Predictive modeling of the length of prepared CNT by CVD through the ANN- MPSO and GEP

Document Type: Research Paper

Authors

1 Department of Materials Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran

2 Department of Materials Science and Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

10.22104/jpst.2020.3835.1156

Abstract

Floating catalyst chemical vapor deposition (FC-CVD) is considered as one of the most appropriate techniques for the preparation of carbon nano-tubes (CNTs) on the industrial scale. This paper tried to model the length of prepared CNTs. That prepared by FC-CVD using two approaches, i.e. gene expression programs, and hybrid artificial neural networks. In this regard, the effect of various FC-CVD parameters, viz. temperature, time, preheat temperature, Ar gas flow, methane gas flow, ethylene gas flow, Al2O3 catalyst, and Fe catalyst, on the length of CNTs, were investigated. At first, a hybrid artificial neural network- modified particle swarm optimization strategy (ANN-MPSO) has been used to model the CNTs length as a function of practical variables. At the next step, the same modeling of the problem was done using gene expression programming (GEP) instead of MPSO-ANN. The accuracy of developed hybrid ANN -MPSO and GEP models were compared with each other by consideration of the linear combination of mean absolute percentage error and correlation coefficient as criteria. The results confirmed that the upgraded ANN model by meta-heuristics strategy could be effectively applied for the accurate predictive model in the estimation of the length of CNTs as a function of the most important FC-CVD practical parameters. Also, the sensitivity analysis confirmed that the precursor type of carbon including CH4 and C2H4 were the administrated parameters on the length of CNTs. While the preheat temperature has the minimum effect on the length of prepared CNTs.

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