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Optimized Artificial Intelligent Model to Boost the Efficiency of Saline Wastewater Treatment Based on Hunger Games Search Algorithm and ANFIS

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  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia
    Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61111, Egypt)

  • Abdul Ghani Olabi

    (Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK)

  • Enas Taha Sayed

    (Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Chemical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

  • Samah Ibrahim Alshathri

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Mohammad Ali Abdelkareem

    (Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Chemical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

Abstract

Chemical oxygen demand (COD) and total organic carbon (TOC) removal efficiencies of saline wastewater treatment indicate the efficiency of the electrochemical oxidation process. Therefore, the main target of this paper is to simultaneously increase COD and TOC removal efficiencies using artificial intelligence and modern optimization. Firstly, an accurate model based on ANFIS was established to simulate the electrochemical oxidation process in terms of reaction time, pH, salt concentration, and DC applied voltage. Compared with ANOVA, thanks to ANFIS modelling, the RMSE values are decreased by 84% and 86%, respectively, for COD and TOC models. Additionally, the coefficient of determination values increased by 3.26% and 7.87% for COD and TOC models, respectively. Secondly, the optimal reaction time values, pH, salt concentration, and applied voltage were determined using the hunger games search algorithm (HGSA). To prove the effectiveness of the HGSA, a comparison with a slime mold algorithm, sine cosine algorithm, and Harris’s hawks optimization was conducted. The optimal values were found at a pH of 8, a reaction time of 36.6 min, a salt concentration of 29.7 g/L, and a DC applied voltage of 9 V. Under this condition, the maximum COD and TOC removal values were 97.6% and 69.4%, respectively. The overall efficiency increased from 76.75% to 83.5% (increased by 6.75%).

Suggested Citation

  • Hegazy Rezk & Abdul Ghani Olabi & Enas Taha Sayed & Samah Ibrahim Alshathri & Mohammad Ali Abdelkareem, 2023. "Optimized Artificial Intelligent Model to Boost the Efficiency of Saline Wastewater Treatment Based on Hunger Games Search Algorithm and ANFIS," Sustainability, MDPI, vol. 15(5), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4413-:d:1084888
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    References listed on IDEAS

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    1. Nassef, Ahmed M. & Fathy, Ahmed & Sayed, Enas Taha & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Tanveer, Waqas Hassan & Olabi, A.G., 2019. "Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms," Renewable Energy, Elsevier, vol. 138(C), pages 458-464.
    2. Enas Taha Sayed & Hussain Alawadhi & Khaled Elsaid & A. G. Olabi & Maryam Adel Almakrani & Shaikha Tamim Bin Tamim & Ghada H. M. Alafranji & Mohammad Ali Abdelkareem, 2020. "A Carbon-Cloth Anode Electroplated with Iron Nanostructure for Microbial Fuel Cell Operated with Real Wastewater," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    3. Hung-Ta Wen & Jau-Huai Lu & Mai-Xuan Phuc, 2021. "Applying Artificial Intelligence to Predict the Composition of Syngas Using Rice Husks: A Comparison of Artificial Neural Networks and Gradient Boosting Regression," Energies, MDPI, vol. 14(10), pages 1-18, May.
    4. Hegazy Rezk & A. G. Olabi & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Enas Taha Sayed, 2023. "Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production," Sustainability, MDPI, vol. 15(3), pages 1-12, January.
    5. Hegazy Rezk & A. G. Olabi & Mohammad Ali Abdelkareem & Abdul Hai Alami & Enas Taha Sayed, 2023. "Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    6. Abdul Ghani Olabi & Salah Haridy & Enas Taha Sayed & Muaz Al Radi & Abdul Hai Alami & Firas Zwayyed & Tareq Salameh & Mohammad Ali Abdelkareem, 2023. "Implementation of Artificial Intelligence in Modeling and Control of Heat Pipes: A Review," Energies, MDPI, vol. 16(2), pages 1-18, January.
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