Venturi Kanalının Havalandırma Performansının Yapay Arı Kolonisi Programlaması ile Tahmin Edilmesi

Yıl 2022, Cilt: 25 Sayı: 1, 389 - 398, 01.03.2022

Zeynep Banu Özger , Ayşe Ece Yağcı , Mehmet Unsal

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

Cited By: 1

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• [1] [1] İlçin E. “Oxygen Transfer of Stepped Spillways”. M.S. Thesis, Firat University, Elazig, Institute of Science, (2005).
• [2] Baylar A. “Study On the Effect of Type Selection for Oxygen Transfer of Weir Aerator”, PhD Thesis, Firat University, Elazig, Institute of Science, (2002).
• [3] Kalinske A. A., Robertson J. M. “Closed Conduit Flow”, Transactions of the Symposium on Entrainment of Air in Flowing Water, 108 (1), 1435−1447, (1943).
• [4] Campbell F. B., Guyton B. “Air demand in gated outlet Works. Proceedings of the 5th Congress of the International Association of Hydraulic Research, Minneapolis, Minnesota, 529–533, (1953).
• [5] Sharma H. R. “Air-Entrainment In High Head Gated Conduits”, Journal of the Hydraulics Division, 102(11), 1629-1646, (1976).
• [6] Harshbarger E. D., Vigander S. and Hecker G. E. “Discussion Of Air-Entrainment In High Head Gated Conduits”, Journal of Hydraulic Division, 103(12), 1486– 1488, (1977).
• [7] Speerli J., Hager W. H. “Air−Water Flow In Bottom Outlets”, Canadian Journal of Civil Engineering, 27(3), 454– 462, (2000).
• [8] Ünsal M. ,Baylar A., Tugal M., Özkan F. “Aeration Efficiency Of Free-Surface Conduit Flow Systems”, Environmental Technology, 30(14), 1539-1546, (2009).
• [9] Baylar A., Özkan F., Ünsal M. “Effect of Air Inlet Hole Diameter of Venturi Tube on Air Injection Rate”, KSCE Journal of Civil Engineering, 14 (4), 489-492, (2010).
• [10] Yağcı A. E., Ünsal M., Ercan B. “Investigation the Aeration Performance of a New Aerator: Venturi-Conduit”, FEB - Fresenius Environmental Bulletin, 29(2), 917-930, (2020).
• [11] Yücel P., Ünsal M. and Yağcı A. E. “Effect of Air Hole Diameter in Circular Conduits for Air Injection”, Iranian Journal of Science and Technology, Transactions of Civil Engineering, 43(1), 561–567, (2018).
• [12] Karaboga D. “An Idea Based On Honey Bee Swarm For Numerical Optimization”, Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, 200, 1-10, (2005).
• [13] Rao H., Shi X., Rodrigue A. K., Feng J., Xia Y., Elhoseny M. and Gu L. “Feature Selection Based On Artificial Bee Colony And Gradient Boosting Decision Tree”, Applied Soft Computing, 74, 634-642, (2019).
• [14] Hajimirzaei B. and Navimipour N. J. “Intrusion Detection For Cloud Computing Using Neural Networks And Artificial Bee Colony Optimization Algorithm”, ICT Express, 5(1), 56-59, (2019).
• [15] Famila S., Jawahar A., Sariga A. and Shankar K. “Improved Artificial Bee Colony Optimization Based Clustering Algorithm For SMART Sensor Environments”, Peer-to-Peer Networking and Applications, 1-9, (2019).
• [16] Cihan P. and Özger Z. B. “A New Heuristic Approach For Treating Missing Value: ABCimp”, Elektronika ir Elektrotechnika, 25(6), 48-54, (2019).
• [17] Abu-Mouti F. S. and El-Hawary M. E. “Optimal Distributed Generation Allocation And Sizing In Distribution Systems Via Artificial Bee Colony Algorithm”, IEEE Transactions On Power Delivery, 26(4), 2090-2101, (2011).
• [18] Omkar S. N., Senthilnath J., Khandelwal R., Naik G. N. and Gopalakrishnan S. “Artificial Bee Colony (ABC) For Multi-Objective Design Optimization Of Composite Structures”, Applied Soft Computing, 11(1), 489-499, (2011).
• [19] Chen C., Xu B., Mei C., Ding Y. and Li K. “Teaching–Learning–Based Artificial Bee Colony For Solar Photovoltaic Parameter Estimation”, Applied Energy, 212, 1578-1588, (2018).
• [20] Gong D., Han Y., Sun J. “A Novel Hybrid Multi-Objective Artificial Bee Colony Algorithm For Blocking Lot-Streaming Flow Shop Scheduling Problems”, Knowledge-Based Systems, 148, 115-130, (2018).
• [21] Asteris P. G. and Nikoo M. “Artificial Bee Colony-Based Neural Network For The Prediction Of The Fundamental Period Of Infilled Frame Structures”, Neural Computing and Applications, 31(9), 4837-4847, (2019).
• [22] Karaboga D., Ozturk C., Karaboğa N., and Görkemli B. “Artificial Bee Colony Programming For Symbolic Regression”, Information Sciences, 209, 1-15, (2012).
• [23]Özkan F. “Study of Air Entrainment and Oxygen Transfer at Pressure Conduit”, PhD Thesis, Firat University, Elazig, Institute of Science, (2005).
• [24]Ferreira, C. “Gene Expression Programming: a New Adaptive Algorithm for Solving Problems”, Complex Syst, 13(2), 87-129, (2001).
• [25]Ünsal, M. “GEP Modeling of Penetration Depth in Sharp Crested Weirs”, Arabian Journal for Science and Engineering, 37(8), 2163-2174, (2012).
• [26]Özbek, A., Ünsal, M. and Dikeç, A. “Estimating uniaxial compressive strength of rocks using genetic expression programming”, Journal of Rock Mechanics and Geotechnical Engineering, 5(4), 325-329, (2013).
• [27] Chaudhuri, B.B. and Bhattacharya, U. “Efficient training and improved performance of multilayer perceptron in pattern classification”, Neurocomputing, 43, 3-31, (2000).
• [28]Fausett, L. Fundamentals of neural networks architectures. Prentice Hall, NJ: Algorithms and Applications, (1994).
• [29]Luo, F.L. and Unbehaben, R. Applied neural networks for signal processing. Cambridge University Press, (1998).
• [30]Yağcı, A. E., Ünsal, M. and Yılmaz, A. E. “Venturili Konduitlerde Hava Giriş Oranının Yapay Sinir Ağları ile Modellenmesi”, IMSEC 2016, Adana-Turkey, 3102-3012, (2016).
• [31] www.gepsoft.com, “GeneXproTools” demo version, (October 2018).