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Prediction of the minimum fluidization velocity of different biomass types by artificial neural networks and empirical correlations

Prediction of the minimum fluidization velocity of different biomass types by artificial neural... This paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications.Design/methodology/approachAn extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis.FindingsThe performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature.Originality/valueTechniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Numerical Methods for Heat and Fluid Flow Emerald Publishing

Prediction of the minimum fluidization velocity of different biomass types by artificial neural networks and empirical correlations

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References (54)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0961-5539
eISSN
0961-5539
DOI
10.1108/hff-10-2023-0655
Publisher site
See Article on Publisher Site

Abstract

This paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications.Design/methodology/approachAn extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis.FindingsThe performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature.Originality/valueTechniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed.

Journal

International Journal of Numerical Methods for Heat and Fluid Flow – Emerald Publishing

Published: Sep 2, 2024

Keywords: Deep learning; Renewable energy; Mathematical modeling; Machine learning

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