Using Artificial Neural Network Models to Assess Water Quality in Water Distribution Networks

Vodárenský distribuční systém je tvořen sítí dílčích prvků a subsystémů, které slouží k dopravě vody od zdroje až k odběratelům. Voda musí být upravena v úpravně vody pro zajistění bezpečné pitné vody pro spotřebitele, neobsahující patogenní a jiné nežádoucí organismy. Důležitým aspektem pro dosažení nezávadné pitné vody a prevencí před šířením chorob přenášených vodou je její hygienické zabezpečení. Chlor je běžným nejpoužívanějším dezinfekčním prostředkem v konvenčních procesech úpravy vody. Jeho rozšířené použití je dáno nízkou cenou a jeho vysokou schopností ničení bakterií. Proto se zajišťují jeho zbytkové koncentrace ve vodárenských distribučních systémech, aby se zabránilo mikrobiologické kontaminaci. Zbytková koncentrace chloru je ovlivněna fenoménem známým jako úbytek chloru, což znamená, že chlor reaguje uvnitř systému a jeho koncentrace se tak snižuje. Chlor je měřen na výstupu z úpravny vody a také v několika daných bodech ve vodárenském distribučním systému určeném pro kontrolu kvality vody. Metody simulace a modelování pomáhají efektivním způsobem předvídat koncentraci chloru ve vodárenských distribučních systémech. Účelem předložené disertační práce je hodnotit koncentraci chloru v některých strategických bodech v rámci vodárenského distribučního systému pomocí historických naměřených údajů některých parametrů kvality vody, které ovlivňují úbytek chloru. Nedávné výzkumy kvality vody prokázaly možnosti použití nelineárního modelování pro predikci úbytku chloru. Úbytek chloru v potrubí je složitý jev, proto vyžaduje techniky, které mohou zajistit spolehlivé a efektivní zastoupení složitosti tohoto chování. Statistické modely založené na umělých neuronových sítích byly shledány vhodnými pro zkoumání a řešení problémů spojených s nelinearitou v predikci úbytku chloru a nabízí výhodu na rozdíl od konvenčních modelovacích technik. V tomto ohledu použivá tato disertační práce specifickou aplikaci neuronové sítě k vyřešení problému předpovídání zbytkového cA water distribution system (WDS) is based in a network of interconnected hydraulic components to transport the water directly to the customers. Water must be treated in a Water Treatment Plant (WTP) to provide safe drinking water to consumers, free from pathogenic and other undesirable organisms. The disinfection is an important aspect in achieving safe drinking water and preventing the spread of waterborne diseases. Chlorine is the most commonly used disinfectant in conventional water treatment processes because of its low cost, its capacity to deactivate bacteria, and because it ensures residual concentrations in WDS to prevent microbiological contamination. Chlorine residual concentration is affected by a phenomenon known as chlorine decay, which means that chlorine reacts with other components along the system and its concentration decrease. Chlorine is measured at the output of the WTP and also in several considered points within the WDS to control the water quality in the system. Simulation and modeling methods help to predict in an effective way the chlorine concentration in the WDS. The purpose of the thesis is to assess chlorine concentration in some strategic points within the WDS by using the historical measured data of some water quality parameters that influence chlorine decay. Recent investigations of the water quality have shown the need of the use of non-linear modeling for chlorine decay prediction. Chlorine decay in a pipeline is a complex phenomenon so it requires techniques that can provide reliable and efficient representation of the complexity of this behavior. Statistical models based on Artificial Neural Networks (ANN) have been found appropriated for the investigation and solution of problems related with non-linearity in the chlorine decay prediction offering advantages over more conventional modeling techniques. In this sense, this thesis uses a specific neural network application to solve the problem of forecasting the residual chlorine

Forecasting Chlorine Residuals in a Water Distribution System Using a General Regression Neural Network

Abstract: In a water distribution system (WDS), chlorine disinfection is important in preventing the spread of waterborne diseases. By strictly controlling residual chlorine throughout the WDS, water quality managers can ensure the satisfaction and safety of their customers. However, due to the travel time of water between the chlorine dosing point and any strategic monitoring points, water treatment plant (WTP) operators often receive information too late for their responses to be effective. Given the ability to forecast the chlorine residual at strategic points in a WDS, it would be possible to have superior control over the chlorine dose, thereby preventing incidents of under-and over-chlorination. In this research, a general regression neural network (GRNN) has been developed for forecasting chlorine residuals in the Myponga WDS to the south of Adelaide, South Australia, 24 hours in advance. A number of critical model issues are addressed including: selection of an appropriate forecasting horizon; division of the available data into subsets for modelling; and, the determination of the inputs that are relevant to the chlorine forecasts. In order to determine if the GRNN is able to capture any nonlinear relationships that may be present in the data set, a comparison is made between the GRNN model and a multiple linear regression (MLR) model. When tested on an independent validation set of data, the GRNN models were able to forecast chlorine levels to a high level of accuracy, up to 24 hours in advance. The GRNN also significantly outperformed the MLR model, thereby providing evidence for the existence of nonlinear relationships in the data set

Improving partial mutual information-based input variable selection by consideration of boundary issues associated with bandwidth estimation

Abstract not availableXuyuan Li, Aaron C. Zecchin, Holger R. Maie

Lost in optimisation of water distribution systems? A literature review of system operation

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Optimisation of the operation of water distribution systems has been an active research field for almost half a century. It has focused mainly on optimal pump operation to minimise pumping costs and optimal water quality management to ensure that standards at customer nodes are met. This paper provides a systematic review by bringing together over two hundred publications from the past three decades, which are relevant to operational optimisation of water distribution systems, particularly optimal pump operation, valve control and system operation for water quality purposes of both urban drinking and regional multiquality water distribution systems. Uniquely, it also contains substantial and thorough information for over one hundred publications in a tabular form, which lists optimisation models inclusive of objectives, constraints, decision variables, solution methodologies used and other details. Research challenges in terms of simulation models, optimisation model formulation, selection of optimisation method and postprocessing needs have also been identified

Embracing Analytics in the Drinking Water Industry

Analytics can support numerous aspects of water industry planning, management, and operations. Given this wide range of touchpoints and applications, it is becoming increasingly imperative that the championship and capability of broad-based analytics needs to be developed and practically integrated to address the current and transitional challenges facing the drinking water industry. Analytics will contribute substantially to future efforts to provide innovative solutions that make the water industry more sustainable and resilient. The purpose of this book is to introduce analytics to practicing water engineers so they can deploy the covered subjects, approaches, and detailed techniques in their daily operations, management, and decision-making processes. Also, undergraduate students as well as early graduate students who are in the water concentrations will be exposed to established analytical techniques, along with many methods that are currently considered to be new or emerging/maturing. This book covers a broad spectrum of water industry analytics topics in an easy-to-follow manner. The overall background and contexts are motivated by (and directly drawn from) actual water utility projects that the authors have worked on numerous recent years. The authors strongly believe that the water industry should embrace and integrate data-driven fundamentals and methods into their daily operations and decision-making process(es) to replace established ìrule-of-thumbî and weak heuristic approaches ñ and an analytics viewpoint, approach, and culture is key to this industry transformation

Cyber-Physical Systems for Smart Water Networks: A Review

There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.acceptedVersio

Events Recognition System for Water Treatment Works

The supply of drinking water in sufficient quantity and required quality is a challenging task for water companies. Tackling this task successfully depends largely on ensuring a continuous high quality level of water treatment at Water Treatment Works (WTW). Therefore, processes at WTWs are highly automated and controlled. A reliable and rapid detection of faulty sensor data and failure events at WTWs processes is of prime importance for its efficient and effective operation. Therefore, the vast majority of WTWs operated in the UK make use of event detection systems that automatically generate alarms after the detection of abnormal behaviour on observed signals to ensure an early detection of WTW’s process failures. Event detection systems usually deployed at WTWs apply thresholds to the monitored signals for the recognition of WTW’s faulty processes. The research work described in this thesis investigates new methods for near real-time event detection at WTWs by the implementation of statistical process control and machine learning techniques applied for an automated near real-time recognition of failure events at WTWs processes. The resulting novel Hybrid CUSUM Event Recognition System (HC-ERS) makes use of new online sensor data validation and pre-processing techniques and utilises two distinct detection methodologies: first for fault detection on individual signals and second for the recognition of faulty processes and events at WTWs. The fault detection methodology automatically detects abnormal behaviour of observed water quality parameters in near real-time using the data of the corresponding sensors that is online validated and pre-processed. The methodology utilises CUSUM control charts to predict the presence of faults by tracking the variation of each signal individually to identify abnormal shifts in its mean. The basic CUSUM methodology was refined by investigating optimised interdependent parameters for each signal individually. The combined predictions of CUSUM fault detection on individual signals serves the basis for application of the second event detection methodology. The second event detection methodology automatically identifies faults at WTW’s processes respectively failure events at WTWs in near real-time, utilising the faults detected by CUSUM fault detection on individual signals beforehand. The method applies Random Forest classifiers to predict the presence of an event at WTW’s processes. All methods have been developed to be generic and generalising well across different drinking water treatment processes at WTWs. HC-ERS has proved to be effective in the detection of failure events at WTWs demonstrated by the application on real data of water quality signals with historical events from a UK’s WTWs. The methodology achieved a peak F1 value of 0.84 and generates 0.3 false alarms per week. These results demonstrate the ability of method to automatically and reliably detect failure events at WTW’s processes in near real-time and also show promise for practical application of the HC-ERS in industry. The combination of both methodologies presents a unique contribution to the field of near real-time event detection at WTW

ANNTAX - an artificial intelligence based decision support system

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous