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    Evolutionary Algorithm for Selecting Wastewater System Configuration

    Source: Journal of Computing in Civil Engineering:;2018:;Volume ( 032 ):;issue: 006
    Author:
    Altarabsheh Ahmad;Ventresca Mario;Kandil Amr
    DOI: 10.1061/(ASCE)CP.1943-5487.0000791
    Publisher: American Society of Civil Engineers
    Abstract: This study proposes a new methodology for selecting a near-optimal configuration of wastewater system components considering the uncertainty in water consumption and population spatial distribution. The proposed methodology is composed of two stages: (1) lay out local sewer networks at the minimum possible cost as a tree graph, and (2) lay out the wastewater system, including the number, location, capacity, and service area of the planned treatment facilities for collecting, treating, and reusing municipal wastewater with minimum life-cycle costs and greenhouse gas emissions. The second stage uses a multiobjective genetic algorithm (NSGA-II) and Monte Carlo simulation, and a breadth-first search algorithm. The proposed methodology was applied to a sewer network in Sahab City, Jordan. The behavior of the proposed approach was statistically evaluated under positive and negative population growth rate scenarios, and the resulting behavior of the proposed algorithm was compared with the results of a current state-of-art planning algorithm that does not consider the change in population spatial distribution throughout the analysis period. The results show that the proposed algorithm outperforms the existing planning model by achieving statistically significant improvement in the life-cycle costs and greenhouse gas emissions at the end of the planning period.
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      Evolutionary Algorithm for Selecting Wastewater System Configuration

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4248652
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    contributor authorAltarabsheh Ahmad;Ventresca Mario;Kandil Amr
    date accessioned2019-02-26T07:40:34Z
    date available2019-02-26T07:40:34Z
    date issued2018
    identifier other%28ASCE%29CP.1943-5487.0000791.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4248652
    description abstractThis study proposes a new methodology for selecting a near-optimal configuration of wastewater system components considering the uncertainty in water consumption and population spatial distribution. The proposed methodology is composed of two stages: (1) lay out local sewer networks at the minimum possible cost as a tree graph, and (2) lay out the wastewater system, including the number, location, capacity, and service area of the planned treatment facilities for collecting, treating, and reusing municipal wastewater with minimum life-cycle costs and greenhouse gas emissions. The second stage uses a multiobjective genetic algorithm (NSGA-II) and Monte Carlo simulation, and a breadth-first search algorithm. The proposed methodology was applied to a sewer network in Sahab City, Jordan. The behavior of the proposed approach was statistically evaluated under positive and negative population growth rate scenarios, and the resulting behavior of the proposed algorithm was compared with the results of a current state-of-art planning algorithm that does not consider the change in population spatial distribution throughout the analysis period. The results show that the proposed algorithm outperforms the existing planning model by achieving statistically significant improvement in the life-cycle costs and greenhouse gas emissions at the end of the planning period.
    publisherAmerican Society of Civil Engineers
    titleEvolutionary Algorithm for Selecting Wastewater System Configuration
    typeJournal Paper
    journal volume32
    journal issue6
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000791
    page4018048
    treeJournal of Computing in Civil Engineering:;2018:;Volume ( 032 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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