Decolorization of the Reactive Blue 19 from Aqueous Solutions with the Fenton Oxidation Process and Modeling with Deep Neural Networks
Yazarlar (2)
Doç. Dr. Nejdet DEĞERMENCİ Kastamonu Üniversitesi, Türkiye
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Water Air and Soil Pollution (Q3) | ||
| Dergi ISSN | 0049-6979 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2020 |
| Kabul Tarihi | 06-01-2020 | Yayınlanma Tarihi | 01-02-2020 |
| Cilt / Sayı / Sayfa | 231 / 2 / 1–13 | DOI | 10.1007/s11270-020-4402-8 |
| Makale Linki | https://link.springer.com/article/10.1007/s11270-020-4402-8 | ||
| Özet |
| The decolorization of Reactive Blue 19 (RB19) from aqueous solutions using the Fenton oxidation process was researched. The effects of different operating parameters, e.g., H2O2, Fe(II), initial dye concentration, pH, and solution temperature, on the decolorization of RB19 were investigated. Increasing, the H2O2 concentration and temperature increased the rate of the decolorization; however, increasing initial RB19 concentration reduced the decolorization. Additionally, modeling of the decolorization obtained by the Fenton oxidation process was researched based on deep neural networks (DNN) architecture providing the best performance in terms of optimum hidden layers and neuron numbers in addition to ideal activation and optimization function pairs. The performances of the models were analyzed on the training, validation, and test data. According to the experimental results, the seven hidden layers DNN … |
| Anahtar Kelimeler |
| Decolorization | Deep neural networks | Reactive blue 19 | Wastewater treatment |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 8 |
| Scopus | 9 |
| Google Scholar | 13 |