Infiltration and inflow to wastewater sewer systems - A literature review on risk management and decision support

Wastewater sewer systems are one of our largest infrastructural assets. By transporting the sewage from our homes and other facilities to the wastewater treatment plants, the sewer systems protect public health, properties, and the environment. However, in addition to the sanitary sewage, there is an infiltration and inflow (I/I) of other water to the sewer system. This additional load can result in adverse effects such as basement flooding, combined sewer overflows, and larger pumping and treatment costs. I/I can originate from rainfall but also from sources such as groundwater, surface water or leaking drinking water pipes. Expected climate change effects include more intense rain events and periods of higher water levels which will increase the problem of I/I. Hence, it is important to manage I/I in a proper way by implementing efficient measures that provide the largest societal gain from a sustainability point of view.This literature review was performed to form a basis for research on developing risk-based decision support models to evaluate I/I in wastewater sewer systems from a system perspective and with focus on sustainability. It reviews publications on I/I focusing on sources, impacts, quantification and mitigation measures, addresses risk definitions, and the risk management process. Further, common decision support methods are described and literature on decision support models to evaluate I/I are reviewed. Important conclusions are that a vast amount of literature exists on finding and reducing I/I from a technical point of view and in several publications different decision support models are used to evaluate measures aiming at reducing I/I. However, existing models are focused on project internal and financial aspects and a need for future studies is identified, evaluating I/I from a broader societal and sustainability perspective, including project external, environmental, and social criteria

A Methodology for the Identification of Critical Locations in Infrastructures

The extreme importance of critical infrastructures to modern society is widely recognized. These infrastructures are complex, interdependent, and ubiquitous; they are sensitive to disruptions that can lead to cascading failures with serious consequences. Protecting the critical infrastructures from terrorism, human generated malevolent attack directed toward maximum social disruption, presents an enormous challenge. Recognizing that society cannot afford the costs associated with absolute protection, it is necessary to identify the critical locations in these infrastructures. By protecting the critical locations society achieves the greatest benefit for the protection investment. This paper presents a methodology for the identification of critical locations in infrastructures. The framework models the infrastructures as interconnected digraphs and employs graph theory and reliability theory to identify the vulnerable points. The vulnerable points are screened for their susceptibility to a terrorist attack, and a prioritized list of critical locations is produced. The prioritization methodology is based on multi-attribute utility theory. The methodology is illustrated through the presentation of a portion on the analysis conducted on the campus of the Massachusetts Institute of Technology

Optimal sensor placement for sewer capacity risk management

2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research

A Grey Theory Based Approach to Big Data Risk Management Using FMEA

Big data is the term used to denote enormous sets of data that differ from other classic databases in four main ways: (huge) volume, (high) velocity, (much greater) variety, and (big) value. In general, data are stored in a distributed fashion and on computing nodes as a result of which big data may be more susceptible to attacks by hackers. This paper presents a risk model for big data, which comprises Failure Mode and Effects Analysis (FMEA) and Grey Theory, more precisely grey relational analysis. This approach has several advantages: it provides a structured approach in order to incorporate the impact of big data risk factors; it facilitates the assessment of risk by breaking down the overall risk to big data; and finally its efficient evaluation criteria can help enterprises reduce the risks associated with big data. In order to illustrate the applicability of our proposal in practice, a numerical example, with realistic data based on expert knowledge, was developed. The numerical example analyzes four dimensions, that is, managing identification and access, registering the device and application, managing the infrastructure, and data governance, and 20 failure modes concerning the vulnerabilities of big data. The results show that the most important aspect of risk to big data relates to data governance

A multi-objective approach to incorporate indirect costs into optimisation models of waterborne sewer systems

Thesis (MScEng (Civil Engineering))--University of Stellenbosch, 2011.ENGLISH ABSTRACT: Waterborne sewage system design and expansion objectives are often focused on minimising initial investment while increasing system capacity and meeting hydraulic requirements. Although these objectives make good sense in the short term, the solutions obtained might not represent the optimal cost-effective solution to the complete useful life of the system. Maintenance and operation of any system can have a significant impact on the life-cycle cost. The costing process needs to be better understood, which include maintenance and operation criteria in the design of a sewer system. Together with increasing public awareness regarding global warming and environmental degradation, environmental impact, or carbon cost, is also an important factor in decisionmaking for municipal authorities. This results in a multiplicity of different objectives, which can complicate the decisions faced by waterborne sewage utilities. Human settlement and migration is seen as the starting point of expansion problems. An investigation was conducted into the current growth prediction models for municipal areas in order to determine their impact on future planning and to assess similarities between the models available. This information was used as a platform to develop a new method incorporating indirect costs into models for planning waterborne sewage systems. The need to balance competing objectives such as minimum cost, optimal reliability, and minimum environmental impact was identified. Different models were developed to define the necessary criteria, thus minimising initial investment, operating cost and environmental impact, while meeting hydraulic constraints. A non-dominated sorting genetic algorithm (NSGA-II) was applied to certain waterborne sewage system (WSS) scenarios that simulated the evolutionary processes of genetic selection, crossover, and mutation to find a number of suitable solutions that balance all of the given objectives. Stakeholders could in future apply optimisation results derived in this thesis in the decision making process to find a solution that best fits their concerns and priorities. Different models for each of the above-mentioned objectives were installed into a multi-objective NSGA and applied to a hypothetical baseline sewer system problem. The results show that the triple-objective optimisation approach supplies the best solution to the problem. This approach is currently not applied in practice due to its inherent complexities. However, in the future this approach may become the norm.AFRIKAANSE OPSOMMING: Spoelafvoering rioolstelsel ontwerp en uitbreiding doelwitte is dikwels gefokus op die vermindering van aanvanklike belegging, terwyl dit die verhoging van stelsel kapasiteit insluit en ook voldoen aan hidrouliese vereistes. Alhoewel hierdie doelwitte goeie sin maak in die kort termyn, sal die oplossings verkry dikwels nie die optimale koste-effektiewe oplossing van die volledige nuttige lewensduur van die stelsel verteenwoordig nie. Bedryf en instandhouding van 'n stelsel kan 'n beduidende impak op die lewensiklus-koste hê, en die kostebepalings proses moet beter verstaan word en die nodige kriteria ingesluit word in die ontwerp van 'n rioolstelsel. Saam met 'n toenemende openbare bewustheid oor aardverwarming en die agteruitgang van die omgewing, is omgewingsimpak, of koolstof koste, 'n belangrike faktor in besluitneming vir munisipale owerhede. As gevolg hiervan, kan die diversiteit van die verskillende doelwitte die besluite wat munisipale besluitnemers in die gesig staar verder bemoeilik. Menslike vestiging en migrasie is gesien as die beginpunt van die uitbreiding probleem. 'n Ondersoek na die huidige groeivoorspelling modelle vir munisipale gebiede is van stapel gestuur om hul impak op die toekomstige beplanning te bepaal, en ook om die ooreenkomstes tussen die modelle wat beskikbaar is te asesseer. Hierdie inligting is gebruik as 'n platform om ‘n nuwe metode te ontwikkel wat indirekte kostes inkorporeer in die modelle vir die beplanning van spoelafvoer rioolstelsels. Die behoefte is geïdentifiseer om meedingende doelwitte soos minimale aanvanklike koste, optimale betroubaarheid en minimum invloed op die omgewing te balanseer. Verskillende modelle is ontwikkel om die bogenoemde kriteria te definiëer, in die strewe na die minimaliseering van aanvanklike belegging, bedryfskoste en omgewingsimpak, terwyl onderhewig aan hidrouliese beperkinge. ‘n Nie-gedomineerde sorteering genetiese algoritme (NSGA-II), istoegepas op sekere spoelafvoering rioolstelsel moontlikhede wat gesimuleerde evolusionêre prosesse van genetiese seleksie, oorplasing, en mutasie gebruik om 'n aantal gepaste oplossings te balanseer met inagname van al die gegewe doelwitte. Belanghebbendes kan in die toekoms gebruik maak van die resultate afgelei in hierdie tesis in besluitnemings prosesse om die bes-passende oplossing vir hul bekommernisse en prioriteite te vind. Verskillende modelle vir elk van die bogenoemde doelwitte is geïnstalleer in die nie-gedomineerde sorteering genetiese algoritme en toegepas op 'n hipotetiese basislyn rioolstelsel probleem. Die resultate toon dat die drie-objektief optimalisering benadering die beste oplossing vir die probleem lewer. Hierdie benadering word tans nie in die praktyk toegepas nie, as gevolg van sy inherente kompleksiteite. Desnieteenstaande, kan hierdie benadering in die toekoms die norm word

Smart Water Utilities

Today there is increasing pressure on the water infrastructure and although unsustainable water extraction and wastewater handling can continue for a while, at some point water needs to be managed in a way that is sustainable in the long-term. We need to handle water utilities “smarter”. New and effective tools and technologies are becoming available at an affordable cost and these technologies are steadily changing water infrastructure options. The quality and robustness of sensors are increasing rapidly and their reliability makes the automatic handling of critical processes viable. Online and real-time control means safer and more effective operation. The combination of better sensors and new water treatment technologies is a strong enabler for decentralised and diversified water treatment. Plants can be run with a minimum of personnel attendance. In the future, thousands of sensors in the water utility cycle will handle all the complexity in an effective way. Smart Water Utilities: Complexity Made Simple provides a framework for Smart Water Utilities based on an M-A-D (Measurement-Analysis-Decision). This enables the organisation and implementation of “Smart” in a water utility by providing an overview of supporting technologies and methods. The book presents an introduction to methods and tools, providing a perspective of what can and could be achieved. It provides a toolbox for all water challenges and is essential reading for the Water Utility Manager, Engineer and Director and for Consultants, Designers and Researchers

Physical Scale Modelling of Urban Flood Systems

Urban flooding is defined as ‘an overflowing or irruption of water over urban pathways which are not usually submerged’. Current economic, climatic and social trends suggest that the frequency, magnitude and cost of flooding are likely to increase in the future. Hydraulic models are commonly used by engineers in order to predict and mitigate flood risk. However full scale calibration and validation datasets for these modelling tools are scarce. The main research objective of this thesis was to design and construct a physical model in order to provide datasets useful to verify, calibrate and validate computer model results in terms of energy losses in manholes. To address these issues, an experimental facility has been constructed to enable the investigation of energy losses under steady and unsteady flow conditions in a scaled sewer system. Originally the model was composed of six manholes and three main pipes and then it was modified into a single pipe linked to an urban surface through a single manhole. Experiments involved the measurement of flow rates, velocity, pressure and water depth within the physical models under different hydraulic scenarios. Steady flow tests were conducted to quantify energy losses though manhole structures with different inlet/outlet configurations under a range of hydraulic conditions. Unsteady flow tests were conducted to examine the performance of different computational hydraulic models. These tests have shown that the performance of the SWMM hydraulic model could be improved by including local losses in the calibration process. After modification the model was used to quantify sewer to surface and surface to sewer flow exchange through a single manhole during pluvial flooding. The work has demonstrated the feasibility of using weir and orifice equations within modelling tools to quantify this exchange under steady conditions. The model was used to empirically quantify discharge coefficients for energy loss equations which describe flow exchange for the first time

Soil-related geohazard assessment for climate-resilient UK infrastructure

UK (United Kingdom) infrastructure networks are fundamental for maintaining societal and economic wellbeing. With infrastructure assets predominantly founded in the soil layer (< 1.5m below ground level) they are subject to a range of soil-related geohazards. A literature review identified that geohazards including, clay-related subsidence, sand erosion and soil corrosivity have exerted significant impacts on UK infrastructure to date; often resulting in both long-term degradation and ultimately structural failure of particular assets. Climate change projections suggest that these geohazards, which are themselves driven by antecedent weather conditions, are likely to increase in magnitude and frequency for certain areas of the UK through the 21st century. Despite this, the incorporation of climate data into geohazard models has seldom been undertaken and never on a national scale for the UK. Furthermore, geohazard risk assessment in UK infrastructure planning policy is fragmented and knowledge is often lacking due to the complexity of modelling chronic hazards in comparison to acute phenomenon such as flooding. With HM Government's recent announcement of £50 million planned infrastructure investment and capital projects, the place of climate resilient infrastructure is increasingly pertinent. The aim of this thesis is therefore to establish whether soil-related geohazard assessments have a role in ensuring climate-resilient UK infrastructure. Soil moisture projections were calculated using probabilistic weather variables derived from a high-resolution version of the UKCP09 (UK Climate Projections2009) weather generator. These were then incorporated into a geohazard model to predict Great Britain's (GB) subsidence hazard for the future scenarios of 2030 (2020-2049) and 2050 (2040-2069) as well as the existing climatic baseline (1961-1990). Results suggest that GB is likely to be subject to increased clay-related subsidence in future, particularly in the south east of England. This thesis has added to scientific understanding through the creation of a novel, national-scale assessment of clay subsidence risk, with future assessments undertaken to 2050. This has been used to help create a soil- informed maintenance strategy for improving the climate resilience of UK local roads, based on an extended case study utilising road condition data for the county of Lincolnshire, UK. Finally, a methodological framework has been created, providing a range of infrastructure climate adaptation stakeholders with a method for incorporating geohazard assessments, informed by climate change projections, into asset management planning and design of new infrastructure. This research also highlights how infrastructure networks are becoming increasingly interconnected, particularly geographically, and therefore even minor environmental shocks arising from soil-related geohazards can cause significant cascading failures of multiple infrastructure networks. A local infrastructure hotspot analysis methodology and case-study is provided

Urban Pluvial Flood Forecasting

Two main approaches to enhance urban pluvial flood prediction were developed and tested in this research: (1) short-term rainfall forecast based on rain gauge networks, and (2) customisation of urban drainage models to improve hydraulic simulation speed. Rain gauges and level gauges were installed in the Coimbra (Portugal) and Redbridge (UK) catchment areas. The collected data was used to test and validate the approaches developed. When radar data is not available urban pluvial flooding forecasting can be based on networks of rain gauges. Improvements were made in the Support Vector Machine (SVM) technique to extrapolate rainfall time series. These improvements are: enhancing SVM prediction using Singular Spectrum Analysis (SSA) for pre-processing data; combining SSA and SVM with a statistical analysis that gives stochastic results. A method that integrates the SVM and Cascade-based downscaling techniques was also developed to carry out high-resolution (5-min) precipitation forecasting with longer lead time. Tests carried out with historical data showed that the new stochastic approach was useful for estimating the level of confidence of the rainfall forecast. The integration of the cascade method demonstrates the possibility of generating high-resolution rainfall forecasts with longer lead time. Tests carried out with the collected data showed that water level in sewers can be predicted: 30 minutes in advance (in Coimbra), and 45 minutes in advance (in Redbridge). A method for simplifying 1D1D networks is presented that increases computational speed while maintaining good accuracy. A new hybrid model concept was developed which combines 1D1D and 1D2D approaches in the same model to achieve a balance between runtime and accuracy. While the 1D2D model runs in about 45 minutes in Redbridge, the 1D1D and the hybrid models both run in less than 5 minutes, making this new model suitable for flood forecasting

Development of an early warning system to predict sewer overflow

Flash flooding in our city is still a fairly common phenomenon.Unfortunately, the development of a flash flood forecasting system in urban areas is not a simple and unambiguous procedure.While attending the PhD course in Civil and Environmental Engineering, research activity has been given to realize an urban overflowing prediction system that was best as possible suited to the drainage network of the city of Palermo. With the support of radar data and hybrid hydraulic model for drainage network has been possible to demonstrate the effectiveness of this instrument, while the reduction of residual flood risk has been supported by modern resilience measures