The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application
Document Type
Article
Publication Date
1-1-2020
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. © 2020 The Royal Society of Chemistry.
Keywords
Biodiesel, Calcination, Catalysts, Mesoporous materials, Neural networks, Nickel oxide, Palm oil, Reusability, Sulfur compounds, Surface active agents, Transesterification
Divisions
fac_eng
Funders
Universiti Putra Malaysia (UPM) for the financial support for funding this research work through Geran Putra UPM GP-IPB/ 2016/9515200,King Saud University (Riyadh, Saudi Arabia) for the support of this research through Researchers Supporting Project number (RSP-2019/80)
Publication Title
RSC Advances
Volume
10
Issue
22
Publisher
Royal Society of Chemistry