Torrefied biomass quality prediction and optimization using machine learning algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10257123" target="_blank" >RIV/61989100:27240/24:10257123 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2666821124000383" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2666821124000383</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ceja.2024.100620" target="_blank" >10.1016/j.ceja.2024.100620</a>
Alternative languages
Result language
angličtina
Original language name
Torrefied biomass quality prediction and optimization using machine learning algorithms
Original language description
Torrefied biomass is a vital green energy source with applications in circular economies, addressing agricultural residue and rising energy demands. In this study, ML models were used to predict durability (%) and mass loss (%). Firstly, data was collected and preprocessed, and its distribution and correlation were analyzed. Gaussian Process Regression (GPR) and Ensemble Learning Trees (ELT) were then trained and tested on 80 % and 20 % of the data, respectively. Both machine learning models underwent optimization through Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for feature selection and hyperparameter tuning. GPR-PSO demonstrates excellent accuracy in predicting durability (%), achieving a training R2 score of 0.9469 and an RMSE value of 0.0785. GPR-GA exhibits exceptional performance in predicting mass loss (%), achieving a training R2 value of 1 and an RMSE value of 9.7373e-05. The temperature and duration during torrefaction are crucial variables that are in line with the conclusions drawn from previous studies. GPR and ELT models effectively predict and optimize torrefied biomass quality, leading to enhanced energy density, mechanical properties, grindability, and storage stability. Additionally, they contribute to sustainable agriculture by reducing carbon emissions, improving cost-effectiveness, and aiding in the design and development of pelletizers. This optimization not only increases energy density and grindability but also enhances nutrient delivery efficiency, water retention, and reduces the carbon footprint. Consequently, these outcomes support biodiversity and promote sustainable agricultural, ecosystem, and environmental practices. © 2024 The Author(s)
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20704 - Energy and fuels
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Chemical Engineering Journal Advances
ISSN
2666-8211
e-ISSN
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Volume of the periodical
19
Issue of the periodical within the volume
Neuveden
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
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UT code for WoS article
001282685600001
EID of the result in the Scopus database
2-s2.0-85197053097