Machine learning model designed to predict the amount of CO2 produced by a small pellet boiler

Alexander Backa; Juraj Drga; Lucia Martvoňová; Mária Polačiková

doi:10.1051/matecconf/202134500002

All issues

Volume 345 (2021)

MATEC Web Conf., 345 (2021) 00002

References

Open Access

Issue		MATEC Web Conf. Volume 345, 2021 20^th Conference on Power System Engineering


Article Number		00002
Number of page(s)		9
DOI		https://doi.org/10.1051/matecconf/202134500002
Published online		12 October 2021

Jandacka, J. - Micieta, M. - Holubcik, M. - Nosek, R., (2016): Inovácie na zefektivnenie procesu spal’ovania biomasy. Zilina: EDIS - vydavatel’ské centrum ZU, 265 s. ISBN 978-80-554-1236-8. [Google Scholar]
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[1] Jandacka, J. - Micieta, M. - Holubcik, M. - Nosek, R., (2016): Inovácie na zefektivnenie procesu spal’ovania biomasy. Zilina: EDIS - vydavatel’ské centrum ZU, 265 s. ISBN 978-80-554-1236-8. [Google Scholar]

[2] Hernandez, R. - Ballester, J. Flame imaging as a diagnostic tool for industrial combustion. Combustion and Flame. (2008). 155. s. 509–528. [CrossRef] [Google Scholar]

[3] Han, Z. - Li, J. - Hossain, M. - Xu, Ch. Prediction of combustion state through a semi-supervised learning model and flame imaging. Fuel. (2021). 289 s. [Google Scholar]

[4] Zheng, Z. et al. Progress in the Application of Machine Learning in Combustion Studies. ES Energy & Environment. (2020). [Google Scholar]

[5] Morari, M. - Lee, J. H. Model predictive control: past, present and future. Computers & Chemical Engineering. (1999). 23(4-5). s. 667–682. [CrossRef] [Google Scholar]

[6] Elmaz, F. - Yücel, Ö. - Mutlu, A. I. Predictive modeling of biomass gasification with machine learning-based regression methods. Energy. (2020). 191, ISSN: 0360-544. [Google Scholar]

[7] Baruah, D., Baruah, D.C., Hazarika, M.K., (2017). Artificial neural network based modeling of biomass gasification in fixed bed downdraft gasifiers. Biomass and Bioenergy 98, s. 264–271. [CrossRef] [Google Scholar]

[8] Mutlu, A.Y., Yucel, O., (2018). An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification. Energy 165, s. 895–901. [CrossRef] [Google Scholar]

[9] Zhou, Z.-H. A brief introduction to weakly supervised learning. National Science Review. (2018). 5(1). s. 44–53. [CrossRef] [Google Scholar]

[10] Binod, J. R. - Ravi, M. R. Development of numerical model for analysis of biomass based pottery furnace. [autorcafé], (2019), s.l.: Summer Research Fellowship Programme of India’s Science Academies. [Google Scholar]

[11] Ambroise, C. - Mclachlan G.J. Selection bias in gene extraction on the basis of microarray gene-expression data. (2002). Proc Natl Acad Sci, 99 (10), pp. 6562–6566 [CrossRef] [Google Scholar]

[12] Draper, N. R. - Smith, H. Applied Regression Analysis. (1998). Wiley-Interscience. ISBN 978-0-471-17082-2 [Google Scholar]