Hexavalent chromium Cr(VI) is a highly toxic pollutant that poses a significant threat to human health and the environment. Electrocoagulation is a promising technology for the removal of Cr(VI) from wastewater. This work reviews and evaluates statistical models developed in different studies published between 2015 and 2021 on the removal of Cr(VI) using electrocoagulation. The analysis showed that none of the models was found to be conclusive, and that they all suffer from issues such as overfitting and the inability to generalize beyond the experiment domain. These models were also highly dependent on the selection of input parameters, model selection criteria, and experimental design. An attempt to solve this problem was to utilize Machine Learning (ML) techniques to develop a more robust model that can provide generalized and accurate predictions on a broader domain. The model was developed using Support Vector Machines Regression analysis (SVR). Data compiled from previously published works were used to train and test the model using a 50:50 split ratio. The model was able to make more generalized predictions but lacked accuracy. As with all ML models, this model requires a higher volume of high-quality data to improve its accuracy. The study concluded that there is still a need for more robust statistical models that can effectively capture the complexity of the electrocoagulation process and generalize well beyond the experiment domain.
| Published in | American Journal of Chemical Engineering (Volume 11, Issue 5) |
| DOI | 10.11648/j.ajche.20231105.11 |
| Page(s) | 85-94 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Statistical Models, Electrocoagulation, Hexavalent Chromium, Machine Learning
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APA Style
Salem, M., Abdelmonem, N., Nassef, E. (2023). Development of a Generalized Statistical Model for Hexavalent Chromium Removal Using Electrocoagulation Through SVR Regression Analysis. American Journal of Chemical Engineering, 11(5), 85-94. https://doi.org/10.11648/j.ajche.20231105.11
ACS Style
Salem, M.; Abdelmonem, N.; Nassef, E. Development of a Generalized Statistical Model for Hexavalent Chromium Removal Using Electrocoagulation Through SVR Regression Analysis. Am. J. Chem. Eng. 2023, 11(5), 85-94. doi: 10.11648/j.ajche.20231105.11
AMA Style
Salem M, Abdelmonem N, Nassef E. Development of a Generalized Statistical Model for Hexavalent Chromium Removal Using Electrocoagulation Through SVR Regression Analysis. Am J Chem Eng. 2023;11(5):85-94. doi: 10.11648/j.ajche.20231105.11
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Download@article{10.11648/j.ajche.20231105.11,
author = {Mohamad Salem and Nabil Abdelmonem and Ehssan Nassef},
title = {Development of a Generalized Statistical Model for Hexavalent Chromium Removal Using Electrocoagulation Through SVR Regression Analysis},
journal = {American Journal of Chemical Engineering},
volume = {11},
number = {5},
pages = {85-94},
doi = {10.11648/j.ajche.20231105.11},
url = {https://doi.org/10.11648/j.ajche.20231105.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajche.20231105.11},
abstract = {Hexavalent chromium Cr(VI) is a highly toxic pollutant that poses a significant threat to human health and the environment. Electrocoagulation is a promising technology for the removal of Cr(VI) from wastewater. This work reviews and evaluates statistical models developed in different studies published between 2015 and 2021 on the removal of Cr(VI) using electrocoagulation. The analysis showed that none of the models was found to be conclusive, and that they all suffer from issues such as overfitting and the inability to generalize beyond the experiment domain. These models were also highly dependent on the selection of input parameters, model selection criteria, and experimental design. An attempt to solve this problem was to utilize Machine Learning (ML) techniques to develop a more robust model that can provide generalized and accurate predictions on a broader domain. The model was developed using Support Vector Machines Regression analysis (SVR). Data compiled from previously published works were used to train and test the model using a 50:50 split ratio. The model was able to make more generalized predictions but lacked accuracy. As with all ML models, this model requires a higher volume of high-quality data to improve its accuracy. The study concluded that there is still a need for more robust statistical models that can effectively capture the complexity of the electrocoagulation process and generalize well beyond the experiment domain.
},
year = {2023}
}
TY - JOUR T1 - Development of a Generalized Statistical Model for Hexavalent Chromium Removal Using Electrocoagulation Through SVR Regression Analysis AU - Mohamad Salem AU - Nabil Abdelmonem AU - Ehssan Nassef Y1 - 2023/12/08 PY - 2023 N1 - https://doi.org/10.11648/j.ajche.20231105.11 DO - 10.11648/j.ajche.20231105.11 T2 - American Journal of Chemical Engineering JF - American Journal of Chemical Engineering JO - American Journal of Chemical Engineering SP - 85 EP - 94 PB - Science Publishing Group SN - 2330-8613 UR - https://doi.org/10.11648/j.ajche.20231105.11 AB - Hexavalent chromium Cr(VI) is a highly toxic pollutant that poses a significant threat to human health and the environment. Electrocoagulation is a promising technology for the removal of Cr(VI) from wastewater. This work reviews and evaluates statistical models developed in different studies published between 2015 and 2021 on the removal of Cr(VI) using electrocoagulation. The analysis showed that none of the models was found to be conclusive, and that they all suffer from issues such as overfitting and the inability to generalize beyond the experiment domain. These models were also highly dependent on the selection of input parameters, model selection criteria, and experimental design. An attempt to solve this problem was to utilize Machine Learning (ML) techniques to develop a more robust model that can provide generalized and accurate predictions on a broader domain. The model was developed using Support Vector Machines Regression analysis (SVR). Data compiled from previously published works were used to train and test the model using a 50:50 split ratio. The model was able to make more generalized predictions but lacked accuracy. As with all ML models, this model requires a higher volume of high-quality data to improve its accuracy. The study concluded that there is still a need for more robust statistical models that can effectively capture the complexity of the electrocoagulation process and generalize well beyond the experiment domain. VL - 11 IS - 5 ER -
Chemical Engineering Department, Faculty of Engineering, Cairo University, Cairo, Egypt
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