Pollution source identification using a coupled diffusion model with a genetic algorithm

Khlaifi, Anis; Ionescu, Anda; Candau, Yves

doi:10.1016/j.matcom.2009.04.020

Cited by 36 publications

(8 citation statements)

References 13 publications

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“…Accurate estimates can be achieved with only three receptors, provided that these are placed down-wind the source. The error in the predictions the source flow rate are higher and much more sensitive to the number of receptor and on their location within the district (Akçelik et al , 2003;Rudd et al , 2012;Khlaifi et al, 2009). Future work will concern the extension of this analysis to different wind direction and obstacles layout, and to time-dependent pollutant emissions.…”

Section: Discussionmentioning

confidence: 96%

“…it admits a unique solution which depends continuously on the data. Otherwise, we need specific optimization and resolution algorithms (Giacobbo et al, 2002;Menut et al, 2006;Rudd et al, 2012;Delle Monache et al, 2008;Khlaifi et al, 2009) such as the Gauss-Newton method, the genetic algorithm, the Bayesian method, the MCMC. In our inverse code, the inversion of (1) varies depending on the number of pollutant sources and on the availability of receptor concentrations.…”

Section: Inverse Modelmentioning

confidence: 99%

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Pollutant source identification in a city district by means of a street network inverse model

Salem

Soulhac

Salizzoni

et al. 2014

IJEP

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This study presents the performances of an inverse modelling approach aiming in identifying position and emission rate of a localised pollutant source placed within a city district. To that purpose we combine wind tunnel experiments and an urban dispersion model. Experiments are performed in an idealised urban canopy, made up of regularly spaced blocks, and provide the pollutant concentration field downwind the source within the canopy. The urban dispersion model, named SIRANE, is an operational model that simulates the main mechanisms governing the pollutant transfer within a network of streets.

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Section: Discussionmentioning

confidence: 96%

Section: Inverse Modelmentioning

confidence: 99%

Pollutant source identification in a city district by means of a street network inverse model

Salem

Soulhac

Salizzoni

et al. 2014

IJEP

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“…N, Global Allen et al (2007aAllen et al ( , 2007b, Haupt (2005), Haupt et al (2006Haupt et al ( , 2009Khlaifi, Ionescu, and Candau (2009);Yang, Yang, Yin, and Li (2008) For the continuous release, the Gaussian plume model (Daniel & Joseph, 2002) was employed. The relevant formulation is…”

Section: Y Localmentioning

confidence: 99%

Inverse calculation approaches for source determination in hazardous chemical releases

Zheng

Chen

2011

Journal of Loss Prevention in the Process Industries

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“…Therefore, these uncertain factors should be considered for optimal sensor placement. The simulation-optimization method based on uncertain scenarios can be adopted [14][15][16][17][18]. These uncertain scenarios are usually generated through coarse enumeration [19][20][21] or sampling of a relatively small subset of the time and location of the injection with Markov Chain Monte Carlo (MCMC) methods, assuming a uniform probability distribution [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Robust Placement of Water Quality Sensor for Long-Distance Water Transfer Projects Based on Multi-Objective Optimization and Uncertainty Analysis

Chu

Wei

et al. 2021

Sustainability

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It is important to place water quality sensors along open channels in long-distance water transfer projects optimally for rapid source identification and efficient management of sudden water contamination. A new framework which considers multiple objectives, including earliest detection time, lowest missing detection rate and lowest sensor cost, and combines the randomness of injected contaminant type and contaminant incident consisting of contaminant intrusion location, time and mass, was established to obtain optimal placement of water quality sensor with better robustness in this paper. The middle route of the South-to-North Water Diversion Project in China was chosen as a case study, and it was found that both missing detection rate and detection time decrease with sensor cost gradually; furthermore, given the higher detecting precision, the detection accuracy and efficiency would be improved, a smaller number of water quality sensors would be needed, and the ten key placement positions where sensor with different detecting precision placed could be identified. Under the constraints of the allowable maximum missing detection rate, 1.00%, and detection time, 120.00 min, the detecting precision of 0.20 mg/L and 38 sensors placed could be selected as the optimal sensor placement scheme. Finally, with the consideration of contaminant uncertainty, the sensor placement scheme with better robustness could be constructed. The proposed framework would be helpful in solving the problem of water quality sensor placement with high practicality and efficiency in long-distance water transfer projects.

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Pollution source identification using a coupled diffusion model with a genetic algorithm

Cited by 36 publications

References 13 publications

Pollutant source identification in a city district by means of a street network inverse model

Pollutant source identification in a city district by means of a street network inverse model

Inverse calculation approaches for source determination in hazardous chemical releases

Robust Placement of Water Quality Sensor for Long-Distance Water Transfer Projects Based on Multi-Objective Optimization and Uncertainty Analysis

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