2nd WDSA/CCWI Joint Conference
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The Department of Hydraulic Engineering and Environment of the Universitat Politècnica de València (Valencia Tech) is pleased to invite you to the second edition of the WDSA/CCWI Joint Conference to be held in Valencia (Spain).
This conference will bring together professionals from municipalities, consulting firms, and universities to exchange ideas about the big challenges facing the water industry.
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Recent Submissions
Now showing 1 - 5 of 127
- PublicationWhich flow and pressure constraints are required for sustainable operation of Water Distribution Systems?(Editorial Universitat Politècnica de València, 2024-03-06) Piller, Olivier; Deuerlein, Jochen; Elhay, Sylvan; Simpson, Angus[EN] Water quantity and quality modelling tools are often used to understand the working and operate properly water distribution systems. In this paper, we discuss how to choose flow and pressure constraints at nodes and links in the distribution network graph for sustainable operation of these systems. Using practical and concrete examples, we show how the problem of choosing the appropriate flow and pressure constraints amounts to verifying that a certain algebraic condition of maximum rank holds (a constraint qualification condition), or equivalently that there is a corresponding spanning tree with unsaturated links and demand nodes. Some situations of non-convergence in the solution path are discussed.
- PublicationAnalysis of PDA-based Water Distribution System Suspension Risk using statistical and machine learning method(Editorial Universitat Politècnica de València, 2024-03-06) Oh, Yoojin; Park, Haekeum; Hyung, Jinseok; Kim, Taehyeon; Kim, Kibum; Koo, Jayong; Ministry of environment (South Korea)[EN] Recently, there have been frequent cases of water shortages caused by failure to old water pipes. As water is the most basic resource in life and is an indispensable resource in various fields such as industry and agriculture, the scale of the failure is significant in the event of accidents in the water supply pipe network, and in order to minimize the damage of accidents, it is important to prevent accidents through timely maintenance. At this time, the risk map of the water shortage of the water pipeline needs to be prepared for efficient maintenance, and it needs to be managed first from the high-risk area.To this end, water shortage risk analysis due to pipe failure was performed in this study. Risk analysis is one of the ways in which water pipes are evaluated and decisions on investment plans, such as replacement or repair, can be supported. The risk is generally calculated by multiplying PoF (Probability of failure) with the resulting direct and indirect effects of CoF (Consequence of failure). In this study, PoF was derived as the failure of an individual water pipe was set as the probability of failure caused by corrosion, and in order for it to be predicted, MLP (Multi-layer perceptron) and XGBoot were developed as a data-based machine learning model. In addition, it was analyzed by setting the amount of water (supply shortage) that CoF could not be supplied due to failure, considering that the failure to the water pipe was directly linked to water shortage. In order to analyze the supply shortage at this time, the mathematical analysis of PDA (Pressure driven analysis) was performed.Finally, the developed methodology was applied to the cities of the Republic of Korea, and the risks were analyzed by calculating the PoF and CoF of individual water pipes, and the GIS technique was used to create the risk map.The results of this study can be more accurate in predicting the condition of water pipes, which can be helpful when water utilities establish maintenance plans.
- PublicationOptimization of reservoir treatment levels considering uncertainty in mixing at cross junctions in water distribution systems using Info-gap decision theory(Editorial Universitat Politècnica de València, 2024-03-06) Boindala, Sriman Pankaj; Jaykrishnan, G; Ostfeld, Avi[EN] Water distribution systems are affected by several uncertainties in multiple stages. This uncertainty makes solving the optimal design and management of WDS a multifaceted problem. Past research has focused only on solving design and management problems of system hydraulics. There have been very few studies that involve considering uncertainties that affect the water quality aspect of WDS. One of the major assumptions in solving the design and management problems of WDS is considering uniform and instantaneous mixing at the cross junctions. However, in reality, this is not true. This assumption is made due to the lack of computational power to accurately estimate the level of mixing at every junction in a water distribution network. This study focuses on considering this level of mixing as uncertain/unknown and provides the optimal treatment levels required at the reservoirs to ensure the system is immune to the level of mixing occurring at the junctions to satisfy the water quality requirements at the customer level. Info-gap decision theory-based optimization approach combined with the cuckoo search metaheuristic is proposed in this study to handle the uncertainty. The proposed methodology is applied to a 4x4 grid hypothetical network example. The study's objective is to provide the best designs that can handle the maximum variation of the level of mixing at junctions within the given budget by the designer. The maximum variation of the level of mixing is reported for different budget levels. The designs are compared with the deterministic case using Monte-Carlo simulations.
- PublicationEnergy equations to analyze pressurized water transport systems(Editorial Universitat Politècnica de València, 2024-03-08) Del Teso March, Roberto; Gómez Sellés, Elena; Estruch Juan, María Elvira; Cabrera Marcet, Enrique; Dpto. de Ingeniería Hidráulica y Medio Ambiente; Escuela Técnica Superior de Ingeniería Industrial; Escuela Politécnica Superior de Alcoy[EN] Pressurized water systems are high energy demanders. They must deliver the demanded volume of water at the minimum service pressure. There is currently a marked change in production and energy tariffs, which is causing prices for water services to rise. In addition to the economic aspect, the energy demand of water systems has an environmental implication linked to gas emissions. With increasing demands, the climate change situation and the shift in energy tariffs, it is necessary to improve the efficiency of pressurized water transport systems. The lower the kWh required to supply, the more energy efficient they will be. The energy analysis of the systems will allow us to know the current situation of the systems, and to know if it is necessary to undertake improvement measures. There are different processes to know the energy status of the networks. The simplest one is to carry out a diagnosis, with little data, which will give an initial idea of the energy status of the networks. This diagnosis can be carry through by applying Bernoulli's equation. Applied from the supply points to the most unfavorable point of the system. If it is desired to know more precisely the energetic status of the networks, the use of the integral energy equation seems more reasonable. This equation makes it possible to audit the system and to know in detail the energy use for a given control volume. It allows the energy supplied to be broken down into: useful energy, structural energy losses (linked to topography) and operational energy losses (friction losses, pumping losses, leakage losses and excess energy). In order to be able to apply this method, it is mandatory to have the mathematical model of the system. This paper discusses the advantages and disadvantages of carrying out the energy analysis of a system using the Bernoulli equation (diagnosis) or the energy integral equation (audit), and when it is convenient to apply one or the other. On one hand, the Bernoulli equation makes it possible to estimate the energy level of the network with very little data, without knowing exactly in which processes the energy introduced into the system is invested. The audit based on the integral energy equation, on the other hand, requires precise data collection and mathematical modelling, but it will provide a detailed understanding of the energy breakdown of the network. Depending on the objective of the energy analysis, it seems reasonable to apply one process or another. For a first estimation and as a start of the energy analysis, it will be sufficient to carry out a diagnosis as quickly as possible, which will allow to know if it is necessary to continue with a more in-depth research of the system status such as an energy audit.
- PublicationMulti-objective insights and analysis on data driven classifiers for anomaly detection in water distribution systems(Editorial Universitat Politècnica de València, 2024-03-06) Carreño-Alvarado, Elizabeth Pauline; Hernández-Alba, Mayra; Reynoso Meza, Gilberto; CNPq; Fundação Araucária[EN] Machine learning techniques have shown to be a powerful tool for extracting and/or inferring complex patterns from data. In the case of the so-called supervised learning, a given learner representation could learn such patterns using labeled data. For example, a helpful approach is to adjust a learner to detect anomalies: historical data can be used, where those events are identified, to find a pattern to classify new data as an anomaly (true event) or not (false event). In this example, the learner's objective is to act as a binary classifier, where a balance between false negatives (predict a typical operation, when in fact an anomaly exists) and false positives (predict an anomaly, when there is not). This balance is attained via an optimization (learning phase), where the learner representation is adjusted. Multi-objective optimization techniques have a natural way of dealing with such problems. They perform a simultaneous optimization of conflicting objectives. As a result, a set of Pareto-optimal solutions, the Pareto front, is calculated. This idea could be used in the training process of binary classifiers. Nevertheless, this requires an integral methodology, merging multi-objective optimization and multi-criteria decision making. While it is true that this idea is not new, methodologies and guidelines are still missing to conduct this process. In this work, we move toward the definition of an integrated methodology of multi-objective learning for binary classifiers for anomaly detection. An anomaly detection database for water distribution systems is used for such a purpose. Preliminary results show to be competitive regarding the F1-score to similar approaches.