Assessment of pressure drop in conical spouted beds of biomass by artificial neural networks and comparison with empirical correlations

Pressure drop is an essential parameter in the operation of conical spouted beds (CSB) and depends on its geometric factors and materials used. Irregular materials, like biomass, are complex to treat and, unlike other gas-solid contact methods, CSB turn out to be a suitable technology for their treatment. Artificial neural networks were used in this study for the prediction of operating and peak pressure drops, and their performance has been compared with that of empirical correlations reported in the literature. Accordingly, a multi-layer perceptron network with backward propagation was used due to its ability to model non-linear multivariate systems. The fitting of the experimental data of both operating and peak pressure drop was significantly better than those reported in the literature, specifically in the case of the peak pressure drop, with R-2 being 0.92. Therefore, artificial neural networks have been proven suitable for the prediction of pressure drop in CSB. (C) 2021 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Automated summary

Artificial neural networks were used to predict pressure drop in conical spouted beds (CSB), and the results were compared to empirical correlations. The neural network model outperformed the existing correlations, indicating its potential for predicting pressure drop in CSB.