Modeling of Dispersed Red 17 Dye Removal from an Aqueous Solution Using Artificial Neural Network

• Autorzy: Ibrahim Abdullah I. , Asmel Nabel K. , Alabdraba Waleed M. S. , Al-Nima Raid R. O.

Identyfikatory

DOI

10.12911/22998993/175673

• Języki publikacji: EN

Abstrakty

EN

A significant amount of hazardous compounds has leaked into the environment due to the widespread usage of organic dyes, and it is essential that these dangerous contaminants be removed in a sustainable way. This study used varying amounts of H₂O₂ (0, 0.5, 1.5, 3, and 5) mM/L to extract the dye from the aqueous solution. Furthermore, concentrations of 0.4, 1, 1.7, and 2.3 mM/L of Fe⁺² as FeSO₄•7H₂O were also utilized. Batch Advanced Oxidation Process (AOP) was carried out under various working conditions, including: contact time (5–60 min), mixing speed (100–300 rpm), and UV light intensity (0–40 W). Utilizing experimental data, the AOP efficiency of Dispersed Red 17 Dye was calculated. Genetic Cascade-forward Neural Network (GCNN) was employed as a machine-learning tool to forecast the oxidation efficiency and the amount of dye that would be removed from the aqueous solution, specifically Dispersed Red 17. When compared to experimental data, the best model had an R² correlation value of 0.955. The findings of the importance analysis showed that the studied parameters affected the discoloration efficiency with order of: H₂O₂, UV, Fe⁺², mixing speed, and contact time. The obtained results demonstrated the effectiveness of GCNN as a novel approach in forecasting the AOP efficiency of Dispersed Red 17 Dye.

Słowa kluczowe

EN

modelling; wastewater; AOP; advanced oxidation process; dye removal; artificial neural network; GCNN

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2024
• Tom: Vol. 25, nr 2
• Strony: 10--19
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Ibrahim Abdullah I.

• Department of Environmental Engineering, University of Mosul, Mosul, Iraq

• https://orcid.org/0000-0002-7391-3885

autor

Asmel Nabel K.

• Building and Construction Technology Engineering, Northern Technical University, Mosul, Iraq

autor

Alabdraba Waleed M. S.

• Department of Environmental Engineering, University of Tikrit, Tikrit, Iraq

autor

Al-Nima Raid R. O.

• Technical Engineering College of Mosul, Northern Technical University, Mosul, Iraq

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).