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

Mohammadi, Kasra; Immonen, Jake; Blackburn, Landen D.; Tuttle, Jacob F.; Andersson, Klas; Powell, Kody M.

doi:10.1515/revce-2021-0107

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

Journal Article · 12 August 2022 · Reviews in Chemical Engineering

DOI:https://doi.org/10.1515/revce-2021-0107· OSTI ID:1981002

Mohammadi, Kasra ^[1]; Immonen, Jake ^[1]; Blackburn, Landen D. ^[1]; Tuttle, Jacob F. ^[1]; Andersson, Klas ^[2]; Powell, Kody M. ^[3]

Department of Chemical Engineering , University of Utah , 50 S. Central Campus Dr., Room 3290 MEB , Salt Lake City , UT 84112-9203 , USA
Department of Space, Earth and Environment, Energy Technology , Chalmers University of Technology , SE – 412 96 Göteborg , Sweden
Department of Chemical Engineering , University of Utah , 50 S. Central Campus Dr., Room 3290 MEB , Salt Lake City , UT 84112-9203 , USA; Department of Mechanical Engineering , University of Utah , 1495 E 100 S., Room 1550 MEK , Salt Lake City , UT 84112 , USA

Abstract 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.

Research Organization:: Univ. of Utah, Salt Lake City, UT (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE)

DOE Contract Number:: FE0031754

OSTI ID:: 1981002

Journal Information:: Reviews in Chemical Engineering, Vol. 0, Issue 0; ISSN 0167-8299

Publisher:: de Gruyter

Country of Publication:: United States

Language:: English

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