Aerobic transformations of organic matters in sewer wastewater,a case study of Milan,Italy

Today, we know that not only the sewers carry urban wastewater, but also act as chemical and biological reactors, in which both organic and inorganic substances undergo significant transformations. This article describes a study on the western part of the sewer system of the city of Milan. The hydraulic model of the sewer system was coupled with the chemical and dynamic biological model to assess the extent of the degradation of organic matter in the network. The hydraulic and biological models were calibrated with real data obtained at the entrance of the wastewater treatment plant (WWTP) ‘Milan South’ and assuming standard values of daily per capita loads of COD and BOD₅.     

Statistical comparison of the performance of data-based models for sewer condition modeling

Abstract

The present article proposes a methodology to consider the uncertainty intrinsic to data-based models when comparing their performance. The goal is to provide a quantification of the variability of this type of models due to the random nature of the calibration process and enable a statistical comparison of the models’ performance when attempting to identify the best. The methodology proposed doesn’t provide an alternative metric to determine the models’ performance, but it expands the traditional deterministic comparison to a stochastic comparison. The methodology builds on the current standard approach for developing data-based model and its application is demonstrated to model sewer condition using data from 4 trunk sewers of the SANEST – Saneamento da Costa do Estoril sewer system, corresponding to 25?km of sewer pipes. The data-based models were developed using artificial neural networks, support vector machines, bootstrapping aggregation and least squares support vector machines. For the case study, the highest and average misclassification performance records are similar for all models (23% to 24% and 31% to 33%, respectively) but the lowest performance varied more significantly (39% to 62%). This demonstrates that selecting a model based on its maximum single realisation performance alone may be misleading.     

Quantification and relative comparison of different types of uncertainties in sewer water quality modeling

Quantifiable sources of uncertainty have been identified for a case study of integrated modeling of a sewer system with a more downstream wastewater treatment plant and storage sedimentation tank. The different sources were classified in model input and model-structure-related uncertainties. They were quantified and propagated towards the uncertainty in the event-based prediction of sewer emissions (flow, and physico-chemical water quality concentrations and loads). Based on the concept of variance decomposition, the total prediction uncertainty was split into the contributions of the various uncertainty sources and the different submodels. Although the results strongly depend on the water quality variable considered, it is in most general terms concluded that the uncertainty contribution by the water quality submodels is an order of magnitude higher than that for the flow submodels. Future model improvement should therefore mainly focus on water quality data collection, which would reduce current problems of spurious model calibration and verification, but also of knowledge gaps in in-sewer processes.     

USE OF A FULL-SCALE TEST FACILITY TO EXAMINE SEWER-SEDIMENT MOBILITY

The mobility of granular in-sewer sediments was measured in a study which was carried out at the National Combined Sewer Overflow Test Facility, Wigan. It is thought that these measurements are unique in that the facility permits tests using crude sewage and real in-sewer sediments to be conducted under full-scale controlled hydraulic conditions. Tests were carried out in which the bedload transport rates, resulting from different steady-flow discharges and flow depths, were measured. The results were compared with transport-rate predictions made using relationships which are recommended by the Construction Industry Research and Information Association. These comparisons indicated that, for the sewer sediments, the existing prediction methods are likely to significantly under-predict the mobility of in-sewer sediments — particularly if a range of grain sizes is present. This might have important implications both for the design of sewerage systems and for sewer flow quality modelling.     

A Survival Analysis Model for Sewer Pipe Structural Deterioration

Abstract: The structural state of sewer systems is often quantified using condition classes. The classes are based on the severity of structural defects observed on individual pipes within the system. Here, a survival analysis model was developed to predict the overall structural state of a sewer network based on camera inspection data from a sample of pipes in the system. The convolution product was used to define the survival functions for cumulative staying times in each condition class. An original calibration procedure for the sewer deterioration model was developed to overcome the censored nature of data (left censored and right censored) available for the calibration of sewer deterioration models. The exponential and Weibull functions were used to represent the distribution of waiting times in each deterioration state. Cross‐validation tests showed that the Weibull function led to greater uncertainty than the exponential function for the simulated proportion of pipes that are in a deteriorated state. Using various sample sizes for model calibration, these cross‐validation tests also showed that the model's results are robust to smaller calibration sample sizes. This confirms the model's potential for predicting the overall state of deterioration of a sewer network when only a small proportion of the pipes have been inspected.     

Quantification of energy losses at a surcharging manhole

Hydraulic models of sewer systems are commonly used to predict the risk of urban flooding. However, suitable calibration datasets in flood conditions are scarce. The quantification of energy losses within manhole structures is a current source of uncertainty within such models. To address this gap, a scaled physical manhole model is used to quantify hydraulic energy losses during surcharging and non-surcharging conditions. Two different novel configurations were tested; (1) With and without the presence of a manhole lid; (2) With and without the presence of a shallow flow on the surface. Results showed that total head losses were found to increase in surcharging conditions. The presence of the lid also marginally increased total head losses. The datasets are used to assess the performance of a numerical urban flood model (SIPSON) and comparisons highlighted that SIPSON tends to overestimate energy losses in surcharging conditions.     

Calibration of urban stormwater drainage models using hydrograph modelling

Use of mathematical models requires the estimation of model parameters, which is usually known as the calibration of the model. In general, parameter optimization is preferred in model calibration to the trial-and-error visual comparison of observed and modelled output responses, due to subjectivity and the time-consuming nature of the latter approach. An optimization procedure, called two-stage inner/outer optimization, is described in this paper, which can be used to estimate the model parameters of any urban stormwater drainage catchment modelled with any urban drainage computer modelling software. However, the ILSAX computer software was used in this study. The method is designed to provide the ‘best’ set of model parameters that consider several storm events simultaneously. Impervious area parameters are obtained from frequent ‘small’ storm events, while the pervious area parameters are obtained from less-frequent ‘large’ events. The Giralang catchment in Canberra (Australia) was used to demonstrate the method. Several ‘small’ and ‘large’ storm events of the catchment were considered in parameter optimization. Few other storm events, which were not used in model calibration, were used to validate the model parameters obtained from calibration. Results from both calibration and validation showed that the ‘best’ set of model parameters obtained for the catchment was able to produce hydrographs similar to the observed hydrographs. Pervious and impervious area parameters obtained from calibration agreed well with the information gathered from other sources such as aerial photographs and published literature.     

Sewer deterioration modeling with condition data lacking historical records

Accurate predictions of future conditions of sewer systems are needed for efficient rehabilitation planning. For this purpose, a range of sewer deterioration models has been proposed which can be improved by calibration with observed sewer condition data. However, if datasets lack historical records, calibration requires a combination of deterioration and sewer rehabilitation models, as the current state of the sewer network reflects the combined effect of both processes. Otherwise, physical sewer lifespans are overestimated as pipes in poor condition that were rehabilitated are no longer represented in the dataset. We therefore propose the combination of a sewer deterioration model with a simple rehabilitation model which can be calibrated with datasets lacking historical information. We use Bayesian inference for parameter estimation due to the limited information content of the data and limited identifiability of the model parameters. A sensitivity analysis gives an insight into the model's robustness against the uncertainty of the prior. The analysis reveals that the model results are principally sensitive to the means of the priors of specific model parameters, which should therefore be elicited with care. The importance sampling technique applied for the sensitivity analysis permitted efficient implementation for regional sensitivity analysis with reasonable computational outlay. Application of the combined model with both simulated and real data shows that it effectively compensates for the bias induced by a lack of historical data. Thus, the novel approach makes it possible to calibrate sewer pipe deterioration models even when historical condition records are lacking. Since at least some prior knowledge of the model parameters is available, the strength of Bayesian inference is particularly evident in the case of small datasets.     

Computational fluid dynamics based assessment of discharge-water depth relationships for combined sewer overflows

This article describes a general method to assess discharge-water depth relationships for combined sewer overflow (CSO) chambers. This method is particularly suitable for: complex geometries, unavailable univoc relationship between upstream and downstream flow and complex downstream hydraulic conditions. The methodology presented in this article is based on computational fluid dynamics. Its aim is to propose a relationship between the water depths in the CSO chamber and the overflow discharge. This study focuses on the evaluation and integration of the uncertainties in the determination of the most suitable depth-overflow relationship. Uncertainties related to numerical model, boundary conditions and water depth have been taken into account in order to elaborate water depth-overflow rate relationship. This approach is illustrated through the example of the ‘Milan’ CSO of Mulhouse city, France.     

A Fuzzy Expert System for Prioritizing Rehabilitation of Sewer Networks

Abstract: Rehabilitation of sewer networks is a huge and very costly global problem that has often been treated on a crisis‐based approach. The development of a rehabilitation program requires models and tools for assessing the condition and performance of sewers. The original contribution of this study is the development of a ranking scheme for sewer rehabilitation priorities. A fuzzy expert system was applied with inputs from a combined assessment of hydraulic, structural performance and potential failure consequences. The fuzzy structural system computes the global structural performance index for each pipe using internal condition, surrounding condition, and site vulnerability (SV) as inputs. The fuzzy hydraulic system uses hydraulic performance index (HPI), hydraulic performance impact, and SV to compute the global HPI. Finally, the fuzzy global system uses all these factors to compute the global performance index for each pipe. This methodology was successfully applied to the sewer system of the City of Laval in Canada. The results show how the fuzzy inference system may be used to establish rehabilitation priorities for each pipe section. The fuzzy expert system provides more realistic results than the intuitive approaches that use structural and hydraulic performance maximum and mean.     

Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

Sanitary sewer systems are designed to collect and transport sanitary wastewater and stormwater. Pipe inspection is important in identifying both the type and location of pipe defects to maintain the normal sewer operations. Closed-circuit television (CCTV) has been commonly utilized for sewer pipe inspection. Currently, interpretation of the CCTV images is mostly conducted manually to identify the defect type and location, which is time-consuming, labor-intensive and inaccurate. Conventional computer vision techniques are explored for automated interpretation of CCTV images, but such process requires large amount of image pre-processing and the design of complex feature extractor for certain cases. In this study, an automated approach is developed for detecting sewer pipe defects based on a deep learning technique namely faster region-based convolutional neural network (faster R-CNN). The detection model is trained using 3000 images collected from CCTV inspection videos of sewer pipes. After training, the model is evaluated in terms of detection accuracy and computation cost using mean average precision (mAP), missing rate, detection speed and training time. The proposed approach is demonstrated to be applicable for detecting sewer pipe defects accurately with high accuracy and fast speed. In addition, a new model is constructed and several hyper-parameters are adjusted to study the influential factors of the proposed approach. The experiment results demonstrate that dataset size, initialization network type and training mode, and network hyper-parameters have influence on model performance. Specifically, the increase of dataset size and convolutional layers can improve the model accuracy. The adjustment of hyper-parameters such as filter dimensions or stride values contributes to higher detection accuracy, achieving an mAP of 83%. The study lays the foundation for applying deep learning techniques in sewer pipe defect detection as well as addressing similar issues for construction and facility management.     

Laboratory Investigation into the Performance of an In-Sewer Vortex Flow Regulator

Aspects of the hydraulic performance of an in-sewer vortex regulator have been studied. These devices are increasingly used in stormwater management schemes, but their characteristics under a variety of prototypical situations have not been widely disseminated. The study used a full-size laboratory mockup of a section of sewer and associated manhole with facilities for measuring discharges of up to 60 1/s. The performance of a typical vortex flow regulator was investigated under both steady and unsteady flow conditions. The steady head-discharge relationship revealed the expected 'kick back'region (during rising head); however, this was less pronounced during falling head. Under unsteady conditions, vortex initiation was affected and a hysteresis effect was noted. The influence of downstream surcharge was significant at lower upstream heads. Theoretical comparison showed that to obtain equivalent performance, an orifice of 62% of the diameter of the vortex regulator would be required, and 25% savings in storage could be made.     

Selecting a series of storm events for a model-based assessment of combined sewer overflows

The hydraulic verification of combined sewer systems as well as the assessment of combined sewer overflows (CSOs) can be conducted using a hydrodynamic model. Unfortunately, long-term simulations with hydrodynamic models for the assessment of CSOs can cause unacceptably long computation times. Using only a series of storm events instead of a precipitation continuum can reduce this time and enables parallel simulation of single storm events. We introduce a method to select this series of storm events. The method’s parameters have been optimized to replicate the overflow volume of the continuous simulation and to minimize the overall computation time. This optimization revealed a generally applicable parameter set that results in series of storm events that are shorter than the precipitation continuum by 86.2–95.2% for the investigated cases. Additionally, the deviation of overflow volume between continuous simulation and series simulation ranges between only 0.1% and 4.1%.     

The potential to retrofit sustainable drainage systems to address combined sewer overflow discharges in the Thames Tideway catchment

Experience of retrofitting sustainable drainage systems (SuDS) in the United Kingdom is limited, and there are no well‐established procedures for evaluating the feasibility, value or cost‐effectiveness of doing this, particularly at the catchment scale. This paper demonstrates a two‐phase process for evaluating the potential to retrofit SuDS to address combined sewer discharges in three subcatchments within the Thames Tideway catchment of London. The first phase evaluates what might be achieved with various levels of disconnection (‘global’ disconnection scenarios) using hydraulic models, while the second phase considers how disconnection might practically be achieved. High levels of disconnection are technically possible but practicably difficult. In selected cases, and with aggressive implementation of SuDS, combined sewer overflow CSO discharges could potentially be eliminated or reduced to acceptable levels without the need for any modifications to underground assets. However, retrofit SuDS could not eliminate the requirement for some form of sewer modification in any subcatchments.     

Energy optimization of the urban drainage system by integrated real-time control during wet and dry weather conditions

An extension of the frequently applied equal filling degree control algorithm is proposed for integrated control of sewer system and wastewater treatment plant (WWTP). Under dry weather conditions (DWC), this control synchronizes sewer storage volume activation at pumping stations (PS) throughout the sewer system with the intermittent aeration at the WWTP. This is in contrast with the frequently advocated influent load equalization at WWTPs. The concept is demonstrated on a case study using an integrated system composed of detailed models for sewer system and WWTP where it reveals considerable potential for energy savings and effluent quality improvement. Moreover, the integrated control allows for the reduction of combined sewer overflows during wet weather events and decreased sedimentation potential in the sewer system. Neither weather predictions nor structural changes at the WWTP or in the sewer system are required. The control concept is applicable to about 50 Flemish WWTPs.     

计算机水力学模型在城市排水管理中的应用

本文介绍计算机水力学模型在城市排水管理领域应用的初步探索。应用实例说明 :计算机水力学模型可在排水管网数据管理、排水管网瓶颈的诊断、运行管理策略的评价、优化运行策略研究等诸方面具有实际应用价值。     

Sustainability assessment in strategic management of wastewater transport system: a case study in Trondheim,Norway

Sewer networks represent high value in water infrastructure assets and it is important to develop and operate them by specified sustainable management. This paper presents the results of a sustainability analysis on the wastewater transport system of Trondheim, Norway, for future planning (2014–2040) from a metabolism-based performance analysis by the Dynamic Metabolism Model (DMM). The aim of this work is to demonstrate a methodology for comparing different pathways toward a sustainable management of wastewater systems. For this purpose, four intervention strategies ‘infiltration and inflow reduction’, ‘increasing rehabilitation rate’, ‘extension of system regarding population growth’, ‘energy management’ along with different combinations of them have been analysed. The results of this study may give some support to decision-makers in wastewater departments. In practice, to achieve strategic level planning of sustainable sewer asset management, it is vital to assess different aspects of sustainability and manage them in a comprehensive system.     

Evaluation of calibration efficacy under different levels of uncertainty

This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.     

Validation of 2D shock capturing flood models around a surcharging manhole

This work offers a detailed validation of finite volume (FV) flood models in the case where horizontal floodplain flow is affected by sewer surcharge flow via a manhole. The FV numerical solution of the 2D shallow water equations is considered based on two approximate Riemann solvers, HLLC and Roe, on both quadrilateral structured and triangular unstructured mesh-types. The models are validated against a high resolution experimental data-set obtained using a physical model of a sewer system linked to a floodplain via a manhole. It was verified that the sensitivity of the models is inversely proportional to the surcharged flow/surface inflow ratio, and therefore requires more calibration from the user especially when concerned with localised modelling of sewer-to-floodplain flow. Our findings provide novel evidence that shock capturing FV-based flood models are applicable to simulate localised sewer-to-floodplain flow interaction.