Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)

A generic water system consists of a series of works that allow the collection, conveyance, storage and finally the distribution of water in quantities and qualities such as to satisfy the needs of end users. In places characterized by high altitude differences between the intake works and inhabited centres, the potential energy of the water is very high. This energy is attributable to high pressures, which could compromise the functionality of the pipelines; it is therefore necessary to dissipate part of this energy. A common alternative to dissipation is the possibility of exploiting this energy by inserting a hydraulic turbine. The present study aims to evaluate the results obtained from a stochastic approach for the solution of the multi-objective optimization problem of PATs (Pumps As Turbines) in water systems. To this end, the Bayesian Monte Carlo optimisation method was chosen for the optimization of three objective functions relating to pressure, energy produced and plant costs. The case study chosen is the Net 3 literature network available in the EPANET software manual. The same problem was addressed using the NSGA-III (Nondominated Sorting Genetic Algorithm) to allow comparison of the results, since the latter is more commonly used. The two methods have different peculiarities and therefore perform better in different contexts

An Environmental Analysis of the Effect of Energy Saving, Production and Recovery Measures on Water Supply Systems under Scarcity Conditions

Water is one of the primary resources provided for maintaining quality of life and social status in urban areas. As potable water is considered to be a primary need, water service has usually been managed without examining the economic and environmental sustainability of supply processes. Currently, due to increases in energy costs and the growth of environment preservation policies, reducing water leakage, energy consumption and greenhouse gas (GHG) production have become primary objectives in reducing the environmental footprint of water service. The present paper suggests the implementation of some performance indicators that show the interdependence of water loss, energy consumption and GHG emission. These indicators are used to compare a few possible mitigation scenarios involving water loss reduction and increasing the system's energy efficiency. The proposed indicators were applied to a complex urban water supply system serving the city of Palermo (Italy)

Impact of diffusion and dispersion of contaminants in water distribution networks modelling and monitoring

Abstract In recent years, there has been a need to seek adequate preventive measures to deal with contamination in water distribution networks that may be related to the accidental contamination and the deliberate injection of toxic agents. Therefore, it is very important to create a sensor system that detects contamination events in real time, maintains the reliability and efficiency of measurements, and limits the cost of the instrumentation. To this aim, two problems have to be faced: practical difficulties connected to the experimental verification of the optimal sensor configuration efficiency on real operating systems and challenges related to the reliability of the network modelling approaches, which usually neglect the dispersion and diffusion phenomena. The present study applies a numerical optimization approach using the NSGA-II genetic algorithm that was coupled with a new diffusive-dispersive hydraulic simulator. The results are compared with those of an experimental campaign on a laboratory network (Enna, Italy) equipped with a real-time water quality monitoring system and those of a full-scale real distribution network (Zandvoort, Netherlands). The results showed the importance of diffusive processes when flow velocity in the network is low. Neglecting diffusion can negatively influence the water quality sensor positioning, leading to inefficient monitoring networks

Optimal water quality sensor positioning in urban drainage system for illicit intrusion identification

In the last decade, the growth of the micro-industry in urban areas has produced an increase in the frequency of xenobiotic polluting discharges in drainage systems. Wastewater treatment plants are usually characterized by low removal efficiencies in respect of such pollutants, which may have an acute or cumulative impact on environmental and public health. To facilitate the early isolation of illicit intrusions, this study aims to develop an approach for positioning water quality sensors based on the Bayesian decision network (BDN). The analysis is focused on soluble conservative pollutants, such as metals. The proposed methodology incorporates several sources of information, including network topology, flows and non-formal ‘grey’ information about the possible locations of contamination sources. The methodology is tested using two sewer systems with increasing complexity: a literature scheme from the Storm Water Management Model (SWMM) manual and a real combined sewer in Italy. In both cases, the approach identifies the optimal sensor location gaining advantage from additional information, which reduces the computational effort needed to obtain the solution. In the real case, the application of the method yielded a better solution with regards to the real position of the implemented sensor network

Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)

A generic water system consists of a series of works that allow the collection, conveyance, storage and finally the distribution of water in quantities and qualities such as to satisfy the needs of end users. In places characterized by high altitude differences between the intake works and inhabited centres, the potential energy of the water is very high. This energy is attributable to high pressures, which could compromise the functionality of the pipelines; it is therefore necessary to dissipate part of this energy. A common alternative to dissipation is the possibility of exploiting this energy by inserting a hydraulic turbine. The present study aims to evaluate the results obtained from a stochastic approach for the solution of the multi-objective optimization problem of PATs (Pumps As Turbines) in water systems. To this end, the Bayesian Monte Carlo optimisation method was chosen for the optimization of three objective functions relating to pressure, energy produced and plant costs. The case study chosen is the Net 3 literature network available in the EPANET software manual. The same problem was addressed using the NSGA-III (Nondominated Sorting Genetic Algorithm) to allow comparison of the results, since the latter is more commonly used. The two methods have different peculiarities and therefore perform better in different contexts

LCA Methodology for the Quantification of the Carbon Footprint of the Integrated Urban Water System

In integrated urban water systems, energy consumption, and consequently the amount of produced CO2, depends on many environmental, infrastructural, and management factors such as supply water quality, on which treatment complexity depends, urban area orography, water systems efficiency, and maintenance levels. An important factor is related to the presence of significant water losses, which result in an increase in the supply volume and therefore a higher energy consumption for treatment and pumping, without effectively supplying users. The current European environmental strategy is committed to sustainable development by generating action plans to improve the environmental performance of products and services. The analysis of carbon footprints is considered one such improvement, allowing for the evaluation of the environmental impact of single production phases. Using this framework, the aim of the study is to apply a Life Cycle Assessment (LCA) methodology to quantify the carbon footprint of an overall integrated urban water system referring to ISO/TS 14067 (2013). This methodology uses an approach known as “cradle to grave” and presumes to conduct an objective assessment of product units, balancing energy, and matter flows along the production process. The methodology was applied to a real case study, i.e., the integrated urban water system of the Palermo metropolitan area in Sicily (Italy). Each process in the system was characterized and globally evaluated from the point of view of water loss, energy consumption, and CO2 production, and some mitigation strategies are proposed and evaluated to reduce the energy consumption and, consequently, the environmental impact of the system

Optimal Deployment of the Water Quality Sensors in Urban Drainage Systems

In the water sector, the problem of pollution-source identification was mainly investigated regarding pressurized distribution networks, with respect to sewers. Even if the Water Framework Directive 2000/60/EC and equivalent law-making bodies in many countries introduce the principle that the polluter pays, it is asking the water manager to detect the most pollutant discharges in sewers. In previous studies, a probabilistic approach to positioning water quality sensors in urban drainage networks shows the progressive increase in identification probability obtained through the Bayesian approach. Following previous literature, the present work aims to improve it by inserting new information beyond network topology. The methodology is applied to the real test case represented by the sub-catchment of the sewer system Palermo (Italy)

Analysis of Optimal Sensor Placement in Looped Water Distribution Networks Using Different Water Quality Models

Urban looped water distribution systems are highly vulnerable to water quality issues. They could be subject to contamination events (accidental or deliberate), compromising the water quality inside them and causing damage to the users’ health. An efficient monitoring system must be developed to prevent this, supported by a suitable model for assessing water quality. Currently, several studies use advective–reactive models to analyse water quality, neglecting diffusive transport, which is claimed to be irrelevant in turbulent flows. Although this may be true in simple systems, such as linear transport pipes, the presence of laminar flows in looped systems may be significant, especially at night and in the peripheral parts of the network. In this paper, a numerical optimisation approach has been compared with the results of an experimental campaign using three different numerical models as inputs (EPANET advective model, the AZRED model in which diffusion–dispersion equations have been implemented, and a new diffusive–dispersive model in dynamic conditions using the random walk method, EPANET-DD). The optimisation problem was formulated using the Monte Carlo method. The results demonstrated a significant difference in sensor placement based on the numerical model

A Novel EPANET Integration for the Diffusive–Dispersive Transport of Contaminants

The EPANET model is commonly used to model hydraulic behaviour and water quality within water distribution networks. The standard version of the model solves the advective transport equation by solving a mass balance of the fundamental plug flow substance that considers the advective transport and kinetic reaction processes. Over the years, several versions of the model have been developed, which have made it possible to improve the modelling of water quality through the introduction of additional terms within the transport equation to solve the problem of dispersive transport (EPANET-AZRED) and to consider multiple interacting species in the mass flow and on the pipe walls (EPANET multi-species extension). The present study proposes a novel integration of the EPANET-DD (dynamic-dispersion) model, which enables the advective–diffusive–dispersive transport equation in dynamic flow conditions to be solved in the two-dimensional case, through the classical random walk method, implementing the diffusion and dispersion equations proposed by Romero-Gomez and Choi (2011). The model was applied to the University of Enna “KORE” laboratory network to verify its effectiveness in modelling diffusive–dispersive transport mechanisms in the presence of variable flow regimes. The results showed that the EPANET-DD model could better represent the actual data than previously developed versions of the EPANET model