Energy, water and environmental balance of a complex water supply system

The present paper describes the analysis of water and energy balance in a complex urban water supply system. The analysis was carried out employing Life Cycle Analysis (LCA) methodologies. The LCA approach was integrated with the analysis of the system energy and water balance. For a real size water supply system, based on the results of the individual LCAs, the current baseline was constructed highlighting the water, energy and environmental (in terms of CO2eq emissions in the atmosphere) costs of supplied water. Then, three different mitigation measures have been evaluated: the first is based on energy production by installation of photovoltaic systems; the second is based on energy recovery by means of hydraulic turbines, exploiting the available pressure potential to produce energy; the third based on energy optimization of pumping stations by installing inverter systems, replacement of rotors with optimized blade profiles and installation of automation systems and self-control. Also the possibility of substituting some of the pipes of the water supply system was considered in the recovery scenario in order to reduce leakages and recovery the energy needed for leakages transport and treatment. The analysis of the results shown that energy recovery scenario is the most reliable solution even without any pipe substitution. Thanks to the recovery of energy and limiting the environmental impact of the system, the CO2eq production per cubic meter of supplied water was reduced from 0.41 to 0.07 kg CO2eq/m3 of supplied water

A decision support tool for water and energy saving in the integrated water system

In the last decades, a growing attention on energy saving associated with water resources usage and leakages reduction has been recorded at both national and international level. Scientific research has focused on implementation of several methodologies aimed at the understanding of energy transformation processes occurring in the integrated water system. The main concern is then identifying energy impacts associated to each macro-area of integrated water system, such as collection, treatment and distribution, and analysing the potential interactions between them. Unfortunately, only overall energy consumptions are usually available at national level. The main objective of the paper is to present a decision support tool, developed in the framework of the ALADIN project, able to analysing the water and energy balance in the integrated water service. In order to achieve a sustainable use of water resources, the tool allows an assessment of the energy impact of different macro - areas of integrated water system. Moreover, each macro - area can be treated as an element able to share energy with other elements, aiming to obtain an energy saving on the whole integrated water system. In this way, the decision support tool could suggest efficient solutions, according to the operator objectives, with regard to energy and water losses management. Therefore, the tool could provide guidelines for choosing the best management solutions, depending on the particular analysed system, and allow, at the same time, the energy and water resources saving. The proposed tool was applied to a complex water supply system, the Favara di Burgio system (Sicily, Italy) in order to show its reliability

Pre-conditioning approach to Bayesian Decision Network for water quality sensors positioning in urban drainage systems

In the last decades, the growth of mini- and micro-industry in urban areas has produced an increase in the frequency of xenobiotic polluting discharges in drainage systems. Such pollutants are usually characterized by low removal efficiencies in urban wastewater treatment plants and they may have an acute or cumulative impact on environment. In order to facilitate early detection and efficient containment of the illicit intrusions, the present work aims to develop a decision-support approach for positioning the water quality sensors. It is mainly based on the use of a decision-making support of the BDN type (Bayesian Decision Network), specifically looking soluble conservative pollutants, such as metals. In the application and result section the methodology is tested on two sewer systems, with increasing complexity: a literature scheme from the SWMM manual and a real combined sewer