Venturi Kanalının Havalandırma Performansının Yapay Arı Kolonisi Programlaması ile Tahmin Edilmesi

Year 2022, , 389 - 398, 01.03.2022

Zeynep Banu Özger , Ayşe Ece Yağcı , Mehmet Unsal

https://doi.org/10.2339/politeknik.972844

Cited By: 1

Abstract

Su kalitesinin en önemli göstergelerinden biri sudaki çözünmüş oksijen konsantrasyonudur. Azalan oksijen konsantrasyonunun değerini artırmak için atmosferden transfer edilen oksijene havalandırma denir. Havalandırma için birçok hidrolik yapı kullanılır. Kapılı borular ve venturi, son yıllarda popüler hale gelen hidrolik yapılardır. Venturi boğaz kısmına ve boğaz kısmına yerleştirilmiş hava deliğine sahiptir. Venturi girişi ile boğaz kısmı arasında havalandırmayı sağlayan bir basınç farkı oluşur. Geçitli bir kanalda, kapı kısmen açıldığında, kapının yukarı ve aşağı akışları arasında basınç farkı oluşur. Hava, kapının akış aşağısında açılan bir havalandırma deliğinden akışa girer. Yeni bir havalandırma sistemi olan venturi-conduit olarak adlandırılan dairesel bir kanalın hava deliğine bir venturi yerleştirildi. Temel yapısı Yapay Arı Kolonisi algoritmasına dayanan Yapay Arı Kolonisi Programlama (ABCP) algoritması, sembolik regresyon problemi için önerilen bir otomatik programlama yöntemidir. Bu çalışmada, venturili kondüitlerin havalandırma performansını modelleyebilecek fonksiyonlar elde etmek için ABCP önerilmiştir. Daralma oranı farklı boyutlarda olan 2 venturili konduit üzerinde deneysel ölçülmüş veri ile eğitilen yöntemin sonuçları yapay sinir ağları ve genetik programlama ile karşılaştırılmıştır. ABCP, her 2 veri kümesinde de, 0,99 R2 değeri ile test verisinde genetik programlama ve yapay sinir ağlarından daha iyi performans göstermiştir. 2 veri kümesinde sırasıyla 1,64 ve 2,66 RMSE değerleri ABCP'nin problem için uygun fonksiyonu üretme yeteneğine sahip olduğunu göstermektedir.

Keywords

Havalandırma, Hava Enjeksiyonu, yapay arı kolonisi programlama, venturi-conduit

Estimating The Aeration Performance Of Venturi-Conduit By Artificial Bee Colony Programming

Abstract

Oxygen concentration dissolved in water is an important parameter used to measure the quality of water. Increasing the concentration of oxygen by transferring oxygen from the atmosphere is named as aeration. Aeration can be performed with many hydraulic structures. Two of the structures that have become widespread in the field of hydraulics are gated conduit and venturi. Venturi has throat part and air hole located into throat part. A difference of pressure, which provides aeration occurs between the venturi inlet and the throat part. A pressure difference occurs between up and down flows of the door in a partially opened gated conduit. Air entrains into flow from an air vent that were drilled downstream of the gate. A venturi was placed on air hole of a circular conduit that as called venturi-conduit, a new aeration system. The basic steps of the Artificial Bee Colony Programming (ABCP) developed for the solution of the symbolic regression problem come from the Artificial Bee Colony (ABC). In this study, ABCP is proposed to obtain functions that can model the ventilation performance of conduits with venture-conduit. The results of the method trained with experimentally measured data on 2 ventures with different contraction ratios were compared with artificial neural networks and genetic programming. ABCP outperformed genetic programming and neural networks on test data with an R2 value of 0.99 in both datasets. The RMSE values of 1.64 and 2.66, respectively, in the 2 data sets indicate that ABCP is capable of generating the appropriate function for the problem.

Keywords

Aeration, Air Injection, Artificial Bee Colony Programming, Venturi-Conduit

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