LAPSE:2023.4337
Published Article

LAPSE:2023.4337
Response Surface Methodology (RSM)-Based Prediction and Optimization of the Fenton Process in Landfill Leachate Decolorization
February 22, 2023
Abstract
As an advanced oxidative processes, the Fenton process is receiving popularity as a wastewater treatment technique that can be used for hazardous landfill leachate. The treatment is simple, yet involves complex interactions between the affecting parameters including reaction time, H2O2/Fe2+ ratio, pH, and iron (II) ion concentration. Hence, the purpose of this present study was to analyze the factors affecting landfill leachate treatment as well as their interaction by means of response surface methodology (RSM) with central composite design. The independent variables were reaction time, H2O2/Fe2+ ratio, iron (II) ion concentration, and pH, and the dependent variable (response) was color-removal percentage. The optimum treatment conditions for pH, H2O2/Fe2+ ratio, Fe2+ concentration, and reaction time were 8.36, 3.32, 964.95 mg/L, and 50.15 min, respectively. The model predicted 100% color removal in optimum conditions, which was close to that obtained from the experiment (97.68%). In conclusion, the optimized Fenton process using the RSM approach promotes efficient landfill leachate treatment that is even higher than that already reported.
As an advanced oxidative processes, the Fenton process is receiving popularity as a wastewater treatment technique that can be used for hazardous landfill leachate. The treatment is simple, yet involves complex interactions between the affecting parameters including reaction time, H2O2/Fe2+ ratio, pH, and iron (II) ion concentration. Hence, the purpose of this present study was to analyze the factors affecting landfill leachate treatment as well as their interaction by means of response surface methodology (RSM) with central composite design. The independent variables were reaction time, H2O2/Fe2+ ratio, iron (II) ion concentration, and pH, and the dependent variable (response) was color-removal percentage. The optimum treatment conditions for pH, H2O2/Fe2+ ratio, Fe2+ concentration, and reaction time were 8.36, 3.32, 964.95 mg/L, and 50.15 min, respectively. The model predicted 100% color removal in optimum conditions, which was close to that obtained from the experiment (97.68%). In conclusion, the optimized Fenton process using the RSM approach promotes efficient landfill leachate treatment that is even higher than that already reported.
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Keywords
color reduction, Fenton process, landfill leachate, prediction and optimization, RSM
Subject
Suggested Citation
Roudi AM, Salem S, Abedini M, Maslahati A, Imran M. Response Surface Methodology (RSM)-Based Prediction and Optimization of the Fenton Process in Landfill Leachate Decolorization. (2023). LAPSE:2023.4337
Author Affiliations
Roudi AM: Department of Environmental Engineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor 81310, Malaysia [ORCID]
Salem S: Department of Economics, Birmingham Business School, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK [ORCID]
Abedini M: School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China; School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China
Maslahati A: Faculty of Mechanical Engineering, Qeshm Non-Profit University, Gheshm 79516-15897, Iran
Imran M: Business Studies Department, Bahria Business School, Bahria University Islamabad, Islamabad 44000, Pakistan [ORCID]
Salem S: Department of Economics, Birmingham Business School, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK [ORCID]
Abedini M: School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China; School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China
Maslahati A: Faculty of Mechanical Engineering, Qeshm Non-Profit University, Gheshm 79516-15897, Iran
Imran M: Business Studies Department, Bahria Business School, Bahria University Islamabad, Islamabad 44000, Pakistan [ORCID]
Journal Name
Processes
Volume
9
Issue
12
First Page
2284
Year
2021
Publication Date
2021-12-20
ISSN
2227-9717
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Original Submission
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PII: pr9122284, Publication Type: Journal Article
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LAPSE:2023.4337
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https://doi.org/10.3390/pr9122284
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Feb 22, 2023
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