Artificial Intelligence Based Modelling of Adsorption Water Desalination System

Mathematics

Abdulrahim A. Al-Zahrani¹  Sharif F. Zaman¹  Hesham Alhumade¹  Hegazy Rezk²  Ahmed Askalany³ 

[1] Chemical and Materials Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 11911, Saudi Arabia;Mechanical Engineering Department, Faculty of Industrial Education, Sohag University, Sohag 82524, Egypt;

关键词: artificial intelligence;    modelling based ANFIS;    adsorption desalination;   

DOI  :  10.3390/math9141674

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown