Artificial Neural Networks for the prediction of the trapping efficiency of a new sewer overflow screening device
Creators: name: Aziz, M. A.
name: Imteaz, Monzur
name: Choudhury, Tanzeem
name: Phillip, D. I.
Publisher: name: Swinburne
Year: 2024

description: Some of the major concerns regarding sewer overflows to receiving water bodies include serious environmental, aesthetic and public health problems. Water management authorities are increasingly receiving public complaints that have led engineers to focus on means of retaining the entrained sewer solids within the sewer system during overflow events. During wet weather conditions, sewer overflows to receiving water bodies raise serious concern to environmental and community health concerns. To address these problems, different types of screening devices are used. Moreover, floatable control is preferred by most of the proposed and existing environmental regulations. This requirement triggers the need to research the different types of screening devices and screenings handling systems to select the most appropriate for a particular installation especially at unmanned locations. In the present study the sewer overflow device consists of a rectangular tank and a sharp crested weir that are followed by series of vertical parallel combs to separate entrained sewer solids from the overflow. The device does not require electrical or mechanical power for the self-cleansing mechanism, enabling the device to work efficiently in unmanned locations. Extensive laboratory investigations are underway to assess the effectiveness of a novel self-cleansing sewer overflow screening device. A series of laboratory tests to determine trapping efficiencies for common sewer solids were conducted for different flow conditions, number of combs layers, spacing of combs and weir crest lengths. Sewer solids from different density materials make sewer flow to analyze in complex Non-Newtonian fluid system with huge computational cost and complicity using physical law based modeling. On the flipside artificial neural model has the capacity to accurately predict the outcome of complex, non-linear physical systems with relatively poorly understood physicochemical processes which makes them highly desirable in the present study. Artificial Neural Networks (ANN) have already been successfully used to simulate flood forecasting in urban drainage system, real time control in combined sewer system, real time water level predictions of sewerage systems covering gauged and un-gauged sites etc. In case of sewer solid capture efficiency: neural network modeling is able to recognize nonlinear input output relations with adapting approach for changing circumstances. In the present study, feed forward artificial neural networks using back propagation algorithms were used, as such networks have been used almost exclusively in environmental modeling. A series of forty seven (47) sets of experimental data were collected to train (calibrate) the ANN model. In addition to these, eight (8) sets of experimental data were collected to validate the trained ANN network to be used in wider prospective of urban drainage conditions. The major areas covered in the ANN modeling include selection of input and output variables, optimization of the model, consideration of different learning algorithms, designing ANN’s training &amp; cross training processes and model validation. In the studied case, complex physical characteristics of different sewer solids, together with multi-fluid sewer system with variable flow phenomena makes it difficult to model with physical considerations. In case of sewer solid capture efficiency; artificial neural network modeling is able to learn the complex input-output relations with adapting approach for changing circumstances. Model considered different learning algorithms, diverse hidden layer structure with varied training samples to optimize the network. It is found that the model can successfully predict the experimental results with average absolute percentage errors varying from 4 to 7 percent.