Mr. Abdelrahman K.A. Khalil Profile

Abdelrahman K.A. Khalil

at Univ of Sharjah

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Proceedings Article | 15 June 2023 Presentation + Paper

Artificial intelligence-based modeling of capacitive deionization for process optimization and cost contribution analysis of electrode features

Abdelrahman K. Khalil, Mohammad Al-Shabi, Khalil Abdelrazek Khalil

Proceedings Volume 12513, 1251309 (2023) https://doi.org/10.1117/12.2664473

KEYWORDS: Electrodes, Deionized water, Ions, Artificial neural networks, Education and training, Neurons, Modeling, Carbon, Mathematical optimization, Histograms

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Capacitive deionization is a promising electrochemical technology employed in water treatment applications. Among the various water desalination and treatment technologies, capacitive deionization technology has many advantages and appreciably increases desalination efficiency. CDI desalinates the Water via the electrosorption of ions inside the porous structure of two oppositely charged electrodes. The electrodes are considered the core of the CDI system. The carbon flow electrode is a new design for improving salt removal efficiency (SRE). Thus, developing a numerical model to predict CDI salt removal efficiency (SRE) and understanding how electrodes jointly contribute to desalination is crucial for rational FCDI system design. This paper demonstrates the concept of using Artificial intelligence-based modeling to predict the electrosorption capacity of FCDI with reasonable accuracy based on the important flow electrode and process features. The contribution and relative importance of each feature in deionization and the cost analysis framework of FCDI are determined and validated. This study shows that artificial neural networks (ANN) have strong abilities in predicting the nonlinear behavior of the CDI system and in revealing each feature’s role of the electrode in desalination. Two hidden layers with 14 and 11 neurons in the first and second hidden layers have been used. The model has good regression of 100% for training, 99.67% for validation 99.809% for testing, and 99.908% for the overall system. The 𝑅𝑀𝑆𝐸, 𝑀𝐴𝐸, and 𝑅𝑀𝑆𝐸_{%𝐸𝑟𝑟𝑜𝑟} were significantly small.

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