نشت یابی شبکه‌های توزیع آب با استفاده از مدل تلفیقی کلونی مورچه ها (ACO) و حذف مرحله ای (SSEM)

محورهای موضوعی : Business Administration and Entrepreneurship

علی نصیریان ¹ , مسعود ثابت ²

1 - استادیار گروه عمران آب، دانشکده مهندسی ، دانشگاه بیرجند، بیرجند، ایران
2 - دانشجو کارشناسی ارشد رشته عمران، دانشکده مهندسی، دانشگاه بیرجند، بیرجند، ایران

تاریخ دریافت : 1395/04/10 تاریخ پذیرش : 1400/06/10 تاریخ انتشار : 1403/02/01

کلید واژه: بهینه یابی, کالیبراسیون, شبکه توزیع آب, نشت‌یابی, ACO,

چکیده مقاله :

چکیده
مقدمه: روش های رایج نشت یابی بسیار هزینه بر و وقت گیر می باشند. لذا اخیراً روش‌هایی که با مدل­سازی شبکه به صورت فراگیر نشت را جستجو می‌کنند، مورد توجه قرار گرفته اند.
روش­: در این روش ها با کالیبراسیون شبکه و تنظیم مصارف در گره ها، اختلاف بین مقادیر فشار و دبی اندازه گیری شده و بدست آمده از مدل به عنوان تابع برازندگی کمینه می شود. در تحلیل ­ها ثابت شد که تابع برازندگی به تنهایی نمی تواند راهنمای خوبی برای رسیدن به نقطه کمینه باشد. برای رفع این مشکل روش ACO  استفاده شد. این روش علاوه بر برازندگی از پارامتری به نام مطلوبیت  نیز استفاده می کند که قابلیت های روش را افزایش می دهد.
یافته ­ها: در این تحقیق سه روش بهینه یابی بر پایه ACO مورد مقایسه قرار گرفتند. روش نخست ACO با مطلوبیت ثابت است که قبلاً در مراجع معرفی شده است. در روش دوم مقدار تابع مطلوبیت بر اساس مقادیر فشارهای مشاهداتی و میزان افت فشار نسبت به حالت بدون نشت، متغیر است. آخرین روش با تلفیق دو مدل SSEM و ACO مورد بررسی و مقایسه قرار گرفت. این روش ها بر روی شبکه ای برگرفته از مراجع و همچنین شبکه بیرجند مورد بررسی قرار گرفت.
نتیجه­ گیری: نتایج نشان می دهند که روش های پیشنهادی این مقاله در زمان و تعداد تکرار کمتر به نتایج  دقیق تری دست یافته اند.
 
 

چکیده انگلیسی:

Abstract
Introduction: Conventional leak detection methods are costly and time-consuming, so attention has recently been drawn to methods that detect leakage by network modeling comprehensively.
Methods: In these methods, the difference between the measured pressure and flow rate and those obtained by the model is minimized as a fitness function by calibrating the network and adjusting the nodal demands. It has been established by analysis that the fitness function in itself cannot be a good guide to achieving the minimum point. This drawback was solved by using the ACO method. In addition to the fitness, this method employs a parameter called heuristic guidance to improve its capabilities.
Results: This research compared three ACO-based optimization methods. The first was ACO with fixed heuristic guidance already introduced in the literature. In the second method, the heuristic guidance varies by the values of the observed pressures and pressure decline versus the no-leak state. The third is studied and compared by integrating SSEM and ACO models. These methods were investigated on a network derived from the literature and the Birjand network.
Conclusion: The results revealed that the methods proposed here achieved more precise results in a shorter time and with fewer iterations.

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