Torrefied biomass quality prediction and optimization using machine learning algorithms

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Torrefied biomass quality prediction and optimization using machine learning algorithms

Popis výsledku v původním jazyce

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)

Název v anglickém jazyce

Torrefied biomass quality prediction and optimization using machine learning algorithms

Popis výsledku anglicky

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)

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Chemical Engineering Journal Advances

ISSN

2666-8211

e-ISSN

—

Svazek periodika

19

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

001282685600001

EID výsledku v databázi Scopus

2-s2.0-85197053097