Top-Down and Bottom-Up Approaches for Water-Energy Balance in Portuguese Supply Systems

With increasing population, the need for research ideas on the field of reducing wastage of water can save a big amount of water, money, time, and energy. Water leakage (WL) is an essential problem in the field of water supply field. This research is focused on real water loss in the water distribution system located in Ethiopia. Top-down and bursts and background estimates (BABE) methodology is performed to assess the data and the calibration process of the WL variables. The top-down method assists to quantify the water loss by the record and observation throughout the distribution network. In addition, the BABE approach gives a specific water leakage and burst information. The geometrical mean method is used to forecast the population up to 2023 along with their fiscal value by the uniform tariff method. With respect to the revenue lost, 42575 Br and 42664 Br or in 1562$ and 1566$ were lost in 2017 and 2018, respectively. The next five-year population was forecasted to estimate the possible amount of water to be saved, which was about 549627 m3 and revenue 65,111$ to make the system more efficient. The results suggested that the majority of losses were due to several components of the distribution system including pipe-joint failure, relatively older age pipes, poor repairing and maintenance of water taps, pipe joints and shower taps, negligence of the consumer and unreliable water supply. As per the research findings, recommendations were proposed on minimizing water leakage.

Section: Methods To Assess the Data By Top-down Approachmentioning

confidence: 99%

Section: Water Loss Analysis By Babe Methodsmentioning

confidence: 99%

Evaluating Physical and Fiscal Water Leakage in Water Distribution System

Bhagat

Tiyasha

Welde

et al. 2019

“…In more advanced cases, energy recovery from turbines or cogeneration is explored. Only in recent years, systemic approaches to assess other sources of inefficiency in drinking water systems, such as inadequate layout and operation and energy associated with water losses, have started to be explored and have demonstrated a high potential for improving efficiency [7][8][9][10]. There remains a need to adapt and explore these approaches to wastewater and stormwater systems to assess inefficiencies associated with sewer inflow, infiltration and network layout.…”

Section: Introductionmentioning

confidence: 99%

“…It is therefore structured by efficiency and effectiveness criteria, and the PIs and their reference values evaluate (i) equipment efficiency, e.g., pumps, equipment for sewer cleaning, aerators, (ii) system efficiency (i.e., due to water losses, undue inflows or inadequate network layout) and (iii) effectiveness. In addition to earlier developed and tested PIs [10,11,19,20], new PIs are herein proposed, e.g., to assess energy efficiency associated with wastewater collection and transport, sewers cleaning, wastewater collection from on-site treatment systems and sludge disposal. Regarding the effectiveness assessment, we propose a set of PIs for each stage that focuses on aspects of the quality of service related to energy consumption or efficiency.…”

Section: Introductionmentioning

confidence: 99%

The Development of a Framework for Assessing the Energy Efficiency in Urban Water Systems and Its Demonstration in the Portuguese Water Sector

Loureiro

Silva

Cardoso

et al. 2020

Self Cite

Urban water systems (UWSs) are energy-intensive worldwide, particularly for drinking-water pumping and aeration in wastewater treatment. Usual approaches to improve energy efficiency focus only on equipment and disregard the UWS as a continuum of stages from source-to-tap-to-source (abstraction/transport—treatment—drinking water transport/distribution—wastewater and stormwater collection/transport—treatment—discharge/reuse). We propose a framework for a comprehensive assessment of UWS energy efficiency and a four-level approach to enforce it: overall UWS (level 1), stage (level 2), infrastructure component (level 3) and processes/equipment (level 4). The framework is structured by efficiency and effectiveness criteria (an efficient but ineffective infrastructure is useless), earlier and newly developed performance indicators and reference values. The framework and the approach are the basis for a sound diagnosis and intervention prioritising, and are being tested in a peer-to-peer innovation project involving 13 water utilities (representing 17% of the energy consumption by the Portuguese water sector in 2017). Results of levels 1–3 of analysis herein illustrated for a water utility demonstrate the framework and approach potential to assess UWS effectiveness and energy efficiency, and to select the stages and infrastructures for improvement and deeper diagnosis.

“…Though various applications of top-down and bottom-up approaches exist in the scientific literature, as reported above, few comparative works have been presented so far [22], aimed at assessing the effectiveness of such approaches in both water resources planning [23] and in identifying main energy inefficiencies in water systems [24]. However, the present paper was aimed at presenting this comparison in a real case study, made up of a smart water network in Naples (Italy), highlighting the differences in synthetic time series generation in terms of statistics, such as media, standard deviation, skewness and correlations.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Bottom-Up and Top-Down Procedures for Water Demand Reconstruction

Fiorillo

Creaco

Paola

et al. 2020

This paper presents a comparison between two procedures for the generation of water demand time series at both single user and nodal scales, a top-down and a bottom-up procedure respectively. Both procedures are made up of two phases. The top-down procedure adopted includes a non-parametric disaggregation based on the K-nearest neighbours approach. Therefore, once the temporal aggregated water demand patterns have been defined (first phase), the disaggregation is used to generate water demand time series at lower levels of spatial aggregation (second phase). In the bottom-up procedure adopted, demand time series for each user and for each time step are generated applying a beta probability distribution with tunable bounds or a gamma distribution with shift parameter (first phase). Then, a Copula based re-sort is applied to the demand time series generated to impose existing rank cross-correlations between users and at all temporal lags (second phase). For the sake of comparison, two case studies were considered, both of which are related to a smart water network in Naples (Italy). The results obtained show that the bottom-up procedure performs significantly better than the top-down procedure in terms of rank-cross correlations at fine scale. However, the top-down procedure showed a better performance in terms of skewness and rank cross-correlation when the aggregated demands were considered. Finally, the level of aggregation in nodes was found to affect the performance of both the procedures considered.