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Machine Learning Modeling of Aerobic Biodegradation for Azo Dyes and Hexavalent Chromium

Author

Listed:
  • Zulfiqar Ahmad

    (State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China)

  • Hua Zhong

    (State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China)

  • Amir Mosavi

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
    Department of Automation, Obuda University, 1034 Budapest, Hungary
    Department of Mathematics, J. Selye University, 94501 Komarno, Slovakia
    Department of Informatics, J. Selye University, 94501 Komarno, Slovakia)

  • Mehreen Sadiq

    (Department of Environmental Sciences, PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan)

  • Hira Saleem

    (Department of Environmental Sciences, PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan)

  • Azeem Khalid

    (Department of Environmental Sciences, PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan)

  • Shahid Mahmood

    (Department of Environmental Sciences, PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan)

  • Narjes Nabipour

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam)

Abstract

The present study emphasizes the efficacy of a biosurfactant-producing bacterial strain Klebsiella sp. KOD36 in biodegradation of azo dyes and hexavalent chromium individually and in a simultaneous system. The bacterial strain has exhibited a considerable potential for biodegradation of chromium and azo dyes in single and combination systems (maximum 97%, 94% in an individual and combined system, respectively). Simultaneous aerobic biodegradation of azo dyes and hexavalent chromium (SBAHC) was modeled using machine learning programming, which includes gene expression programming, random forest, support vector regression, and support vector regression-fruit fly optimization algorithm. The correlation coefficient includes the dispersion index, and the Willmott agreement index was employed as statistical metrics to assess the performance of each model separately. In addition, the Taylor diagram was used to further investigate the methods used. The findings of the present study were that the support vector regression-fruitfly optimization algorithm (SVR-FOA) with correlation coefficient (CC) of 0.644, (scattered index) SI of 0.374, and (Willmott’s index of agreement) WI of 0.607 performed better than the autonomous support vector regression (SVR), gene expression programming (GEP), and random forest (RF) methods. In addition, the standalone SVR model with CC of 0.146, SI of 0.473, and WI of 0.408 ranked the second best. In summary, the SBAHC can be accurately estimated using the hybrid SVR-FOA method. In other words, FOA has proven to be a powerful optimization algorithm for increasing the accuracy of the SVR method.

Suggested Citation

  • Zulfiqar Ahmad & Hua Zhong & Amir Mosavi & Mehreen Sadiq & Hira Saleem & Azeem Khalid & Shahid Mahmood & Narjes Nabipour, 2020. "Machine Learning Modeling of Aerobic Biodegradation for Azo Dyes and Hexavalent Chromium," Mathematics, MDPI, vol. 8(6), pages 1-17, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:913-:d:367232
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    References listed on IDEAS

    as
    1. Chuncai Xiao & Kuangrong Hao & Yongsheng Ding, 2015. "An Improved Fruit Fly Optimization Algorithm Inspired from Cell Communication Mechanism," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-15, March.
    2. Nabipour, Narjes & Daneshfar, Reza & Rezvanjou, Omid & Mohammadi-Khanaposhtani, Mohammad & Baghban, Alireza & Xiong, Qingang & Li, Larry K.B. & Habibzadeh, Sajjad & Doranehgard, Mohammad Hossein, 2020. "Estimating biofuel density via a soft computing approach based on intermolecular interactions," Renewable Energy, Elsevier, vol. 152(C), pages 1086-1098.
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    Cited by:

    1. Fatemehsadat Mirshafiee & Emad Shahbazi & Mohadeseh Safi & Rituraj Rituraj, 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study," Energies, MDPI, vol. 16(1), pages 1-20, January.

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