Detail publikace

Multi-objective optimization of thermophysical properties GO powders-DW/EG Nf by RSM, NSGA-II, ANN, MLP and ML

Kiannejad Amiri, M. Ghorbanzade Zaferani, S.P. Samasti Emami, M.R. Zahmatkesh, S. Pourhanasa, R. Sadeghi Namaghi, S. Klemeš, J.J. Bokhari, A. Hajiaghaei-Keshteli, M.

Originální název

Multi-objective optimization of thermophysical properties GO powders-DW/EG Nf by RSM, NSGA-II, ANN, MLP and ML

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

In this study, prediction, modeling, and optimization have been performed for four TPH properties of graphene oxide nano powder-deionized water/ethylene glycol nf, which is unique compared to other studies. Response surface methodology, artificial neural networks based on multiple layers of perceptron, and algorithms based on machine learning have been developed for prediction and modeling. RSM modeling resulted in coefficients of determination of 0.9984, 0.9986, 0.9995, and 0.9996 for TC (k), density (ρ), SHC (cp), and viscosity (μ), respectively. The highest prediction errors for RSM models were 0.3644%, 0.0374%, 2.049%, and 0.2296% for k, ρ, μ, and cp. A higher temperature and a higher WF of NPs increased the TC of the nf. The maximum MLP model error was 0.43%, 6.59%, 12.64%, and 1.04% for ρ, cp, μ, and k, respectively. TC and SHC were optimized using the NSGA-II algorithm. The NSGA-II procedure indicated the maximum k and cp occurred at the highest temperatures. The temperature must be kept at its maximum to reach the optimal stage. Also, it was proven that temperature is a much more significant parameter than the nanoparticle WF.

Klíčová slova

ANN; GONs-DW/EG nf; MLP; Multi-objective optimization; NSGA-II; RSM

Autoři

Kiannejad Amiri, M.; Ghorbanzade Zaferani, S.P.; Samasti Emami, M.R.; Zahmatkesh, S.; Pourhanasa, R.; Sadeghi Namaghi, S.; Klemeš, J.J.; Bokhari, A.; Hajiaghaei-Keshteli, M.

Vydáno

1. 10. 2023

Nakladatel

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

Místo

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

ISSN

0360-5442

Periodikum

Energy

Číslo

280

Stát

Spojené království Velké Británie a Severního Irska

Strany počet

10

URL

https://www.sciencedirect.com/science/article/pii/S0360544223015700?via%3Dihub

BibTex

@article{BUT187986,
  author="Kiannejad Amiri, M. and Ghorbanzade Zaferani, S.P. and Samasti Emami, M.R. and Zahmatkesh, S. and Pourhanasa, R. and Sadeghi Namaghi, S. and Klemeš, J.J. and Bokhari, A. and Hajiaghaei-Keshteli, M.",
  title="Multi-objective optimization of thermophysical properties GO powders-DW/EG Nf by RSM, NSGA-II, ANN, MLP and ML",
  journal="Energy",
  year="2023",
  number="280",
  pages="10",
  doi="10.1016/j.energy.2023.128176",
  issn="0360-5442",
  url="https://www.sciencedirect.com/science/article/pii/S0360544223015700?via%3Dihub"
}