Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO. (March 2019)

• Record Type: Journal Article
• Authors: Shahbaz, Muhammad; Taqvi, Syed A.; Minh Loy, Adrian Chun; Inayat, Abrar; Uddin, Fahim; Bokhari, Awais; Naqvi, Salman Raza
• Is Part Of: Renewable energy. Volume 132(2019)
• Page Start: 243
• Page End: 254
• Publication Date: 2019-03
• Subjects: Artificial neural network -- Biomass gasification -- Syngas -- Layer -- Palm kernel shell -- Temperature -- Coal bottom ash; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.07.142
• Languages: English
• ISSNs: 0960-1481

Abstract

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 R 2 = 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. Graphical abstract: Image Highlights: The examination of steam gasification of PKS using fluidized and fixed bed. ANN modelling for steam gasification of PKS using CaO and coal bottom ash. Modelling results has good agreement with experimental results. Temperature and steam/biomass ratio is found influence for gas composition. Coal bottom ash shows good catalytic effect in gasification system.