Hydraulic and Structural Analysis of Complex Cross-Section Reinforced Concrete Pipes to Improve Sewage Flow in a Combined Sewer System

A complex cross-section reinforced concrete pipe that combines a sub-pipe for the flow of sewage in dry weather and a main pipe for the flow of rainwater was developed to reduce sedimentation of the combined sewer system in dry weather. The sub-pipe was designed, considering the flow velocity, constructability, and maintenance. By fitting the sewage data in the dry weather to the normal distribution, the ratio of the cross-sectional area of sewage flow to that of the pipe was determined to be approximately 0.418, which could cover 99.85% of the sewage volume of the target site. Based on this ratio, the diameter of the sub-pipe corresponding to the combined sewer system with a pipe diameter between 450 and 1300 mm was determined. The hydraulic performance analysis results showed that the flow velocity increased by 11 to 12% compared to the circular pipe based on the full sub-pipe and by more than 15% depending on the water level. The shear stress increased by more than 16.5%, and higher tractive force was observed. Structural safety was determined as the crack load and failure load far exceeded the minimum criteria, thereby verifying the feasibility and field applicability of the complex cross-section reinforced concrete pipe.

Characterization, Spatial Variation and Management Strategy of Sewer Sediments Collected from Combined Sewer System: A Case Study in Longgang District, Shenzhen

In the urban drainage system, the formation of sewer sediments is inevitable, and the removal of sewer sediments is necessary for system maintenance. Disposal of arisings from sewer sediment removal is becoming a serious environmental issue. The current knowledge of sewer sediments is limited, which is restrained to sewer sediments management. To better understand this municipal waste, the sewer sediments of a combined sewer system in Longgang District, Shenzhen were collected and characterized, and the spatial distribution characteristics of contaminants were analyzed. Based on the bivariate correlation analysis, it is found that many contaminants in sewer sediments have a strong relationship with spatial variables. Compared to the sewer sediments in industrial areas, those in residential areas contain higher concentrations of Hg and phosphorus. The sediments in the sewage conduit also contain more organic matter (OM), phosphorus, Cu, and Ni, and the sediments in the rainwater conduit contain a higher concentration of Cd. Moreover, the sediments produced in different catchments also show huge differences in the content of contaminants. These spatial distribution characteristics may provide help for the further classification of sewer sediments, thereby making the disposal of sediments more targeted. According to the local standards of sludge disposal, land application and incineration are not suitable for managing sewer sediments due to the low OM content and poor lower heating value (LHV). Although sanitary landfill is feasible for sewer sediments disposal, the complicated composition of sewer sediments still poses the risk of polluting the surrounding environment. The management of sewer sediments via the production of building materials is a promising technical route that can avoid the migration of hazardous contaminants and produce valuable products. This study may improve our understanding of sewer sediments and provide a reliable recommendation for sewer sediment management.

Sediment Level Prediction of a Combined Sewer System Using Spatial Features

The prediction of sediment levels in combined sewer system (CSS) would result in enormous savings in resources for their maintenance as a reduced number of inspections would be needed. In this paper, we benchmark different machine learning (ML) methodologies to improve the maintenance schedules of the sewerage and reduce the number of cleanings using historical sediment level and inspection data of the combined sewer system in the city of Barcelona. Two ML methodologies involve the use of spatial features for sediment prediction at critical sections of the sewer, where the cost of maintenance is high because of the dangerous access; one uses a regression model to predict the sediment level of a section, and the other one a binary classification model to identify whether or not a section needs cleaning. The last ML methodology is a short-term forecast of the possible sediment level in future days to improve the ability of operators to react and solve an imminent sediment level increase. Our study concludes with three different models. The spatial and short-term regression methodologies accomplished the best results with Artificial Neural Networks (ANN) with 0.76 and 0.61 R2 scores, respectively. The classification methodology resulted in a Gradient Boosting (GB) model with an accuracy score of 0.88 and an area under the curve (AUC) of 0.909.

Air flow model development and application in a complex combined sewer system

A steady-state air flow model was developed and applied in a complex combined sewer system in the city of Edmonton, Alberta, Canada. The model solves the continuity at each junction and the momentum equation for the links coupled with dropshaft and other manholes. The dropshaft pressure gradient is computed using the dropshaft equation and air flow rate through manhole pickholes is computed considering the opening as an orifice. A leakage factor is used as a calibration parameter to represent the area through which air can leak from the manholes into the neighborhood. The model uses an iterative solution algorithm with a forward sweep for the continuity and backward sweep for the momentum equation. An underrelaxation is applied to update pressure in each iteration for model stability. The model was calibrated and validated by using the measured air flow rate and manhole pressure in the sewer network, with good results. An analysis of the air flow characteristics shows that a significant amount of air is brought into the system due to a large headspace in the upstream trunk but over 70% of this air is released into the neighborhood due to reduced headspace in the downstream trunk.

Attribute-based intervention development for increasing resilience of urban drainage systems

Resilience building commonly focuses on attributes such as redundancy. Whilst this may be effective in some cases, provision of specific attributes does not guarantee resilient performance and research is required to determine the suitability of such approaches. This study uses 250 combined sewer system virtual case studies to explore the effects of two attribute-based interventions (increasing distributed storage and reducing imperviousness) on performance-based resilience measures. These are found to provide improvement in performance under system failure in the majority of case studies, but it is also shown that attribute-based intervention development can result in reduced resilience.