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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2026 / Cilt: 25 Sayı: 2

Methylene Blue Adsorption onto Drinking Water Treatment Plant’s Sludge and Modeling by Artificial Neural Network

İÇME SUYU ARITMA TESİSİ ATIK ÇAMURU ÜZERİNE METİLEN MAVİSİ ADSORPSİYONU VE YAPAY SİNİR AĞLARI İLE MODELLENMESİ

Nurcan Öztürk(Karadeniz Teknik Üniversitesi)0000-0002-2907-5941
Hasan Şentürk(KARADENİZ TEKNİK ÜNİVERSİTESİ)0000-0002-4272-0255
Ali Gündoğdu(GÜMÜŞHANE ÜNİVERSİTESİ)0000-0002-9594-4121
Celal Duran(KARADENİZ TEKNİK ÜNİVERSİTESİ)0000-0002-1306-9061
Pages
1083–1104
Publication Date
2020-08-31
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

Artificial Neural Network (ANN) model was developed considering the effects of certain experimental parameters for adsorption of Methylene Blue on DWTS and it was concluded that DWTS can be used to remove organic pollutants from wastewaters

📌 3 Key Takeaways

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Objective & Hypothesis:In this work, it was investigated usability of Drinking Water Treatment Sludge (DWTS), obtained as end product at the end of treatment stages, as an adsorbent for removal of Methylene Blue, the organic pollutant (dyestuff), present in wastewaters.
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Key Findings & Data:DWTS’s maximum adsorption capacity obtained from Langmuir model for Methylene Blue was 62.50 mg g-1.
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Conclusion & Impact:Artificial Neural Network (ANN) model was developed considering the effects of certain experimental parameters for adsorption of Methylene Blue on DWTS and it was concluded that DWTS can be used to remove organic pollutants from wastewaters
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Metilen mavisi · Adsopsiyon · Yapay Sinir Ağları

Abstract

In this work, it was investigated usability of Drinking Water Treatment Sludge (DWTS), obtained as end product at the end of treatment stages, as an adsorbent for removal of Methylene Blue, the organic pollutant (dyestuff), present in wastewaters. DWTS used for the adsorption experiments were not subjected to physical or chemical pre-treatment. Firstly, DWTS was characterized employing various analytical procedures including determination of moisture, density, volatiles, fixed carbon, ash, as well as elemental, SEM, FT-IR, XRD, XRF and TGA/DTA analysis. Then, adsorption experiments were carried out in a batch system and DWTS’s removal potential of the pollutant specie from aqueous solution was investigated in terms of equilibrium, kinetics and thermodynamics. DWTS’s maximum adsorption capacity obtained from Langmuir model for Methylene Blue was 62.50 mg g-1. Artificial Neural Network (ANN) model was developed considering the effects of certain experimental parameters for adsorption of Methylene Blue on DWTS and it was concluded that DWTS can be used to remove organic pollutants from wastewaters

Özet

Bu çalışmada içme suyu arıtma tesislerinde arıtım aşamaları sonucu oluşan İçme Suyu Arıtma Tesisi Atık Çamurunun (İSATAÇ), atık sularda organik kökenli bir kirletici (boyar madde) olan Metilen Mavisinin uzaklaştırılmasında adsorban olarak kullanılabilirliği araştırılmıştır. Adsorpsiyon çalışmalarında kullanılan İSATAÇ fiziksel ya da kimyasal ön işleme tabi tutulmamıştır. Öncelikle İSATAÇ’ın nem, yoğunluk, uçucu madde, sabit karbon, kül tayini yanı sıra elementel, SEM, FT-IR, XRD, XRF ve TGA/DTA analiz gibi çeşitli analitik işlemlerle karakterize edilmiştir. Sonrasında adsorpsiyon deneyleri kesikli sistemle gerçekleştirilmiş ve İSATAÇ’ın sulu çözeltiden bu kirletici türü uzaklaştırma potansiyeli denge, kinetik ve termodinamik parametreler açısından incelenmiştir. Metilen mavisinin uzaklaştırılmasında Langmuir izoterm modelinden elde edilen maksimum adsorplama kapasitesi 62,50 mg g-1 olarak tespit edilmiştir. Bazı deneysel parametrelerin İSATAÇ üzerinde Metilen Mavisi adsorpsiyonuna etkileri göz önüne alınarak Yapay Sinir Ağı (YSA) modeli geliştirilmiş ve İSATAÇ’ın atık sulardan organik kirleticilerin uzaklaştırılmasında kullanılabileceği sonucuna varılmıştır.

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Cite This Article

Öztürk, N., Şentürk, H., Gündoğdu, A., Duran, C. (2020). Methylene Blue Adsorption onto Drinking Water Treatment Plant’s Sludge and Modeling by Artificial Neural Network. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 25 Sayı: 2, 1083–1104. https://doi.org/10.17482/uumfd.674224

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