THERMAL SCIENCE

International Scientific Journal

Ana Matin

,

Karlo Špelić

,

Vanja Jurišić

,

Božidar Matin

,

Mateja Grubor

,

Ivana Tomić

,

Tugomir Majdak

,

Ivan Brandić

THE POSSIBILITY OF MODELING AGRICULTURAL BIOMASS ASH BY NEURAL NETWORKS CONCERNING PROXIMATE ANALYSIS INPUTS

ABSTRACT
Agricultural biomass is an important RES with significant environmental and eco¬nomic benefits. However, high ash content in biomass can lead to problems such as slagging, fouling, and corrosion and can reduce the efficiency of energy systems. This study analyzes the proximate composition of different biomass samples, focusing on ash content, and uses machine learning to model ash content based on other components. Six biomass types, including rapeseed, barley, wheat, corn, soybean and sunflower, were examined to analyze the content of coke, fixed carbon, volatile matter, and ash. The results showed considerable variability, with ash content ranging from 8.25% for rapeseed to 12.3% for soybean. Artificial neural networks were used to model ash content with a high accuracy of R² = 0.92. The model effectively estimated the ash content based on the input parameters and demonstrated the potential of machine learning to optimize biomass selection for energy production.
KEYWORDS
biomass, artificial neural networks, proximate analysis, ash content, modelling
PAPER SUBMITTED: 2024-06-18
PAPER REVISED: 2024-09-03
PAPER ACCEPTED: 2024-09-13
PUBLISHED ONLINE: 2024-11-09
DOI REFERENCE: https://doi.org/10.2298/TSCI240618238M
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 6, PAGES [4771 - 4780]
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The possibility of modeling agricultural biomass ash by neural networks concerning proximate analysis inputs

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence