Applications of machine learning in thermochemical conversion of biomass-A review
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Applications of machine learning in thermochemical conversion of biomass-A review
Original language description
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.
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
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
<a href="/en/project/LTI19002" target="_blank" >LTI19002: The involvement of Czech research organizations in the Energy Research Alliance EERA</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Fuel
ISSN
0016-2361
e-ISSN
1873-7153
Volume of the periodical
332
Issue of the periodical within the volume
2022
Country of publishing house
US - UNITED STATES
Number of pages
21
Pages from-to
nestrankovano
UT code for WoS article
000870315200002
EID of the result in the Scopus database
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