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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2017 / Cilt: 22 Sayı: 3

Investigation of the Treatability of Hand-printed Textile Wastewaters by UV/H2O2 Using Artificial Neural Networks

EL BASKISI İŞLETMELERİNDEN KAYNAKLANAN TEKSTİL ATIKSULARININ UV/H2O2 PROSESİYLE ARITILABİLİRLİĞİNİN YAPAY SİNİR AĞLARI İLE ARAŞTIRILMASI

Melike Yalılı Kılıç(ULUDAĞ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ÇEVRE MÜHENDİSLİĞİ BÖLÜMÜ)
Taner Yonar(ULUDAĞ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ÇEVRE MÜHENDİSLİĞİ BÖLÜMÜ)
Pages
201–212
Publication Date
2017-12-31
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AI Summary & Key Takeaways

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

According to the results, pH is the predominant variable, and the reaction mean time and H 2 O 2 volume are the less influential variables.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, the photooxidation of pollutants such as COD and color present in hand-printed textile wastewaters has been carried out in the presence of hydrogen peroxide (H 2 O 2 ), using 256 nm UV light (15 W), and the results of the treatment has been presented.
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Key Findings & Data:The obtained data has been successfully tested through a NeuroSolutions 5.06 model.
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Conclusion & Impact:According to the results, pH is the predominant variable, and the reaction mean time and H 2 O 2 volume are the less influential variables.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Arıtma · el baskısı tekstil atıksuyu · UV/H2O2

Abstract

In this study, the photooxidation of pollutants such as COD and color present in hand-printed textile wastewaters has been carried out in the presence of hydrogen peroxide (H 2 O 2 ), using 256 nm UV light (15 W), and the results of the treatment has been presented. The obtained data has been successfully tested through a NeuroSolutions 5.06 model. Each sample has been characterized by three independent variables (i.e., pH, H 2 O 2 concentration, and time of operation) and two dependent variables (i.e., color and COD). According to the results, pH is the predominant variable, and the reaction mean time and H 2 O 2 volume are the less influential variables. The neural model obtained presented coefficients of correlation of 99% for COD and 99% for color, indicating the prediction power of the model and its character of generalization.

Özet

Bu çalışmada, el baskısı işletmelerinden kaynaklanan tekstil atıksularındaki KOİ ve renk gibi kirleticilerin fotooksidasyonu, UV/H 2 O 2 prosesi ile 256 nm dalga boyunda, 15 W’lık lambaların kullanımıyla gerçekleştirilmiş ve arıtma sonuçları sunulmuştur. Veriler NeuroSolutions 5.06 model ile başarılı bir şekilde test edilmiştir. Her bir örnek, üç bağımsız değişken (pH, H 2 O 2 konsantrasyonu, işletme süresi) ve iki bağımlı değişken (renk ve KOİ) ile karakterize edilmiştir. Sonuçlar, pH’ın baskın değişken olduğunu, reaksiyon süresi ile H 2 O 2 konsantrasyonunun daha az etkili değişkenler olduğunu göstermiştir. Model, KOİ için %99 ve renk için %99 korelasyon katsayıları sunarken, modelin tahmin gücünü ve genellemenin karakterini belirtmektedir.

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Cite This Article

Kılıç, M. Y., Yonar, T. (2017). Investigation of the Treatability of Hand-printed Textile Wastewaters by UV/H2O2 Using Artificial Neural Networks. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 22 Sayı: 3, 201–212. https://doi.org/10.17482/uumfd.320528

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