On the Issue of the Ecological State of Storm Wastewater in an Urbanized Territory

The article raises the issue of the ecological state of storm wastewater, their technical condition. The issues of assessing the functioning of storm sewers are included in the list of municipal environmental control, which is carried out by the executive authorities in the field of ecology and nature management. The research paper outlines the key problems associated with underfunding of the current work to ensure the efficient operation of storm water. The main categories in the system “treatment facilities – storm drains – collectors” are considered. The traditional stormwater runoff system involves the diversion of surface runoff through open water bodies (e.g. river systems). The main environmental indicators to be achieved during the construction and commissioning of a complex of sewage storm treatment facilities have been analyzed. The analysis of the design indicators showed that a total of 18.9 million m3 of storm water, rainwater runoff and drainage waters are subject to treatment. The measures for the installation of sewage treatment plants, storm sewers will prevent the river Volga from entering of more than 22 thousand tons of suspended solids, including heavy metals and 1.2 thousand tons of oil products. According to the project documentation, the construction of more than 50 facilities will allow upgrading the stormwater drainage system of the city of Volgograd within the framework of the National Project “Ecology”. The conclusions reflected in the article reveal the main reasons for the current situation: inconsistency of repair and restoration work to replace parts, assemblies; insufficient amount of technical re-equipment; inconsistency of the chemical composition of wastewater with the established regulatory environmental indicators; insufficient municipal control of the services responsible for this object; insufficient financial support.

Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers

ABSTRACT The concept of sediment transport at the limit of deposition in storm sewers represents one operational condition that avoid deposition of sediments maintaining the discharge capacity of the pipes. In this study, this condition was analyzed applying one Artificial Neural Network Multilayer Perceptron (ANN-MLP) model to predict the volumetric concentration at the limit of deposition, using 544 experimental data from literature. It was evaluated different input variables combinations and model configurations, showing the sensitivity of the model with these changes. Through this study, it was demonstrated that the proposed model outperforms the existing equations, leading to more assertive predictions in the determination of volumetric concentrations at the limit of deposition, resulting in values of R2 = 0.92, Mean Absolute Percentage Error (MAPE) = 35.09 % and Mean Average Error (MAE) = 59.84 ppm. With the performed analysis, the study selects one equation to be used for extrapolations when determining the volumetric concentration at the limit of deposition in storm sewers. The selected equation is superior due to its theoretical basis. This work includes one more concept to a better methodology in obtaining the conditions of the flow at the limit of deposition.

New formulations for prediction of velocity at limit of deposition in storm sewers based on a stochastic technique

Sedimentation in storm sewers strongly depends on velocity at limit of deposition. This study provides application of a novel stochastic-based model to predict the densimetric Froude number in sewer pipes. In this way, the generalized likelihood uncertainty estimation (GLUE) is used to develop two parametric equations, called GLUE-based four-parameter and GLUE-based two-parameter (GBTP) models to enhance the prediction accuracy of the velocity at the limit of deposition. A number of performance indices are calculated in training and testing phases to compare the developed models with the conventional regression-based equations available in the literature. Based on the obtained performance indices and some graphical techniques, the research findings confirm that a significant enhancement in prediction performance is achieved through the proposed GBTP compared with the previously developed formulas in the literature. To make a quantified comparison between the established and literature models, an index, called improvement index (IM), is computed. This index is a resultant of all the selected indices, and this indicator demonstrates that GBTP is capable of providing the most performance improvement in both training () and testing () phases, comparing with a well-known formula in this context.