Artificial Neural Network-driven Optimization of Fe3 O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation

Efficient degradation of industrial dyes remains a critical challenge in environmental engineering. This study introduces a novel Fe3 O4 nanoparticles/PVDF macrospheres in a Fenton-like system, optimized using an Artificial Neural Network (ANN) for the degradation of Methylene Blue (MB). A feedforwa...

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Published in:Journal of Advanced Research in Micro and Nano Engineering
Main Author: Osman M.S.; Khairudin K.; Hassan H.; Sam S.-T.; Nazeri N.B.M.; Mustapa N.L.S.; Fitriyanti M.
Format: Article
Language:English
Published: Penerbit Akademia Baru 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203092056&doi=10.37934%2farmne.22.1.6884&partnerID=40&md5=ffc4885690fbf8dc2f61f631e22986f1
id 2-s2.0-85203092056
spelling 2-s2.0-85203092056
Osman M.S.; Khairudin K.; Hassan H.; Sam S.-T.; Nazeri N.B.M.; Mustapa N.L.S.; Fitriyanti M.
Artificial Neural Network-driven Optimization of Fe3 O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation
2024
Journal of Advanced Research in Micro and Nano Engineering
22
1
10.37934/armne.22.1.6884
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203092056&doi=10.37934%2farmne.22.1.6884&partnerID=40&md5=ffc4885690fbf8dc2f61f631e22986f1
Efficient degradation of industrial dyes remains a critical challenge in environmental engineering. This study introduces a novel Fe3 O4 nanoparticles/PVDF macrospheres in a Fenton-like system, optimized using an Artificial Neural Network (ANN) for the degradation of Methylene Blue (MB). A feedforward backpropagation neural network model to optimize and predict the performance of this advanced oxidation process under various operational conditions. The model was trained, validated, and tested with robust datasets, demonstrating high predictive accuracy and generalization capability. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) during testing were 0.0200 and 0.1414, respectively, indicating precise predictions. The coefficient of determination (R²) and correlation coefficient (R) were exceptionally high at 0.9744 and 0.9871, affirming the model's ability to capture the underlying dynamics of the degradation process effectively. The ANN-driven approach not only enhanced the efficiency of the MB degradation process but also provided significant insights into the scalability and applicability of the Fe3 O4 /PVDF system for practical water treatment solutions. This study underscores the potential of integrating advanced machine learning techniques with chemical engineering processes to achieve sustainable and efficient environmental management solutions, particularly for the treatment of recalcitrant wastewater contaminants. © 2024, Penerbit Akademia Baru. All rights reserved.
Penerbit Akademia Baru
27568210
English
Article
All Open Access; Hybrid Gold Open Access
author Osman M.S.; Khairudin K.; Hassan H.; Sam S.-T.; Nazeri N.B.M.; Mustapa N.L.S.; Fitriyanti M.
spellingShingle Osman M.S.; Khairudin K.; Hassan H.; Sam S.-T.; Nazeri N.B.M.; Mustapa N.L.S.; Fitriyanti M.
Artificial Neural Network-driven Optimization of Fe3 O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation
author_facet Osman M.S.; Khairudin K.; Hassan H.; Sam S.-T.; Nazeri N.B.M.; Mustapa N.L.S.; Fitriyanti M.
author_sort Osman M.S.; Khairudin K.; Hassan H.; Sam S.-T.; Nazeri N.B.M.; Mustapa N.L.S.; Fitriyanti M.
title Artificial Neural Network-driven Optimization of Fe3 O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation
title_short Artificial Neural Network-driven Optimization of Fe3 O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation
title_full Artificial Neural Network-driven Optimization of Fe3 O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation
title_fullStr Artificial Neural Network-driven Optimization of Fe3 O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation
title_full_unstemmed Artificial Neural Network-driven Optimization of Fe3 O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation
title_sort Artificial Neural Network-driven Optimization of Fe3 O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation
publishDate 2024
container_title Journal of Advanced Research in Micro and Nano Engineering
container_volume 22
container_issue 1
doi_str_mv 10.37934/armne.22.1.6884
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203092056&doi=10.37934%2farmne.22.1.6884&partnerID=40&md5=ffc4885690fbf8dc2f61f631e22986f1
description Efficient degradation of industrial dyes remains a critical challenge in environmental engineering. This study introduces a novel Fe3 O4 nanoparticles/PVDF macrospheres in a Fenton-like system, optimized using an Artificial Neural Network (ANN) for the degradation of Methylene Blue (MB). A feedforward backpropagation neural network model to optimize and predict the performance of this advanced oxidation process under various operational conditions. The model was trained, validated, and tested with robust datasets, demonstrating high predictive accuracy and generalization capability. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) during testing were 0.0200 and 0.1414, respectively, indicating precise predictions. The coefficient of determination (R²) and correlation coefficient (R) were exceptionally high at 0.9744 and 0.9871, affirming the model's ability to capture the underlying dynamics of the degradation process effectively. The ANN-driven approach not only enhanced the efficiency of the MB degradation process but also provided significant insights into the scalability and applicability of the Fe3 O4 /PVDF system for practical water treatment solutions. This study underscores the potential of integrating advanced machine learning techniques with chemical engineering processes to achieve sustainable and efficient environmental management solutions, particularly for the treatment of recalcitrant wastewater contaminants. © 2024, Penerbit Akademia Baru. All rights reserved.
publisher Penerbit Akademia Baru
issn 27568210
language English
format Article
accesstype All Open Access; Hybrid Gold Open Access
record_format scopus
collection Scopus
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