Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27650%2F23%3A10254087" target="_blank" >RIV/61989100:27650/23:10254087 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s42004-023-01077-z" target="_blank" >https://www.nature.com/articles/s42004-023-01077-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s42004-023-01077-z" target="_blank" >10.1038/s42004-023-01077-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes

Popis výsledku v původním jazyce

Feedstock properties play a crucial role in thermal conversion processes, where understanding the influence of these properties on treatment performance is essential for optimizing both feedstock selection and the overall process. In this study, a series of van Krevelen diagrams were generated to illustrate the impact of H/C and O/C ratios of feedstock on the products obtained from six commonly used thermal conversion techniques: torrefaction, hydrothermal carbonization, hydrothermal liquefaction, hydrothermal gasification, pyrolysis, and gasification. Machine learning methods were employed, utilizing data, methods, and results from corresponding studies in this field. Furthermore, the reliability of the constructed van Krevelen diagrams was analyzed to assess their dependability. The van Krevelen diagrams developed in this work systematically provide visual representations of the relationships between feedstock and products in thermal conversion processes, thereby aiding in optimizing the selection of feedstock and the choice of thermal conversion technique

Název v anglickém jazyce

Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes

Popis výsledku anglicky

Feedstock properties play a crucial role in thermal conversion processes, where understanding the influence of these properties on treatment performance is essential for optimizing both feedstock selection and the overall process. In this study, a series of van Krevelen diagrams were generated to illustrate the impact of H/C and O/C ratios of feedstock on the products obtained from six commonly used thermal conversion techniques: torrefaction, hydrothermal carbonization, hydrothermal liquefaction, hydrothermal gasification, pyrolysis, and gasification. Machine learning methods were employed, utilizing data, methods, and results from corresponding studies in this field. Furthermore, the reliability of the constructed van Krevelen diagrams was analyzed to assess their dependability. The van Krevelen diagrams developed in this work systematically provide visual representations of the relationships between feedstock and products in thermal conversion processes, thereby aiding in optimizing the selection of feedstock and the choice of thermal conversion technique

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Communications Chemistry

ISSN

2399-3669

e-ISSN

2399-3669

Svazek periodika

6

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

10

Strana od-do

—

Kód UT WoS článku

001122502600001

EID výsledku v databázi Scopus

2-s2.0-85179331588