Progresses and Challenges of Machine Learning Approaches in Thermochemical Processes for Bioenergy: A Review

Thermochemical conversions of nonedible biomass into energy are promising alternatives for ensuring a sustainable energy society. However, determining the optimum design and operating conditions of the processes remains a major challenge due to the laborious and costly experimental methods. Machine...

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Published inThe Korean journal of chemical engineering Vol. 41; no. 7; pp. 1923 - 1953
Main Authors Ogunsola, Nafiu Olanrewaju, Oh, Seung Seok, Jeon, Pil Rip, Ling, Jester Lih Jie, Park, Hyun Jun, Park, Han Saem, Lee, Ha Eun, Sohn, Jung Min, Lee, See Hoon
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2024
Springer Nature B.V
한국화학공학회
Subjects
Alternative energy sources
Biofuels
Biomass
Biomass energy production
Biotechnology
Catalysis
Chemistry
Chemistry and Materials Science
Industrial Chemistry/Chemical Engineering
Machine learning
Materials Science
Renewable energy
Review Article
Robustness (mathematics)
화학공학
Sustainable biomass utilization
Thermochemical conversion
Bioenergy
Machine learning
Artificial neural networks
Online AccessGet full text
ISSN0256-1115
1975-7220
DOI10.1007/s11814-024-00181-7

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Abstract Thermochemical conversions of nonedible biomass into energy are promising alternatives for ensuring a sustainable energy society. However, determining the optimum design and operating conditions of the processes remains a major challenge due to the laborious and costly experimental methods. Machine learning techniques are cost-effective and non-time consuming and have been widely utilized in thermochemical conversion process modelling with robust and accurate results and solutions. Nonetheless, no standard method has been proposed for applying ML models to biomass thermochemical processes. Consequently, the general development procedure for ML models with high accuracy and robustness remains unclear. This review provides a comprehensive review of machine learning techniques for predicting biofuel yield and composition. It is recommended that quality datasets be ensured to enable the development of more robust machine learning-aided models for practical engineering applications. Finally, solutions to the identified challenges and prospective future research directions on machine learning-based biomass thermochemical conversion processes are recommended to accelerate the optimization and large-scale deployment of these processes.
AbstractList Thermochemical conversions of nonedible biomass into energy are promising alternatives for ensuring a sustainable energy society. However, determining the optimum design and operating conditions of the processes remains a major challenge due to the laborious and costly experimental methods. Machine learning techniques are cost-eff ective and non-time consuming and have been widely utilized in thermochemical conversion process modelling with robust and accurate results and solutions. Nonetheless, no standard method has been proposed for applying ML models to biomass thermochemical processes. Consequently, the general development procedure for ML models with high accuracy and robustness remains unclear. This review provides a comprehensive review of machine learning techniques for predicting biofuel yield and composition. It is recommended that quality datasets be ensured to enable the development of more robust machine learning-aided models for practical engineering applications. Finally, solutions to the identifi ed challenges and prospective future research directions on machine learning-based biomass thermochemical conversion processes are recommended to accelerate the optimization and large-scale deployment of these processes. KCI Citation Count: 0
Thermochemical conversions of nonedible biomass into energy are promising alternatives for ensuring a sustainable energy society. However, determining the optimum design and operating conditions of the processes remains a major challenge due to the laborious and costly experimental methods. Machine learning techniques are cost-effective and non-time consuming and have been widely utilized in thermochemical conversion process modelling with robust and accurate results and solutions. Nonetheless, no standard method has been proposed for applying ML models to biomass thermochemical processes. Consequently, the general development procedure for ML models with high accuracy and robustness remains unclear. This review provides a comprehensive review of machine learning techniques for predicting biofuel yield and composition. It is recommended that quality datasets be ensured to enable the development of more robust machine learning-aided models for practical engineering applications. Finally, solutions to the identified challenges and prospective future research directions on machine learning-based biomass thermochemical conversion processes are recommended to accelerate the optimization and large-scale deployment of these processes.
Author Oh, Seung Seok
Park, Hyun Jun
Jeon, Pil Rip
Ogunsola, Nafiu Olanrewaju
Sohn, Jung Min
Lee, Ha Eun
Lee, See Hoon
Park, Han Saem
Ling, Jester Lih Jie
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  givenname: Seung Seok
  surname: Oh
  fullname: Oh, Seung Seok
  organization: Department of Environment and Energy, Jeonbuk National University
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  givenname: Pil Rip
  surname: Jeon
  fullname: Jeon, Pil Rip
  organization: Department of Chemical Engineering, Kongju National University
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  givenname: Jester Lih Jie
  surname: Ling
  fullname: Ling, Jester Lih Jie
  organization: Department of Environment and Energy, Jeonbuk National University
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  givenname: Hyun Jun
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  fullname: Park, Hyun Jun
  organization: Department of Environment and Energy, Jeonbuk National University
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  fullname: Park, Han Saem
  organization: Department of Environment and Energy, Jeonbuk National University
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  fullname: Lee, Ha Eun
  organization: Department of Environment and Energy, Jeonbuk National University
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  givenname: Jung Min
  surname: Sohn
  fullname: Sohn, Jung Min
  organization: Department of Mineral Resources and Energy Engineering, Jeonbuk National University
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  givenname: See Hoon
  orcidid: 0000-0002-5147-8720
  surname: Lee
  fullname: Lee, See Hoon
  email: donald@jbnu.ac.kr
  organization: Department of Mineral Resources and Energy Engineering, Jeonbuk National University, Department of Environment and Energy, Jeonbuk National University
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  ident: 181_CR121
  publication-title: Energies
  doi: 10.3390/en13174572
– ident: 181_CR146
  doi: 10.1016/j.fuel.2021.122082
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Snippet Thermochemical conversions of nonedible biomass into energy are promising alternatives for ensuring a sustainable energy society. However, determining the...
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StartPage 1923
SubjectTerms Alternative energy sources
Biofuels
Biomass
Biomass energy production
Biotechnology
Catalysis
Chemistry
Chemistry and Materials Science
Industrial Chemistry/Chemical Engineering
Machine learning
Materials Science
Renewable energy
Review Article
Robustness (mathematics)
화학공학
Title Progresses and Challenges of Machine Learning Approaches in Thermochemical Processes for Bioenergy: A Review
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https://www.proquest.com/docview/3072276371
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Volume 41
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ispartofPNX Korean Journal of Chemical Engineering, 2024, 41(7), 292, pp.1923-1953
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