Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate
Abstract Sustainable municipal solid waste leachate (MSWL) management requires a paradigm shift from removing contaminants to effectively recovering resources and decreasing contaminants simultaneously. In this study, two types of humic substances, fulvic acid (FA) and humic acid (HA) were extracted...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-39373-2 |
| _version_ | 1797752705661272064 |
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| author | Salimeh Rezaeinia Ali Asghar Ebrahimi Arash Dalvand Mohammad Hassan Ehrampoush Hossien Fallahzadeh Mehdi Mokhtari |
| author_facet | Salimeh Rezaeinia Ali Asghar Ebrahimi Arash Dalvand Mohammad Hassan Ehrampoush Hossien Fallahzadeh Mehdi Mokhtari |
| author_sort | Salimeh Rezaeinia |
| collection | DOAJ |
| description | Abstract Sustainable municipal solid waste leachate (MSWL) management requires a paradigm shift from removing contaminants to effectively recovering resources and decreasing contaminants simultaneously. In this study, two types of humic substances, fulvic acid (FA) and humic acid (HA) were extracted from MSWL. HA was extracted using HCl and NaOH solution, followed by FA using a column bed under diversified operations such as flow rate, input concentration, and bed height. Also, this work aims to evaluate efficiency of Artificial Neural Network (ANN) and Dynamic adsorption models in predicting FA. With the flow rate of 0.3 mL/min, bed height of 15.5 cm, and input concentration of 4.27 g/mL, the maximum capacity of FA was obtained at 23.03 mg/g. FTIR analysis in HA and FA revealed several oxygen-containing functional groups including carboxylic, phenolic, aliphatic, and ketone. The high correlation coefficient value (R2) and a lower mean squared error value (MSE) were obtained using the ANN, indicating the superior ability of ANN to predict adsorption capacity compared to traditional modeling. |
| first_indexed | 2024-03-12T17:08:25Z |
| format | Article |
| id | doaj.art-97a453b1438142938be6935d939dc3c5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T17:08:25Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-97a453b1438142938be6935d939dc3c52023-08-06T11:15:32ZengNature PortfolioScientific Reports2045-23222023-08-0113111410.1038/s41598-023-39373-2Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachateSalimeh Rezaeinia0Ali Asghar Ebrahimi1Arash Dalvand2Mohammad Hassan Ehrampoush3Hossien Fallahzadeh4Mehdi Mokhtari5Environmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical SciencesEnvironmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical SciencesEnvironmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical SciencesEnvironmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical SciencesDepartment of Biostatistics and Epidemiology, Research Center of Prevention and Epidemiology of Non‑Communicable Disease, Shahid Sadoughi University of Medical SciencesEnvironmental Science and Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical SciencesAbstract Sustainable municipal solid waste leachate (MSWL) management requires a paradigm shift from removing contaminants to effectively recovering resources and decreasing contaminants simultaneously. In this study, two types of humic substances, fulvic acid (FA) and humic acid (HA) were extracted from MSWL. HA was extracted using HCl and NaOH solution, followed by FA using a column bed under diversified operations such as flow rate, input concentration, and bed height. Also, this work aims to evaluate efficiency of Artificial Neural Network (ANN) and Dynamic adsorption models in predicting FA. With the flow rate of 0.3 mL/min, bed height of 15.5 cm, and input concentration of 4.27 g/mL, the maximum capacity of FA was obtained at 23.03 mg/g. FTIR analysis in HA and FA revealed several oxygen-containing functional groups including carboxylic, phenolic, aliphatic, and ketone. The high correlation coefficient value (R2) and a lower mean squared error value (MSE) were obtained using the ANN, indicating the superior ability of ANN to predict adsorption capacity compared to traditional modeling.https://doi.org/10.1038/s41598-023-39373-2 |
| spellingShingle | Salimeh Rezaeinia Ali Asghar Ebrahimi Arash Dalvand Mohammad Hassan Ehrampoush Hossien Fallahzadeh Mehdi Mokhtari Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate Scientific Reports |
| title | Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate |
| title_full | Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate |
| title_fullStr | Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate |
| title_full_unstemmed | Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate |
| title_short | Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate |
| title_sort | application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate |
| url | https://doi.org/10.1038/s41598-023-39373-2 |
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