Identification of Factors That Influence Energy Performance in Water Distribution System Mains

Abstract

This paper aims at identifying paramount hydraulic factors in energy dynamics of water mains, using Principal Components Analysis (PCA). The proposed method is applied to two large ensembles of leaky and non-leaky pipes comprising over 40,000 pipes selected from 18 North American water distribution systems to guarantee the versatility of pipe characteristics and statistical significance of the explored patterns. PCA mono-plots indicate energy metrics such as Net Energy Efficiency, Energy Lost to Friction and Energy Lost to Leakage serve better in identification of low from high efficiency pipes. In addition, PCA mono-plots and bi-plots reveal relative importance of hydraulic parameters and that average flow rate, hydraulic proximity to major components and average unit headloss can have more tangible effects on energy dynamics of pipes compared to leakage and average pressure. Some factors such as elevation, diameter and C HW are not as influential as expected in distinguishing high-efficiency from low-efficiency pipes. Further, a comparison between the approach used in this paper and a simplified common-practice replacement strategy points out the difference energy considerations can make, if included in a bigger asset management landscape.

City Water Resource Allocation Considering Energy Consumption in Jinan, China

“…This study quantifies and compares the energy consumption benchmarks for the supply, consumption, drainage, and treatment of different water sources in Jinan [24][25][26][27] and comprehensively organizes the energy consumption levels of each form of water resource utilization.…”

Examining the Relative Impact of Drivers on Energy Input for Municipal Water Supply in Africa

“…PCA was chosen as a multi-variate statistical tool to identify the indicators with the highest component loadings and hence the highest influence on energy input. PCA reduces the dimensionality of the data by building up on correlation analysis to identify the indicators, which account for the largest proportion of variance in the dataset that are not captured by the correlation analysis [42,43]. As outlined in [42], only those principal components with the highest loadings were assigned attributes to show the impact on energy input.…”

“…PCA reduces the dimensionality of the data by building up on correlation analysis to identify the indicators, which account for the largest proportion of variance in the dataset that are not captured by the correlation analysis [42,43]. As outlined in [42], only those principal components with the highest loadings were assigned attributes to show the impact on energy input. The analysis was performed for twenty (20) countries in sub-Saharan Africa, which were selected based on the best possible complete data available.…”

Methodology for Determining the Maximum Potentially Recoverable Energy in Water Distribution Networks

“…Specifically, these works identify the hydraulic parameters that have the greatest impact on energy performance in WDNs. In this sense, Hashemi et al [7] propose to a methodology based on three steps: i) determine the hydraulic parameters with the greatest influence on the energy performance of the WDN; ii) carry out combinations of hydraulic parameters that allow distinguishing the most/least efficient pipelines from the energy point of view; and iii) look for the effect that a pipeline rehabilitation (based on age or breakage rate) would have on the energy efficiency of WDNs. These actions are related by measuring the energy that can be recovered, and it is measured from the characteristics of the network.…”

“…This is obtained by analyzing the relationship between the pipeline and hydraulic parameters such as the transported flow, roughness coefficient, pipeline diameter, unit losses, pressure, elevation and the hydraulic proximity to the main components of the WDN. In general, energy dynamics together with risk assessment, pipeline age, breakage rates and water quality can help prioritize pipeline replacement and rehabilitation, and should be considered as part of the big picture to improve the overall performance of a WDN [7]. These correlations allow to initially identify the parameters that could be relevant for the installation of possible ER points or devices.…”