Research Article | | Peer-Reviewed

Development of a Generalized Statistical Model for Hexavalent Chromium Removal Using Electrocoagulation Through SVR Regression Analysis

Received: 7 November 2023     Accepted: 27 November 2023     Published: 8 December 2023
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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.

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), 2023. Published by Science Publishing Group

Keywords

Statistical Models, Electrocoagulation, Hexavalent Chromium, Machine Learning

References
[1] K. Thirugnanasambandham and K. Shine, "Investigation on the Removal of Chromium from Wastewater using Electrocoagulation," International Journal of Chemical Reactor Engineering, vol. 16, 2018.
[2] Xiaokun Wang, Yingqin Wei, Shasha Wang, Lingxin Chen, "Red-to-blue colorimetric detection of chromium via Cr (III)-citrate chelating based on Tween 20-stabilized gold nanoparticles," Colloids and Surfaces A: Physicochemical and Engineering Aspects, pp. 57-62, 2015.
[3] S. Dhiraj, M. Garima and K. M. P., "Agricultural waste material as potential adsorbent for sequestering heavy metal ions from aqueous solutions – A review," Bioresource Technology, vol. 99, no. 14, pp. 6017-6027, 2008.
[4] H. Jean, G. Bouchaib, C. Mohammed, S. Youssef, V. Christophe, D. Patrick and N. Jamal, "Electrocoagulation process in water treatment: A review of electrocoagulation modeling approaches," Desalination, no. 404, pp. 1-21, 2017.
[5] P. R. Sunil and S. P. Parikh, "Statistical optimizing of electrocoagulation process for the removal of Cr(VI) using response surface methodology and kinetic study," Arabian Journal of Chemistry, vol. 13, no. 9, pp. 7032-7044, 2020.
[6] R. H. Salman, H. A. Hassan, K. M. Abed, A. F. Al-Alawy, D. A. Tuama, K. M. Hussein and H. A. Jabir, "Removal of chromium ions from a real wastewater of leather industry using electrocoagulation and reverse osmosis processes," in AIP Conference Proceedings, 2020.
[7] E. Aguilar-Ascón, L. Marrufo-Saldaña and W. Neyra-Ascón, "Reduction of Total Chromium Levels from Raw Tannery Wastewater via Electrocoagulation using Response Surface Methodology," Journal of Ecological Engineering, vol. 20, no. 11, pp. 217-224, 2019.
[8] N. M. Genawi, M. H. Ibrahim, M. H. El-Naas and A. E. Alshaik, "Chromium Removal from Tannery Wastewater by Electrocoagulation: Optimization and Sludge Characterization," Water, vol. 12, no. 5, 2020.
[9] U. Tezcan Un, S. Eroglu Onpeker and E. Ozel, "The treatment of chromium containing wastewater using electrocoagulation and the production of ceramic pigments from the resulting sludge," Journal of Environmental Management, vol. 200, pp. 196-203, 2017.
[10] Y. A. El-Taweel, E. M. Nassef, I. Elkheriany and D. Sayed, "Removal of Cr(VI) ions from waste waterby electrocoagulation using iron electrode," Egyptian Journal of Petroleum, vol. 24, no. 2, pp. 183-192, 2015.
[11] A. Prasetyaningrum, B. Jos, Y. Dharmawan, B. T. Prabowo, M. Fathurrazan and Fyrouzabadi, "The influence of electrode type on electrocoagulation process for removal of chromium (VI) metal in plating industrial wastewater," Journal of Physics: Conference Series, vol. 1025, 2018.
[12] V. Gilhotra, R. Yadav, A. Sugha, L. Das, A. Vashisht, R. Bhatti and M. S. Bhatti, "Electrochemical treatment of high strength chrome bathwater: A comparative study for best-operating conditions," Cleaner Engineering and Technology, vol. 2, 2021.
[13] F. Zhang and L. J. O'Donnell, "Chapter 7 - Support vector regression," in Machine Learning, Academic Press, 2020, pp. 123-140.
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Cite This Article
  • 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

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    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

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    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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  • @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}
    }
    

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  • 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
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    Y1  - 2023/12/08
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    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
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    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  - 

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Author Information
  • Chemical Engineering Department, Faculty of Engineering, Cairo University, Cairo, Egypt

  • Chemical Engineering Department, Faculty of Engineering, Cairo University, Cairo, Egypt

  • Petrochemical Eng. Department, Faculty of Engineering, Pharos University, Alexandria, Egypt

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