Abstract
Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated. For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers proposed an alternative method for sewer sediment accumulation calculation using predictive models harnessing multiple linear regression model (MLRM) and artificial neural network (ANN). AL-Thawra trunk sewer in Baghdad city is selected as a case study area; data from a survey done on this trunk is used in the modeling process. Results showed that MLRM is acceptable, with an adjusted coefficient of determination (adj. R2) in order of 89.55%. ANN model found to be practical with R2 of 82.3% and fit the data better throughout its range. Sensitivity analysis showed that the flow is the most influential parameter on the depth of sediment deposition.
Highlights
In-sewer sediment is getting more scientific and operational interest in the last decade [1]; Sediments disposition in combined sewers is a critical aspect as it is a primary cause of several hydraulic and environmental problems such as blockage, reduction in hydraulic capacity, increase in the flooding frequency, sewer wall corrosion, shock loads to wastewater treatment plants and, erosion and resuspension of the deposited solids during wet weather flow (WWF) [2]
Hannouche et al [4] assert that the contribution of the sewer sediment resuspension to the total TSS of the wastewater is in order of 20-80%
Depending on the their characteristics, Crabtree [5] classified sewer sediment into five classes: Class-A, is coarser, loose, granular, mainly inorganic material found in the inverts of pipes; Class-B, Same as Class-A but the grains are found mixed with cementation agent; Class-C, mobile, smaller grain size deposits found in low-velocity regions overlaying Class-A solids; Class-D, highly organic biofilms found in sewer walls in the vicinity of the mean flow level; Class-E, small grained size sediment found in tanks
Summary
In-sewer sediment is getting more scientific and operational interest in the last decade [1]; Sediments disposition in combined sewers is a critical aspect as it is a primary cause of several hydraulic and environmental problems such as blockage, reduction in hydraulic capacity, increase in the flooding frequency, sewer wall corrosion, shock loads to wastewater treatment plants and, erosion and resuspension of the deposited solids during wet weather flow (WWF) [2]. Hannouche et al [4] assert that the contribution of the sewer sediment resuspension to the total TSS of the wastewater is in order of 20-80% Depending on the their characteristics , Crabtree [5] classified sewer sediment into five classes: Class-A, is coarser, loose, granular, mainly inorganic material found in the inverts of pipes; Class-B, Same as Class-A but the grains are found mixed with cementation agent; Class-C, mobile, smaller grain size deposits found in low-velocity regions overlaying Class-A solids; Class-D, highly organic biofilms found in sewer walls in the vicinity of the mean flow level; Class-E, small grained size sediment found in tanks. Information about structural, operational, hydraulic and environmental situations are required to improve the operation and maintenance (O&M) of the system [7].Inspection
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