Sedimentation is the most common and effectively practiced method of urban drainage control in terms of operating installations and duration of service. Assessing the percentage of suspended solids removed after a given detention time is essential for both design and management purposes. In previous experimental studies by some of the authors, the expression of iso-removal curves (i.e. representing the water depth where a given percentage of suspended solids is removed after a given detention time in a sedimentation column) has been demonstrated to depend on two parameters which describe particle settling velocity and flocculation factor. This study proposes an investigation of the influence of some hydrological and pollutant aggregate information of the sampled events on both parameters. The Multi-Objective (EPR-MOGA) and Multi-Case Strategy (MCS-EPR) variants of the Evolutionary Polynomial Regression (EPR) are originally used as data-mining strategies. Results are proved to be consistent with previous findings in the field and some indications are drawn for relevant practical applicability and future studies. © IWA Publishing 2012.

Data-mining approach to investigate sedimentation features in combined sewer overflows

Berardi L.;
2012

Abstract

Sedimentation is the most common and effectively practiced method of urban drainage control in terms of operating installations and duration of service. Assessing the percentage of suspended solids removed after a given detention time is essential for both design and management purposes. In previous experimental studies by some of the authors, the expression of iso-removal curves (i.e. representing the water depth where a given percentage of suspended solids is removed after a given detention time in a sedimentation column) has been demonstrated to depend on two parameters which describe particle settling velocity and flocculation factor. This study proposes an investigation of the influence of some hydrological and pollutant aggregate information of the sampled events on both parameters. The Multi-Objective (EPR-MOGA) and Multi-Case Strategy (MCS-EPR) variants of the Evolutionary Polynomial Regression (EPR) are originally used as data-mining strategies. Results are proved to be consistent with previous findings in the field and some indications are drawn for relevant practical applicability and future studies. © IWA Publishing 2012.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11564/706513
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