Performance Modeling of the Fenton Process Used as a Single Unit for Treating Raw Textile Effluent

Year 2024, Volume: 39 Issue: 3, 679 - 693, 03.10.2024

Selman Türkeş , Hakan Güney , Bülent Sarı , Olcayto Keskinkan

https://doi.org/10.21605/cukurovaumfd.1560112

Abstract

This study investigates the direct application of the Fenton Oxidation Process (FOP) to untreated textile wastewater, specifically from a woven fabric production facility. Under optimized conditions (pH 3, 0.7 g/L Fe+2, 2 mM H2O2), the process achieved significant removal efficiencies: 81% Chemical Oxygen Demand (COD), 80% Suspended Solids (SS), and 93% color removal. Artificial Neural Networks (ANN) and NARX-ANN were utilized in Matlab R2020a to model FOP performance, employing Levenberg-Marquardt (trainlm) and Scaled Conjugate Gradient (trainscg) algorithms. With a 9-20-3 network topology, the ANN model demonstrated superior predictive capability, achieving an R2 of 0.9843.

Keywords

Raw wastewater treatment, Fenton process, ANN, NARX-ANN models, Textile industry

Ham Tekstil Atık Sularının Arıtılması İçin Tek Bir Ünite Olarak Kullanılan Fenton Prosesinin Performans Modellemesi

Abstract

Bu çalışma, Fenton Oksidasyon Prosesinin (FOP) doğrudan arıtılmamış tekstil atık suyuna uygulanmasını, özellikle dokuma kumaş üretim tesisinden gelen atık suyu hedef alarak incelemektedir. Optimize edilmiş koşullar (pH 3, 0.7 g/L Fe+2, 2 mM H2O2) altında, proses %81 Kimyasal Oksijen İhtiyacı (KOİ), %80 Askıda Katı Madde (AKM) ve %93 renk giderimi gibi önemli verimlilikler sağlamıştır. FOP performansını modellemek amacıyla Matlab R2020a'da Yapay Sinir Ağları (YSA) ve NARX-YSA modelleri, Levenberg-Marquardt (trainlm) ve Ölçeklenmiş Eşlenik Gradyan (trainscg) algoritmaları kullanılarak oluşturulmuştur. 9-20-3 ağ topolojisine sahip YSA modeli, 0.9843 R2 değeri ile yüksek bir tahmin yeteneği göstermiştir.

Keywords

Ham atıksu arıtma, Fenton prosesi, YSA, NARX-YSA modelleri, Tekstil endüstrisi

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