# Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

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## Abstract

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## 1. Introduction

## 2. Contamination Source Identification (CSI) Problem

## 3. Water Quality Models

## 4. Solution Approaches to the Pipe Network Problem

## 5. Water Quality Modelling Approach

## 6. Available Simulation Tools

#### 6.1. EPANET

#### EPANET MSX

#### 6.2. PORTEAU

#### 6.3. Piccolo

#### 6.4. Synergi Water

#### 6.5. WaterGEMS

#### 6.6. H2ONET

## 7. Solution Approaches to Source Identification Problems

#### 7.1. Simulation–Optimisation Approach

#### 7.2. Probabilistic Approach

#### 7.3. Other Approaches

## 8. Summary of Existing Approaches

## 9. Challenges, Suggested Solutions and Future Directions

## 10. Conclusions

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Sample of water distribution network with contaminant injection adapted from [48].

**Figure 2.**Various approaches to the solution of pipe network analysis adapted from [78].

**Table 1.**Overview of water quality models: Adapted from [66].

Numerical Model | Governing Equation | Hydraulic Model | Citations |
---|---|---|---|

E-FDM | Advection-reaction equation | SSM | [58] |

E-DVM | Advection-reaction equation | DA | [64] |

E-FDM | Advection-reaction equation | TA | [67] |

Eulerian-Langragian method | Advection-diffusion-reaction equation | SSM | [68,69] |

L-MEDM | Advection-reaction equation | SSM | [70] |

L-EDM | Advection-reaction equation | SSM | [61] |

Specific Method | Classification | Remarks | Citations |
---|---|---|---|

NLP | Optimisation | Performance affected by source location and not up to large network | [32,112,170] |

PB | Optimisation | Explicit mathematical computation | [89,90] |

MIQP | Optimisation | Show positive result | [171] |

SO | Optimisation | Show robustness | [42,124,128,170,172] |

LSF | Optimisation | Show potential to reveal location | [126] |

MTLPA | Optimisation | Show efficiency | [173] |

GA | Optimisation | Revealed approximation time of injection | [114,116] |

FMC | Optimisation | Show applicability | [127] |

ADOPT | Optimisation | Converges to best solution | [43,44] |

QRLS | Optimisation | Show potential usage of the procedure | [130] |

RTM | Others | Fundamentals path was more efficient computationally | [49,174] |

BBN | Probability | Effective for steady flow condition for single instantaneous source | [40,149,150,151,153,154] |

ASA | Others | Show promising result | [175] |

ANN | Others | Positive correlation | [160] |

DMA | Others | [98] | |

ESHA | Others | Algorithm had good performance | [159] |

KST | Others | Indicates potential to detect source location | [166] |

DT | Others | Required further investigations | [158] |

MBA | Others | Show capability | [167] |

CSMHSM | Others | Demonstrated to identify location and evaluating degree of non-uniqueness | [162] |

HM | Others | Show robustness | [161] |

RP | Others | Show robustness | [151] |

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**MDPI and ACS Style**

Adedoja, O.S.; Hamam, Y.; Khalaf, B.; Sadiku, R.
Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network. *Water* **2018**, *10*, 579.
https://doi.org/10.3390/w10050579

**AMA Style**

Adedoja OS, Hamam Y, Khalaf B, Sadiku R.
Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network. *Water*. 2018; 10(5):579.
https://doi.org/10.3390/w10050579

**Chicago/Turabian Style**

Adedoja, Oluwaseye Samson, Yskandar Hamam, Baset Khalaf, and Rotimi Sadiku.
2018. "Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network" *Water* 10, no. 5: 579.
https://doi.org/10.3390/w10050579