Kalyan R. Piratla and Samuel T. Ariaratnam
The purpose of this paper is to investigate design alternatives for pump‐included water distribution networks considering sustainability and reliability aspects. The aim is to…
Abstract
Purpose
The purpose of this paper is to investigate design alternatives for pump‐included water distribution networks considering sustainability and reliability aspects. The aim is to demonstrate that CO2 emissions could be reduced at a reasonable cost. The paper also investigates the trade‐offs between cost and reliability of water distribution networks.
Design/methodology/approach
An existing genetic algorithm optimization tool is customized in this research to perform multi‐objective optimization with various objectives and constraints. The developed model is demonstrated using a benchmark water distribution network.
Findings
The results from this research suggest that CO2 emissions from water distribution networks could be reduced at a reasonable cost by choosing better objectives during the design stage. High system reliability could also be ensured for the lifetime by paying reasonable additional cost. This research presents various design alternatives for an engineer to choose from.
Research limitations/implications
The design of water distribution networks is a computationally complex process and often requires significant CPU time to arrive at an optimal solution. This problem is significant in the case of larger networks, especially when all the failed states need to be simulated. Simpler measures of reliability could be adopted in the future.
Originality/value
Although a significant amount of research had been undertaken in the area of optimal water distribution network design, only limited research includes environmental impacts as a design objective. This paper not only includes environmental aspects but also considers reliability. The model proposed in this research is a useful tool for engineers for considering various alternatives before choosing the best design.
Details
Keywords
Sepideh Yazdekhasti, Kalyan Ram Piratla, John C. Matthews, Abdul Khan and Sez Atamturktur
There has been a sustained interest over the past couple of decades in developing sophisticated leak detection techniques (LDTs) that are economical and reliable. Majority of…
Abstract
Purpose
There has been a sustained interest over the past couple of decades in developing sophisticated leak detection techniques (LDTs) that are economical and reliable. Majority of current commercial LDTs are acoustics based and they are not equally suitable to all pipe materials and sizes. There is also limited knowledge on the comparative merits of such acoustics-based leak detection techniques (ALDTs). The purpose of this paper is to review six commercial ALDTs based on four decisive criteria and subsequently develop guidance for the optimal selection of an ALDT.
Design/methodology/approach
Numerous publications and field demonstration reports are reviewed for evaluating the performance of various ALDTs in this study to inform their optimal selection using an integrated multi-criteria decision analysis (MCDA) framework. The findings are validated using interviews of water utility experts.
Findings
The study approach and the findings will have a broad impact on the water utility industry by identifying a suite of suitable ALDTs for a range of typical application scenarios. The evaluated ALDTs include listening devices, noise loggers, leak-noise correlators, free-swimming acoustic, tethered acoustic, and acoustic emissions. The evaluation criteria include cost, reliability, access requirements, and the ability to quantify leakage severity. The guidance presented in this paper will support efficient decision making in water utility management to minimize pipeline leakage.
Originality/value
This study attempts to address the problem of severe dearth of performance data for pipeline inspection techniques. Performance data reported in the published literature on various ALDTs are appropriately aggregated and compared using a MCDA, while the uncertainty in performance data is addressed using the Monte Carlo simulation approach.