Title: Improving CNTs properties using computational intelligence algorithms

Authors: Muath Jarrah; Zakaria N.M. Alqattan; Abdul Syukor Mohamad Jaya; Sharif Naser Makhadmeh; Ahmed Ismail Abu-Khadrah; Ibrahim Aljarrah; Osama Ahmad Alomari

Addresses: College of Computing and Informatics, University of Sharjah, Sharjah, 27272, UAE; Department of Computer Science, University Malaysia of Computer Science and Engineering (UNIMY), Cyberjaya, Selangor, Malaysia ' Department of Cyber Security and Cloud Computing Techniques Engineering, Northern Technical University, Mosul, Iraq; Department of Computer Science, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia (IIUM), Kuala Lumpur, Malaysia ' Department of Intelligent Computing and Analytics, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Melaka, Malaysia ' Department of Data Science and Artificial Intelligence, University of Petra, Amman, 11196, Jordan ' College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia ' Department of Computer Information Systems (CIS), Jordan University of Science and Technology, Irbid, Jordan ' Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates

Abstract: Carbon nanotubes (CNTs) have emerged in various applications due to their outstanding characteristics. The most common technique for producing CNTs with high yield and quality is known as chemical vapour deposition (CVD). However, manufacturers rely on conventional experimental studies to produce CNTs, which raise issues such as time, cost, and dealing with toxic materials. Alternatively, modelling and optimisation using metaheuristic algorithms are suggested to address these issues. This paper uses response surface methodology (RSM) for modelling work, while four metaheuristic algorithms are employed for optimisation. The regression and mathematical models, correlations, and significant CNTs process parameters are identified, analysed, and validated using RSM. The optimisation process and result are validated using different performance measure metrics and supported by other researchers. The CNTs yield and quality values improvement percentages in this paper are up to 36.45% compared to the referred original work.

Keywords: carbon nanotubes; CNTs; chemical vapour deposition; CVD; optimisation algorithms; response surface methodology; RSM.

DOI: 10.1504/IJMPT.2024.136839

International Journal of Materials and Product Technology, 2024 Vol.68 No.1/2, pp.169 - 198

Received: 31 Oct 2022
Accepted: 04 Aug 2023
Published online: 22 Feb 2024 *

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