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Journals / Ekoloji / 2013 / Cilt: 22 - Sayı: 88

Farklı tip yapay sulakalanların karasudan fenol gideriminde kullanımının yapay sinir ağları ile modellenmesi

Modeling different types of constructed wetlands for removing phenol from olive mill wastewater using an artificial neural network

Arda YALÇUK
Journal
Ekoloji
Pages
28–35
Publication Date
2022-10-17
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

ANN can easily be applied to improve the performance of horizontal and vertical type constructed wetlands.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:An Artificial Neural Network (ANN) was developed to model phenol removal in vertical and horizontal constructed wetlands.
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Key Findings & Data:The reactors were operated for 89 days.
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Conclusion & Impact:ANN can easily be applied to improve the performance of horizontal and vertical type constructed wetlands.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

An Artificial Neural Network (ANN) was developed to model phenol removal in vertical and horizontal constructed wetlands. For this, a pilot scale horizontal-flow (planted and unplanted) and three vertical-flow (planted and unplanted) reactors made of PVC were designed. In these reactor systems two wetland plants, Typha latifolia and Cyperus alternatifolus, and different bedding media were used. The reactors were operated for 89 days. A feed-forward network was used to model the wetlands. The network was fed with two subsets of operational data. The training procedure for effluent phenol concentrations from different wetlands was quite successful; a perfect match was obtained between measured and calculated concentrations. The ANN model was competent in providing a reasonable match between the measured and calculated concentration of phenol for the following scenarios: Horizontal flow with Cyperus (R2=1 for HF planted and R2=0.79 for HF unplanted), vertical flow with Cyperus (R2=0.99 for VF1 planted and R2=0.99 for VF1 unplanted), vertical flow with Typha (R2=0.99 for VF2 planted and R2=0.99 for VF2 unplanted), and vertical flow with Cyperus and Typha (R2=0.99 for VF3 planted and R2=0.99 for VF3 unplanted) in the effluents of the constructed wetlands. ANN can easily be applied to improve the performance of horizontal and vertical type constructed wetlands.

Özet

Bu çalışmada kullanılan ANN modeli, yatay ve dikey akışlı sulak alanlar ile karasudan phenol giderimi için geliştirilmiştir. Bu amaçla pilot ölçekli bir adet yatay akışlı (bitkili-bitkisiz) (planted-unplanted), üç adetde (planted- (bitkili-bitkisiz) unplanted) dikey akışlı PVC malzemeden yapılmış reactor tasarlanmıştır. Bu sistemlerde Typha latifolia ve Cyperus alternatifolus olmak üzere iki tip sulak alan bitkisi ve farklı bedding media kullanılmıştır. Sistemler 89 gün işletilmiştir. Sulakalanlarda ileri akımlı yapay sinir ağı modeli kullanılmıştır. Çalışmada ağ modeli iki alt küme ile beslenmiştir. Fenolun çıkış konsantrasyon verilerinin eğitimi ile ölçülen değerler ile hesaplanan değerler oldukça uyum göstermiştir. Sistemlerdeki çıkış konsantrasyonları arasındaki regrasyon katsayıları şu şekilde elde edilmiştir. Yatay akış Cyperus ile (R2=1 HF bitkili, R2=0,79 HF bitkisiz), Dikey Akış Cyperus ile (R2=0,99 VF1 bitkili, R2=0,99 VF1 bitkisiz), Dikey Akış Typha ile (R2=0,99 VF2 bitkili, R2=0,99 VF2 bitkisiz), Dikey Akış Cyperus ve Typha ile (R2=0,99 VF3 bitkili, R2=0,99 VF3 bitkisiz). Yatay ve dikey akışlı yapay sulakalanların geliştirilmesinde ANN uygulanabilir.

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Cite This Article

YALÇUK, A. (2022). Farklı tip yapay sulakalanların karasudan fenol gideriminde kullanımının yapay sinir ağları ile modellenmesi. Ekoloji, Cilt: 22 - Sayı: 88, 28–35. https://ajindex.com/articles/499009

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