Chlorine dosage management in drinking water systems: comparing Bayesian optimization to evolutionary algorithms

To maintain a sufficient chlorine residual in water distribution systems (WDSs), chlorine dosage needs to be regulated. The majority of previous studies that aimed to optimize chlorine dosage in WDSs considered single-species water quality (WQ) models featuring chlorine decay with simple reaction ki...

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Bibliographic Details
Published inJournal of hydroinformatics Vol. 26; no. 11; pp. 2720 - 2738
Main Authors Moeini, Mohammadreza, Abokifa, Ahmed
Format Journal Article
LanguageEnglish
Published London IWA Publishing 01.11.2024
Subjects
Algorithms
Bayesian analysis
Bayesian theory
Chlorine
Computational efficiency
Disinfection & disinfectants
Dosage
Drinking water
Emission standards
Evolutionary algorithms
Genetic algorithms
Geographical distribution
Kinetics
Linear programming
Mathematical models
Optimization
Optimization algorithms
Partial differential equations
Probability theory
Reaction kinetics
Scheduling
Sensitivity analysis
Simulation
Water distribution
Water distribution systems
Water engineering
Water quality
Water treatment
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Summary:To maintain a sufficient chlorine residual in water distribution systems (WDSs), chlorine dosage needs to be regulated. The majority of previous studies that aimed to optimize chlorine dosage in WDSs considered single-species water quality (WQ) models featuring chlorine decay with simple reaction kinetics. Recent efforts have proposed using multi-species water quality (MS-WQ) models to account for chlorine interactions with various chemical and microbiological species, thus providing a comprehensive and accurate evaluation of the WQ within WDSs. Nevertheless, the key challenge of implementing MS-WQ models within optimization frameworks is their high computational cost and poor scalability for larger WDSs. Furthermore, previous optimization studies generally relied on evolutionary algorithms (EAs), which require conducting a significant number of WQ simulations. Bayesian optimization (BO) has been recently suggested as an efficient alternative to EAs for the optimization of computationally expensive functions. This study aims to present a systematic comparison between BO and other widely used EAs for the optimization of MS-WQ in WDSs. A case study featuring a real-life, midsized benchmark WDS was implemented to comprehensively evaluate all three optimization techniques. The results revealed that BO is notably more computationally efficient and less sensitive to changes in the constraints than EAs.
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ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2024.090