Zoran Kapelan

A review of methods for leakage management in pipe networks

Leakage in water distribution systems is an important issue which is affecting water companies and their customers worldwide. It is therefore no surprise that it has attracted a lot of attention by both practitioners and researchers over the past years. Most of the leakage management related methods developed so far can be broadly classified as follows: (1) leakage assessment methods which are focusing on quantifying the amount of water lost; (2) leakage detection methods which are primarily concerned with the detection of leakage hotspots and (3) leakage control models which are focused on the effective control of current and future leakage levels. This paper provides a comprehensive review of the above methods with the objective to identify the current state-of-the-art in the field and to then make recommendations for future work. The review ends with the main conclusion that despite all the advancements made in the past, there is still a lot of scope and need for further work, especially in area of real-time models for pipe networks which should enable fusion of leakage detection, assessment and control methods.

Quo vadis water distribution model calibration

Hydraulic water distribution system (WDS) simulation models are widely used by planners, water utility personnel, consultants and many others involved in analysis, design, operation or maintenance of water distribution systems. Similarly, water quality models that work in concert with hydraulic models are now commonly used to simulate the dispersion of and changes in water quality in a water distribution network. As with all mathematical models, WDS model parameters (hydraulic and water quality) require calibration before useful results may be obtained. Nowadays, automated calibration procedures are widely available for hydraulic models, but less so for water quality models. Even when they are available as commercial software, the use of these advanced calibration tools in engineering practice has been limited. This paper provides a review of model calibration approaches and considers future challenges and directions.

A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks

Leakage detection and calibration of hydraulic models are important issues for the management of water and other distribution networks. An inverse transient model based on a hybrid search technique is presented here. The inverse model is developed mainly for the detection of leaks in water distribution networks. The inverse transient procedure is formulated as a constrained optimisation problem of weighted least-squares type. Two optimisation techniques are tested: the genetic algorithm (GA) and the Levenberg-Marquardt (LM) method. After examining their performance, a new hybrid genetic algorithm (HGA) is developed to exploit the advantages of combining the two methods. The resulting HGA-based inverse transient model is compared with the GA and LM-based inverse transient models using two case studies. The HGA-based inverse transient model proved to be more stable than the LM-based model and it is more accurate and much faster than the GA-based inverse transient model.

Adaptive Flood Risk Management Under Climate Change Uncertainty Using Real Options and Optimization

It is well recognized that adaptive and flexible flood risk strategies are required to account for future uncertainties. Development of such strategies is, however, a challenge. Climate change alone is a significant complication, but, in addition, complexities exist trying to identify the most appropriate set of mitigation measures, or interventions. There are a range of economic and environmental performance measures that require consideration, and the spatial and temporal aspects of evaluating the performance of these is complex. All these elements pose severe difficulties to decisionmakers. This article describes a decision support methodology that has the capability to assess the most appropriate set of interventions to make in a flood system and the opportune time to make these interventions, given the future uncertainties. The flood risk strategies have been explicitly designed to allow for flexible adaptive measures by capturing the concepts of real options and multiobjective optimization to evaluate potential flood risk management opportunities. A state-of-the-art flood risk analysis tool is employed to evaluate the risk associated to each strategy over future points in time and a multiobjective genetic algorithm is utilized to search for the optimal adaptive strategies. The modeling system has been applied to a reach on the Thames Estuary (London, England), and initial results show the inclusion of flexibility is advantageous, while the outputs provide decisionmakers with supplementary knowledge that previously has not been considered.

Assessing the combined effects of urbanisation and climate change on the river water quality in an integrated urban wastewater system in the UK

Climate change and urbanisation are key factors affecting the future of water quality and quantity in urbanised catchments and are associated with significant uncertainty. The work reported in this paper is an evaluation of the combined and relative impacts of climate change and urbanisation on the receiving water quality in the context of an Integrated Urban Wastewater System (IUWS) in the UK. The impacts of intervening system operational control parameters are also investigated. Impact is determined by a detailed modelling study using both local and global sensitivity analysis methods together with correlation analysis. The results obtained from the case-study analysed clearly demonstrate that climate change combined with increasing urbanisation is likely to lead to worsening river water quality in terms of both frequency and magnitude of breaching threshold dissolved oxygen and ammonium concentrations. The results obtained also reveal the key climate change and urbanisation parameters that have the largest negative impact as well as the most responsive IUWS operational control parameters including major dependencies between all these parameters. This information can be further utilised to adapt future IUWS operation and/or design which, in turn, should make these systems more resilient to future climate and urbanisation changes.

Battle of the Water Networks II

The Battle of the Water Networks II (BWN-II) is the latest of a series of competitions related to the design and operation of water distribution systems (WDSs) undertaken within the Water Distribution Systems Analysis (WDSA) Symposium series. The BWN-II problem specification involved a broadly defined design and operation problem for an existing network that has to be upgraded for increased future demands, and the addition of a new development area. The design decisions involved addition of new and parallel pipes, storage, operational controls for pumps and valves, and sizing of backup power supply. Design criteria involved hydraulic, water quality, reliability, and environmental performance measures. Fourteen teams participated in the Battle and presented their results at the 14th Water Distribution Systems Analysis conference in Adelaide, Australia, September 2012. This paper summarizes the approaches used by the participants and the results they obtained. Given the complexity of the BWN-II problem and the innovative methods required to deal with the multiobjective, high dimensional and computationally demanding nature of the problem, this paper represents a snap-shot of state of the art methods for the design and operation of water distribution systems. A general finding of this paper is that there is benefit in using a combination of heuristic engineering experience and sophisticated optimization algorithms when tackling complex real-world water distribution system design problems

Risk-Based Sensor Placement for Contaminant Detection in Water Distribution Systems

A method for optimizing sensor locations to effectively and efficiently detect contamination in a water distribution network is presented here. The problem is formulated and solved as a twin-objective optimization problem with the objectives being the minimization of the number of sensors and minimization of the risk of contamination. Unlike past approaches, the risk of contamination is explicitly evaluated as the product of the likelihood that a set of sensors fails to detect contaminant intrusion and the consequence of that failure (expressed as volume of polluted water consumed prior to detection). A novel importance-based sampling method is developed and used to effectively determine the relative importance of contamination events, thus reducing the overall computation time. The above problem is solved by using the nondominated sorting genetic algorithm II. The methodology is tested on a case study involving the water distribution system of Almelo (The Netherlands) and the potential intrusion of E. co...

Probabilistic prediction of urban water consumption using the SCEM-UA algorithm

Prediction of urban water consumption can help to improve the performance of water distribution systems. Despite the obvious presence of uncertainty in measurements and in assumed model types/structures, most of the existing water consumption prediction models are developed and used in a deterministic context. Methods for more realistic assessment of parameter and model prediction uncertainties have begun to appear in literature only recently. A novel application of the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) for the calibration of a water consumption prediction model is proposed here. The model is applied to a case study of the city of Catania (Italy) with the aim to predict daily water consumption. The SCEM-UA algorithm is used to calibrate the parameters of the artificial neural network based prediction model and in turn to determine the associated parameter and model prediction uncertainties. The results obtained using the SCEM-UA ANN approach were compared to the corresponding results obtained using other predictive models developed recently by the authors of the paper. When compared to the these models, the SCEM-UA ANN based water consumption prediction model shows similar predictive capability but also the ability to identify simultaneously the prediction uncertainty bounds associated with the posterior distribution of the parameter estimates.

Dealing with Uncertainty in Water Distribution System Models: A Framework for Real-Time Modeling and Data Assimilation

AbstractWater distribution system (WDS) models may improve system control when applied using real-time data, and in doing so, help meet consumer and regulatory demands. Such real-time modeling often overlooks the multiple sources of system uncertainty that cascade into model forecasts and affect the identification of robust operational solutions. This paper considers key uncertainties in WDS modeling and reviews promising approaches for uncertainty quantification and reduction in the modeling cascade from calibration, through data assimilation, to model forecasting. An uncertainty framework exemplifying how such methods may be applied to propagate uncertainty through the real-time control process is outlined. Innovative methods to constrain uncertainty when the time-horizon and data availability limit such thorough analysis are also discussed, alongside challenges that need to be addressed to incorporate uncertain information into the control decision. Further work evaluating the value of these methods in...

An efficient algorithm for sensor placement in water distribution systems

The objective of this paper is to present an optimal sensor placement methodology to assist in the effective and efficient detection of accidental and/or intentional contaminant intrusion(s) in water distribution systems. The work presented here is done in response to call for papers for the Battle of the Water Sensors Networks (BWSN), at the Water Distribution Systems Analysis Symposium (2006). The above problem is formulated and solved as a constrained multiobjective optimisation problem. The four objectives are: (1) minimisation of the expected time of detection, (2) minimisation of the expected population affected prior to detection, (3) minimisation of the expected demand of contaminated water prior to detection and (4) maximisation of the detection likelihood. The constraint modelled is the pre-specified number of detection sensors used in the sampling design. Decision variables are the sensor network locations. The solution methodology proposed is based on the novel Noisy Cross-Entropy Sensor Locator (nCESL) algorithm. This algorithm is applied to the two competition networks under four base contamination scenarios (A, B, C and D) and two different numbers of sensors available (5 and 20). The results obtained demonstrate the effectiveness and efficiency of the sensor placement methodology proposed. Copyright ASCE 2006.