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Artificial Intelligence Based Modelling of Adsorption Water Desalination System

Author

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  • Hesham Alhumade

    (Chemical and Materials Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Center of Excellence in Desalination Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Hegazy Rezk

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 11911, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, 61517 Minia, Egypt)

  • Abdulrahim A. Al-Zahrani

    (Chemical and Materials Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Sharif F. Zaman

    (Chemical and Materials Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Ahmed Askalany

    (Mechanical Engineering Department, Faculty of Industrial Education, Sohag University, Sohag 82524, Egypt)

Abstract

The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.

Suggested Citation

  • Hesham Alhumade & Hegazy Rezk & Abdulrahim A. Al-Zahrani & Sharif F. Zaman & Ahmed Askalany, 2021. "Artificial Intelligence Based Modelling of Adsorption Water Desalination System," Mathematics, MDPI, vol. 9(14), pages 1-13, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:14:p:1674-:d:595460
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    References listed on IDEAS

    as
    1. Tanveer, Waqas Hassan & Rezk, Hegazy & Nassef, Ahmed & Abdelkareem, Mohammad Ali & Kolosz, Ben & Karuppasamy, K. & Aslam, Jawad & Gilani, Syed Omer, 2020. "Improving fuel cell performance via optimal parameters identification through fuzzy logic based-modeling and optimization," Energy, Elsevier, vol. 204(C).
    2. Rezk, Hegazy & Sayed, Enas Taha & Al-Dhaifallah, Mujahed & Obaid, M. & El-Sayed, Abou Hashema M. & Abdelkareem, Mohammad Ali & Olabi, A.G., 2019. "Fuel cell as an effective energy storage in reverse osmosis desalination plant powered by photovoltaic system," Energy, Elsevier, vol. 175(C), pages 423-433.
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