The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

Soroush Soltani; Taha Roodbar Shojaei; Nasrin Khanian; Thomas Shean Yaw Choong; Umer Rashid; Imededdine Arbi Nehdi; Rozita Binti Yusoff

doi:10.1039/D0RA00892C

The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

Soroush Soltani,

*^a Taha Roodbar Shojaei,^b Nasrin Khanian,^c Thomas Shean Yaw Choong,^a Umer Rashid,^d Imededdine Arbi Nehdi^ef and Rozita Binti Yusoff^g

Author affiliations

* Corresponding authors

^a Department of Chemical and Environmental Engineering, Universiti Putra Malaysia, 43400 Selangor, Malaysia
E-mail: soroush.soltaani@gmail.com

^b Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

^c Department of Physics, Faculty of Science, Islamic Azad University, Karaj, Iran

^d Institute of Advanced Technology, Universiti Putra Malaysia, 43400 Selangor, Malaysia

^e Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia

^f Laboratoire de Recherche LR18ES08, Chemistry Department, Science College, Tunis El Manar University, Tunis 2092, Tunisia

^g Department of Chemical Engineering, Faculty of Engineering, University of Malaya, W. Persekutuan Kuala Lumpur, 50603 Kuala Lumpur, Malaysia

Abstract

In the present research, artificial neural network (ANN) modelling was utilized to determine the relative importance of effective variables to achieve optimum specific surface areas of a synthesized catalyst. Initially, carbonaceous nanocrystalline mesoporous NiO core–shell solid sphere composites were produced by applying incomplete carbonized glucose (ICG) as the pore directing agent and polyethylene glycol (PEG; 4000) as the surfactant via a hydrothermal-assisted method. The Brunauer–Emmett–Teller (BET) model was applied to ascertain the textural characteristics of the as-prepared mesoporous NiO catalyst. The effects of several key parameters such as metal ratio, surfactant and template concentrations, and calcination temperature on the prediction of the surface areas of the as-synthesized catalyst were evaluated. In order to verify the optimum hydrothermal fabrication conditions, ANN was trained over five different algorithms (QP, BBP, IBP, LM, and GA). Among five different algorithms, LM-4-7-1 representing 4 nodes in the input layer, 7 nodes in the hidden layer, and 1 node in the output layer was verified as the optimum model due to its optimum numerical properties. According to the modelling study, the calcination temperature demonstrated the most effective parameter, while the ICG concentration indicated the least effect. By verifying the optimum hydrothermal fabrication conditions, the thermal decomposition of ammonium sulphate (TDAS) was applied to the functionalized surface areas and mesoporous walls by –SO₃H functional groups. In addition, the catalytic performance and reusability of the produced mesoporous SO₃H–NiO catalyst were evaluated via the transesterification of waste cooking palm oil, resulting in a methyl ester content of 97.4% and excellent stability for nine consecutive transesterification reactions without additional treatments.

RSC Advances

The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

Abstract

Article information

Download Citation

Permissions

The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

Social activity

Search articles by author

Spotlight

Advertisements