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Licensed Unlicensed Requires Authentication Published online by De Gruyter August 12, 2022

A review on the application of machine learning for combustion in power generation applications

  • Kasra Mohammadi , Jake Immonen , Landen D. Blackburn , Jacob F. Tuttle , Klas Andersson and Kody M. Powell EMAIL logo


Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.

Corresponding author: Kody M. Powell, Department of Chemical Engineering, University of Utah, 50 S. Central Campus Dr., Room 3290 MEB, Salt Lake City, UT 84112-9203, USA; and Department of Mechanical Engineering, University of Utah, 1495 E 100 S., Room 1550 MEK, Salt Lake City, UT 84112, USA, E-mail:

Funding source: United States Department of Energy (DOE)

Award Identifier / Grant number: DE-FE0031754

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work is funded by the United States Department of Energy (DOE) under the DE-FE0031754 grant, which is affiliated with the DOE’s Office of Fossil Energy.

  3. Conflict of interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


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Received: 2022-01-03
Accepted: 2022-06-03
Published Online: 2022-08-12

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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