Optimization of capacitive deionization electrode features and materials using artificial-intelligence-based modeling

Abdelrahman K. A. Khalil; Mohammad AlShabi; Khalil Abdelrazek Khalil; Khaled Obaideen

doi:10.1117/12.3013901

7 June 2024 Optimization of capacitive deionization electrode features and materials using artificial-intelligence-based modeling

Abdelrahman K. A. Khalil, Mohammad AlShabi, Khalil Abdelrazek Khalil, Khaled Obaideen

Author Affiliations +

Proceedings Volume 13027, Energy Harvesting and Storage: Materials, Devices, and Applications XIV; 130270A (2024) https://doi.org/10.1117/12.3013901
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

Capacitive deionization (CDI) is an emerging technique for removing dissolved, charged species from aqueous solutions. It has been previously applied to brackish water and seawater desalination, wastewater remediation, and water softening. The CDI unit cell comprises two parallel electrode sheets separated by a non-conductive spacer (nylon cloth, 100 mm thick) and fixed with a rubber gasket. The electrodes are typically carbon, and the feed water flows between or through the two charged electrodes. The porous electrode pair is accused of an applied voltage difference (called the cell or charging voltage). Optimizing the CDI electrode features is essential for scaling up the technique to an industrial scale. The effect of the water flow rate and the applied voltage are key factors that affect the efficiency of the CDI units. This research used Artificial Intelligence (AI) as a smart-based modeling tool to optimize and predict the highest efficiency concerning the electrode and process parameters. The results showed that a carbon-based structure with super-electrochemical and mechanical properties could revolutionize CDI technology.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Abdelrahman K. A. Khalil, Mohammad AlShabi, Khalil Abdelrazek Khalil, and Khaled Obaideen "Optimization of capacitive deionization electrode features and materials using artificial-intelligence-based modeling", Proc. SPIE 13027, Energy Harvesting and Storage: Materials, Devices, and Applications XIV, 130270A (7 June 2024); https://doi.org/10.1117/12.3013901

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available