Sensitivity Analysis of an Agent-Based Model Used to Simulate the Spread of Low-Flow Fixtures for Residential Water Conservation and Evaluate Energy Savings in a Canadian Water Distribution System

2019 ◽  
Vol 145 (1) ◽  
pp. 04018086 ◽  
Author(s):  
Alexandre Tourigny ◽  
Y. Filion
2013 ◽  
Vol 15 (3) ◽  
pp. 862-880 ◽  
Author(s):  
M. Ehsan Shafiee ◽  
Emily M. Zechman

In the event that a contaminant is introduced to a water distribution network, a large population of consumers may risk exposure. Selecting mitigation actions to protect public health may be difficult, as contamination is a poorly predictable dynamic event. Consumers who become aware of an event may select protective actions to change their water demands from typical demand patterns, and new hydraulic conditions can arise that differ from conditions that would be predicted when demands are considered as exogenous inputs. Consequently, the movement of the contaminant plume in the pipe network may shift from its expected trajectory. A sociotechnical model is developed here to integrate agent-based models of consumers with an engineering water distribution system model and capture the dynamics between consumer behaviors and the water distribution system for predicting contaminant transport and public exposure. Consumers are simulated as agents with behaviors, including movement, water consumption, exposure, reduction in demands, and communication with other agents. As consumers decrease their water use, the location of the contaminant plume is updated and the amount of contaminant consumed by each agent is calculated. The framework is tested through simulating realistic contamination scenarios for a virtual city and water distribution system.


2017 ◽  
Vol 18 (5) ◽  
pp. 1554-1563
Author(s):  
Alexandra Archer ◽  
Brian D. Barkdoll

Abstract The practical energy minimization algorithm (EMA) is introduced here to determine if a water distribution system (WDS) can be less energy dependent. The EMA is a simple algorithm that can be used by practitioners in the planning and management of WDS. The EMA employs the Jatropha Curcas (JC) tree as a source of oil for fueling water pumps. The EMA is demonstrated on a WDS in Senegal, West Africa, and calculates the level of JC production required to be self-sufficient in fueling the water system to meet drinking, sanitation, and JC irrigation requirements. It was found that the EMA successfully showed that the demonstration WDS can be energy self-sufficient to provide recommended amounts of drinking water for the people and enough irrigation for the JC trees, but only if greywater was used to supplement the irrigation and if a mechanical press was used in lieu of a hand press to extract the oil from the JC leaves. An adequate amount of oil was thus produced to power the required mechanical press as well. Payback periods of significantly less than the life of the required equipment indicate the viability of JC oil as fuel and the feasibility of having an energy independent WDS.


2015 ◽  
Vol 17 (6) ◽  
pp. 891-916 ◽  
Author(s):  
Helena Mala-Jetmarova ◽  
Andrew Barton ◽  
Adil Bagirov

This paper presents an extensive analysis of the sensitivity of multi-objective algorithm parameters and objective function scaling tested on a large number of parameter setting combinations for a water distribution system optimisation problem. The optimisation model comprises two operational objectives minimised concurrently, the pump energy costs and deviations of constituent concentrations as a water quality measure. This optimisation model is applied to a regional non-drinking water distribution system, and solved using the optimisation software GANetXL incorporating the NSGA-II linked with the network analysis software EPANet. The sensitivity analysis employs a set of performance metrics, which were designed to capture the overall quality of the computed Pareto fronts. The performance and sensitivity of NSGA-II parameters using those metrics is evaluated. The results demonstrate that NSGA-II is sensitive to different parameter settings, and unlike in the single-objective problems, a range of parameter setting combinations appears to be required to reach a Pareto front of optimal solutions. Additionally, inadequately scaled objective functions cause the NSGA-II bias towards the second objective. Lastly, the methodology for performance and sensitivity analysis may be used for calibration of algorithm parameters.


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