Applications of machine learning in thermochemical conversion of biomass-A review

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251263" target="_blank" >RIV/61989100:27240/22:10251263 - isvavai.cz</a>

Výsledek na webu

<a href="https://reader.elsevier.com/reader/sd/pii/S0016236122028794?token=3135C92C9F6941DDA7692476520A950C63C22723B6D3F7B0A398B11293DAC28DC7F89A97F4B898C85710BF87D4210C42&originRegion=eu-west-1&originCreation=20230206090009" target="_blank" >https://reader.elsevier.com/reader/sd/pii/S0016236122028794?token=3135C92C9F6941DDA7692476520A950C63C22723B6D3F7B0A398B11293DAC28DC7F89A97F4B898C85710BF87D4210C42&originRegion=eu-west-1&originCreation=20230206090009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.fuel.2022.126055" target="_blank" >10.1016/j.fuel.2022.126055</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Applications of machine learning in thermochemical conversion of biomass-A review

Popis výsledku v původním jazyce

Thermochemical conversion of biomass has been considered a promising technique to produce alternative renewable fuel sources for future energy supply. However, these processes are often complex, labor-intensive, and time-consuming. Significant efforts have been made in developing strategies for modeling thermochem-ical conversion processes to maximize their performance and productivity. Among these strategies, machine learning (ML) has attracted substantial interest in recent years in thermochemical conversion process optimi-zation, yield prediction, real-time monitoring, and process control. This study presents a comprehensive review of the research and development in state-of-the-art ML applications in pyrolysis, torrefaction, hydrothermal treatment, gasification, and combustion. Artificial neural networks have been widely employed due to their ability to learn extremely non-linear input-output correlations. Furthermore, the hybrid ML models out-performed the traditional ML models in modeling and optimization tasks. The comparison between various ML methods for different applications, and insights about where the current research is heading, is highlighted. Finally, based on the critical analysis, existing research knowledge gaps are identified, and future recommen-dations are presented.

Název v anglickém jazyce

Applications of machine learning in thermochemical conversion of biomass-A review

Popis výsledku anglicky

Thermochemical conversion of biomass has been considered a promising technique to produce alternative renewable fuel sources for future energy supply. However, these processes are often complex, labor-intensive, and time-consuming. Significant efforts have been made in developing strategies for modeling thermochem-ical conversion processes to maximize their performance and productivity. Among these strategies, machine learning (ML) has attracted substantial interest in recent years in thermochemical conversion process optimi-zation, yield prediction, real-time monitoring, and process control. This study presents a comprehensive review of the research and development in state-of-the-art ML applications in pyrolysis, torrefaction, hydrothermal treatment, gasification, and combustion. Artificial neural networks have been widely employed due to their ability to learn extremely non-linear input-output correlations. Furthermore, the hybrid ML models out-performed the traditional ML models in modeling and optimization tasks. The comparison between various ML methods for different applications, and insights about where the current research is heading, is highlighted. Finally, based on the critical analysis, existing research knowledge gaps are identified, and future recommen-dations are presented.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/LTI19002" target="_blank" >LTI19002: Zapojení českých výzkumných organizací do Evropské aliance pro energetický výzkum EERA (EERA-CZ 2)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Fuel

ISSN

0016-2361

e-ISSN

1873-7153

Svazek periodika

332

Číslo periodika v rámci svazku

2022

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

nestrankovano

Kód UT WoS článku

000870315200002

EID výsledku v databázi Scopus

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