Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO

Muhammad Shahbaz, Syed A. Taqvi, Adrian Chun Minh Loy, Abrar Inayat, Fahim Uddin, Awais Bokhari and Salman Raza Naqvi

Renewable Energy, 2019, vol. 132, issue C, 243-254

Abstract: The Artificial Neural Network (ANN) modelling is presented for the steam gasification of palm kernel shell using CaO adsorbent and coal bottom ash as a catalyst. The effect of the parameters such as; temperature, CaO/biomass ratio and Coal bottom ash wt.% at fixed steam/biomass ratio and steam/biomass ratio at the fixed temperature on product gas composition of H2, CO, CO2, and CH4 are modelled using ANN. The effect of parameters is used as an input, while the gas compositions, syngas yield, LHVgas and HHVgas of gas as the output of the network. Back propagation algorithm has been used for the training with 7 neurons in the hidden layer. Hence, the selected ANN architecture was (2-7-1). The gas composition predicted by the ANN model are compared with experimental results obtained from pilot scale gasification system that has been reported in our previous study. The ANN predicted results show high agreement with the published experimental values with the coefficient of determination R2 = 0.998 for almost all the cases, i.e., the effect of parameters. RMSE, MAD, and AARE have been reported to be very insignificant for the predicted and experimental values.

Keywords: Artificial neural network; Biomass gasification; Syngas; Layer; Palm kernel shell; Temperature; Coal bottom ash (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014811830939X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:132:y:2019:i:c:p:243-254

DOI: 10.1016/j.renene.2018.07.142

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().