Drinking water temperatures are expected to increase in the Netherlands due to climate change and the installation of district heating networks as part of the energy transition. To determine effective measures to prevent undesirable temperature increases in drinking water, a model was developed. This model describes the temperature in the drinking water distribution network as a result of the transfer of heat from the climate and above and underground heat sources through the soil. The model consists of two coupled applications. The extended soil temperature model (STM+) describes the soil temperatures using a two-dimensional finite element method that includes a drinking water pipe and two hot water pipes coupled with a micrometeorology model. The extended water temperature model (WTM+) describes the drinking water temperature as a function of the surrounding soil temperature (the boundary temperature resulting from the STM+), the thermal sphere of influence where the drinking water temperature influences the soil temperature, and the hydraulics in the drinking water network. Both models are validated with field measurements. This study describes the WTM+. Previous models did not consider the cooling effect of the drinking water on the surrounding soil, which led to an overestimation of the boundary temperature and how quickly the drinking water temperature reaches this boundary temperature. The field measurements show the improved accuracy of the WTM+ when considering one to two times the radius of the drinking water pipe as the thermal sphere of influence around the pipe.

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Large water distribution networks require efficient use of their resources. One of the ways to become more efficient is to reduce the energy consumption due to pumping systems [1] [2]. In the European context the city of Milan has a large water supply system for 1.3 million inhabitants and around 4.0 million commuters, which is supplied entirely by 26 pumping stations. The system currently supplies its ∼50,000 customers with 103 pumps which are actively operated during the day [3]. In previous years a pump scheduling algorithm has been proposed to the utility for a Pressure Management Zone (PMZ) in the south of the city named Abbiategrasso containing only 4 pumps [4] [5]. However, it is of the interest for the utility to extend the analysis to the whole system [6] [7]. For that reason it is necessary to perform a proper pump scheduling. The solution proposed here, is a Multi-Objective Optimization (MOO) for the energy consumption reduction of the whole Water Distribution Network (WDN) using an EPANET model of the whole network. Results show that there is room for improvement of energy and pressure management in the system. The solution presented here can be applied to other utilities with similar challenges.

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This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the west coast of Turkey, near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically, we consider three objectives, which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces, as well as special functions, such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement, by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points, so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions, a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature, we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation, and gives promising results when the Pareto front approximation is examined.@en

Decision support systems (DSS) have been wildly developed in the recent decades, in various areas of decision making. In the 21st century, the advances in Information and Communication Technology (ICT) have brought the era of cloud computing, in which network-distributed resources (simulation and optimization models, data, etc.) are integrated in decision support applications. The aim of the present article is to present the application of web-based decision support systems (WBDSS) in the area of water supply networks (WSN). A simplified method to integrate ICT solutions, comprising two multi-objective optimization algorithms, and one hydraulic simulation model, into one WBDSS is described. Details of the architecture, implementation, and the perspective of future development are addressed. The WBDSS is developed for one particular case study that represents one zone of the water distribution network of city of Milan in Italy. This zone, named Abbiategrasso pilot zone, is supplied by pumped water, and has been isolated from the rest of the water distribution network for purposes of testing new operational strategies, leading to increased energy and water efficiency and improved pressure management. The WBDSS has been developed and applied in this zone for pump scheduling optimization. Although the focus of the article is on WBDSS, selected results from this optimization are also presented. This research is part of the ICeWater project, which is funded by EU FP7 Programme.@en

Along with nowadays growth of urban areas and population, it is more and more important to have better water resources management, especially regarding drinking water supply. Maintaining a good state of a water distribution network poses challenges that are addressed by research community. Two of the main challenges in management of water distribution systems are the reduction of energy consumption due to the high pumping requirements in some systems and/or reduction of water losses. European Commission is encouraging research in this area and currently has funded the EU FP7 project ICe WATER, that aims at development of new ICT strategies for management and operation of water supply systems. Present article focuses on the presentation of the solution for reducing energy consumption, as it was proposed within the project. The solution is applicable to any water distribution system; therefore the principles and methodology demonstrated herein are general.@en

Most developments for Water Distribution Network (WDN) modeling purposes refer to optimization approaches for design, rehabilitation or operation. However, most case studies presented in the literature are rather small and represent only a small portion or simplification of a network. The city of Milano with a population of around 1.3 million inhabitants is an example of a WDN where many phenomena occur simultaneously and where the utility, Metropolitana Milanese S.p.A, (MM), needs to deal with many factors for its daily operation. Under the framework of the EU-FP7 ICeWater project, a new model for the WDN of the city of Milano has been developed with UNESCO-IHE, during the last 2.5 years. This article presents the process of model building, the challenges for the calibration process, open issues and tasks to be developed in the near future.@en

The application of Information and Communication Technologies (ICT) for water supply systems has steadily increased in the last 20 years. The city of Milan is presented as case study where the main challenge for the coming 20 years is the efficient use of energy. Opposite to other parts of the world, in Milan, water is neither scarce nor there is a growing need for supply. Given that the system completely relies on pumping, optimization algorithms and sensor technologies can be applied to reduce energy consumption. This article presents research in optimization for Pump Scheduling (PS), carried out under the framework of the ICeWater project (funded by EU-FP7). For this purpose approaches using two multi-objective optimization algorithms (Nondominated Sorting Genetic Algorithm - NSGA-II and Archive based Micro-Genetic Algorithm -AMGA2) have been applied. The results indicate that a tangible energy consumption reduction can be achieved by using pump scheduling optimization. @en

Lake Taihu is a shallow, wind-driven lake where eutrophication is always a serious problem. The study presented herein investigates the effect of wind on the eutophication of the lake, by using a two-dimensional (2D) hydrodynamic model coupled with a 2D water quality model. The 2D water quality model looks in particular at the water quality situation of Lake Taihu in the year 2008 and predicts future changes in the algae bloom situation. The Delft3D modelling tool was used to build the hydrodynamic model based on the measured geomorphologic and meteorological data of Lake Taihu. Three different sets of wind conditions (no wind, constant wind and measured wind) were considered as wind effects on the water level and velocity, in both the wet and dry seasons. Based on the calibrated hydrodynamic model a 2D water quality model was built which was calibrated based on estimated data for nutrient loads. A one-year hydrodynamic simulation was performed, in order to determine the water quality situation of Lake Taihu. The estimated nutrient loads included total nitrogen (TN) and total phosphorus (TP) concentrations. The estimates are based on the population of cities around Lake Taihu, and the wastewater treatment plant capacity of the provinces of Jiangsu and Zhejiang, where the main sources of pollutants come from. Two time periods (spring and autumn) of algae blooms development for the year 2008 are presented. The results of the study indicate that: 1) the hydrodynamic model with constant wind conditions (southeast, 5m/s) showed better results than the one where measured wind conditions were used, simply because the measured wind data is insufficient at the moment; 2) the model clearly represents the water quality to an acceptable degree; 3) an increase in population in the area would lead to an increase in the maximum value of algae concentration from 35% to 76%; 4) if there is an improvement in technology at the wastewater treatment plant, then the maximum value of algae concentration will be between 17% and 42%.@en