of the PM increased on average by 32±20%. This increase corresponded to a shift of smaller size classes (<0.5 mm) towards the larger ones (>0.5 mm). During the day, the pattern was reversed, and the quantity of PM >0.5 mm decreased. During the study period, the standing stock of PM (60-800 m) decreased from 7.5 to less than 2 g m?2 but the diel variations persisted, except for two short periods in the superficial layer following a wind event. The cyclic feeding activity induced by the diel vertical migration of zooplankton could be the best candidate to explain the observed diel fluctuations in the size classes of PM in the water column. However, our results also suggest that in the upper layer additional driving forces such as the increase of the level of turbulence after a wind event or the modification of the zoo- and phytoplankton community can influence the PM temporal evolution.
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The research work presented in the thesis describes a new methodology for the automated near real-time detection of pipe bursts in Water Distribution Systems (WDSs). The methodology analyses the pressure/flow data gathered by means of SCADA systems in order to extract useful informations that go beyond the simple and usual monitoring type activities and/or regulatory reporting , enabling the water company to proactively manage the WDSs sections. The work has an interdisciplinary nature covering AI techniques and WDSs management processes such as data collection, manipulation and analysis for event detection. Indeed, the methodology makes use of (i) Artificial Neural Network (ANN) for the short-term forecasting of future pressure/flow signal values and (ii) Rule-based Model for bursts detection at sensor and district level. The results of applying the new methodology to a District Metered Area in Emilia- Romagna’s region, Italy have also been reported in the thesis. The results gathered illustrate how the methodology is capable to detect the aforementioned failure events in fast and reliable manner. The methodology guarantees the water companies to save water, energy, money and therefore enhance them to achieve higher levels of operational efficiency, a compliance with the current regulations and, last but not least, an improvement of customer service.
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Chiefly tables.
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Reprint. Originally published: Milwaukee : Daily Wisconsin Printing House, 1866.
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First edition, London, 1793-94.
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Mode of access: Internet.