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Lookup NU author(s): Anna Laino, Dr Ben WoodingORCiD, Professor Russell DavenportORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Royal Society of Chemistry.This study develops quantifiable metrics to describe the resilience of Water Resource Recovery Facilities (WRRFs) under extreme stress events, including those posed by long-term challenges such as climate change and population growth. Resilience is the ability of the WRRFs to withstand adverse events while maintaining compliance or an operational level of service. Existing studies lack standardised resilience measurement methods. In this paper, we propose a resilience metric based on signal temporal logic (STL) to describe acceptable functionality of the WRRFs (e.g. meeting regulatory limits). By using Monte Carlo simulations and scenario optimisation on a model of a WRRF, we determine the maximum stress the WRRF can handle while meeting STL constraints for biochemical oxygen demand (BOD) and chemical oxygen demand (COD) compliance limits. The results are applied to a simple digital model of a facility with 22 components. Importantly, this method can be applied to data that water companies routinely and regularly monitor, and could be incorporated into SCADA systems. In our case studies, we determine threshold stressor values of extreme rainfall that result in a loss of resilience. Our results offer insights into the design of more resilient treatment processes to reduce environmental impacts.
Author(s): Laino AS, Wooding B, Soudjani S, Davenport RJ
Publication type: Article
Publication status: Published
Journal: Environmental Science: Water Research and Technology
Year: 2024
Pages: epub ahead of print
Online publication date: 31/10/2024
Acceptance date: 15/10/2024
Date deposited: 02/12/2024
ISSN (print): 2053-1400
ISSN (electronic): 2053-1419
Publisher: Royal Society of Chemistry
URL: https://doi.org/10.1039/D4EW00649F
DOI: 10.1039/d4ew00649f
Data Access Statement: The python codes can be found at the following link: https:// github.com/annalaino/coding-paper.git. The data and Python code were developed in collaboration with industrial partners and cannot be published online due to a non-disclosure agreement (NDA) between the authors and the industrial partners. A simplified version of the code may be published in the future, pending approval from the industrial partners. If approved, a link to the code will be included in the final version of the paper.
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