A review on the application of machine learning for combustion in power generation applications
Review article, 2023

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.

machine learning algorithms

coal fired plants

carbon capture technology

combustion process

optimization

Author

Kasra Mohammadi

University of Utah

Jake Immonen

University of Utah

Landen D. Blackburn

University of Utah

Jacob F. Tuttle

University of Utah

Klas Andersson

Chalmers, Space, Earth and Environment, Energy Technology

Kody M. Powell

University of Utah

Reviews in Chemical Engineering

0167-8299 (ISSN) 2191-0235 (eISSN)

Vol. 39 6 1027-1059

Subject Categories

Other Mechanical Engineering

Other Environmental Engineering

Energy Systems

DOI

10.1515/revce-2021-0107

More information

Latest update

9/7/2023 1