Al-Biruni Earth Radius Optimization Based Algorithm for Improving Prediction of Hybrid Solar Desalination System

Abdelhameed Ibrahim, El-Sayed M. El-kenawy (), A. E. Kabeel, Faten Khalid Karim (), Marwa M. Eid, Abdelaziz A. Abdelhamid, Sayed A. Ward, Emad M. S. El-Said, M. El-Said and Doaa Sami Khafaga
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Abdelhameed Ibrahim: Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
El-Sayed M. El-kenawy: Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
A. E. Kabeel: Faculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt
Faten Khalid Karim: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Marwa M. Eid: Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
Abdelaziz A. Abdelhamid: Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
Sayed A. Ward: Faculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt
Emad M. S. El-Said: Mechanical Engineering Department, Faculty of Engineering, Dameitta University, Damietta 34511, Egypt
M. El-Said: Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Doaa Sami Khafaga: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Energies, 2023, vol. 16, issue 3, 1-20

Abstract: The performance of a hybrid solar desalination system is predicted in this work using an enhanced prediction method based on a supervised machine-learning algorithm. A humidification–dehumidification (HDH) unit and a single-stage flashing evaporation (SSF) unit make up the hybrid solar desalination system. The Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) algorithms serve as the foundation for the suggested algorithm. Using experimental data, the BER–PSO algorithm is trained and evaluated. The cold fluid and injected air volume flow rates were the algorithms’ inputs, and their outputs were the hot and cold fluids’ outlet temperatures as well as the pressure drop across the heat exchanger. Both the volume mass flow rate of hot fluid and the input temperatures of hot and cold fluids are regarded as constants. The results obtained show the great ability of the proposed BER–PSO method to identify the nonlinear link between operating circumstances and process responses. In addition, compared to the other analyzed models, it offers better statistical performance measures for the prediction of the outlet temperature of hot and cold fluids and pressure drop values.

Keywords: humidification–dehumidification; flashing desalination; machine learning; meta-heuristic optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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