123
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Comparative study of machine learning methods integrated with different optimisation algorithms for prediction of thermal performance and emissions in a pellet stove

ORCID Icon, ORCID Icon & ORCID Icon
Pages 7673-7693 | Received 14 Feb 2023, Accepted 02 Jun 2023, Published online: 16 Jun 2023

References

  • Adams, D., O. Dong-Hoon, D.-W. Kim, C.-H. Lee, and O. Min. 2020. Prediction of SOx–NOx emission from a coal-fired cfb power plant with machine learning: Plant data learned by deep neural network and least square support vector machine. Journal of Cleaner Production 270:122310. doi:10.1016/j.jclepro.2020.122310.
  • Backa, A., J. Drga, L. Martvoňová, M. Polačiková, L. Richter, M. Pelikan, M. Volf, and M. Vackova. 2021. Machine learning model designed to predict the amount of CO 2 produced by a small pellet boiler. MATEC Web of Conferences 345 (October):2. doi:10.1051/matecconf/202134500002.
  • Badra, J. A., F. Khaled, M. Tang, Y. Pei, J. Kodavasal, P. Pal, O. Owoyele, C. Fuetterer, B. Mattia, and F. Aamir. 2020. Engine combustion system optimization using computational fluid dynamics and machine learning: A methodological Approach. Journal of Energy Resources Technology 143 (2). doi:10.1115/1.4047978.
  • Castro, C., L. Fraga, E. Ferreira, J. Martins, P. Ribeiro, and J. C. Teixeira. 2021. Experimental studies on wood pellets combustion in a fixed bed combustor using taguchi method. Fuels 2 (4):376–92. doi:10.3390/fuels2040022.
  • Ceylan, Z., and B. Sungur. 2020. Estimation of coal elemental composition from proximate analysis using machine learning techniques. Energy sources, part a: recovery, utilization and environmental effects 2576–92. Taylor & Francis. 10.1080/15567036.2020.1790696
  • Choudhury, N. D., N. Saha, B. Ranjan Phukan, and R. Kataki. 2021, January. Characterization and evaluation of energy properties of pellets produced from coir pith, saw dust and ipomoea carnea and their blends. In Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–18. Taylor & Francis. doi:10.1080/15567036.2020.1871446.
  • Claesen, M., J. Simm, D. Popovic, Y. Moreau, and B. De Moor. 2014, December. Easy Hyperparameter Search Using Optunity. 10.48550/arxiv.1412.1114
  • Das, A. K., D. Das, S. Jaypuria, D. Kumar Pratihar, and G. Gopal Roy. 2021. Input–output modeling and multi-objective optimization of weld attributes in EBW. Arabian Journal for Science & Engineering Springer Science and Business Media Deutschland GmbH. 46(4):4087–101. doi:10.1007/s13369-020-05248-1.
  • Gjorgievski, V. Z., N. Markovska, T. Pukšec, N. Duić, and A. Foley. 2021. Supporting the 2030 agenda for sustainable development: special issue dedicated to the conference on sustainable development of energy, water and environment systems 2019. Renewable and Sustainable Energy Reviews 143:110920. doi:10.1016/j.rser.2021.110920.
  • González, J. F., B. Ledesma, A. Alkassir, and J. González. 2011. Study of the influence of the composition of several biomass pellets on the drying process. Biomass & bioenergy 35 (10):4399–406. doi:10.1016/j.biombioe.2011.08.019.
  • Grochowalski, J., P. Jachymek, M. Andrzejczyk, M. Klajny, A. Widuch, P. Morkisz, B. Hernik, J. Zdeb, and W. Adamczyk. 2021. Towards application of machine learning algorithms for prediction temperature distribution within CFB boiler based on specified operating conditions. Energy 237:121538. doi:10.1016/j.energy.2021.121538.
  • Haobo, B., Q. Lin, C. Wang, X. Jiang, C. Jiang, and L. Bao. 2020. An experimental study of single unconventional biomass pellets: ignition characteristics, combustion processes, and artificial neural network modeling. International Journal of Energy Research John Wiley & Sons, Ltd 44 (4):2952–65. doi:10.1002/er.5117.
  • Jasper, S., H. Larochelle, and R. P. Adams. 2012. Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems 4:2951–59. doi:10.48550/arxiv.1206.2944.
  • Kaleli, A., and H. İ̇brahim Akolaş. 2021, June. The design and development of a diesel engine electromechanical EGR cooling system based on machine learning-genetic algorithm prediction models to reduce emission and fuel consumption. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, SAGE Publications Ltd. doi:10.1177/09544062211020045.
  • Karthic, S. V., and S. Kumar Masimalai. 2020. Predicting the performance and emission characteristics of a mahua oil-hydrogen dual fuel engine using artificial neural networks. Energy Sources, Part A: Recovery, Utilization, & Environmental Effects Taylor & Francis 42 (23):2891–910. doi:10.1080/15567036.2019.1618997.
  • Kocer, A., and A. Kurklu. 2022. Production of pellets from pruning residues and determination of pelleting physical properties. Energy Sources, Part A: Recovery, Utilization, & Environmental Effects Taylor & Francis. 44(4):10346–58. doi:10.1080/15567036.2020.1752857.
  • Kougioumtzis, M. A., I. Panagiota Kanaveli, E. Karampinis, P. Grammelis, and E. Kakaras. 2021. Combustion of olive tree pruning pellets versus sunflower husk pellets at industrial boiler. Monitoring of emissions and combustion efficiency. Renewable Energy 171:516–25. doi:10.1016/j.renene.2021.02.118.
  • Kovalnogov, V., R. Fedorov, V. Klyachkin, D. Generalov, Y. Kuvayskova, and S. Busygin. 2022. Applying the random forest method to improve burner efficiency. Mathematics 10 (12):2143. doi:10.3390/math10122143.
  • Liu, H., J. Chaney, L. Jinxing, and C. Sun. 2013. Control of NOx emissions of a domestic/small-scale biomass pellet boiler by air staging. Fuel 103:792–98. doi:10.1016/j.fuel.2012.10.028.
  • Moffat, R. J. 1988. Describing the uncertainties in experimental results. Experimental Thermal & Fluid Science 1 (1):3–17. doi:10.1016/0894-1777(88)90043-X.
  • Nyirandayisabye, R., L. Huixia, Q. Dong, T. Hakuzweyezu, and F. Nkinahamira. 2022. Automatic pavement damage predictions using various machine learning algorithms: evaluation and comparison. In Results in Engineering, September, 100657. Elsevier. doi:10.1016/j.rineng.2022.100657.
  • Sharma, P., Z. Said, A. Kumar, S. Nižetić, A. Pandey, A. Tuan Hoang, Z. Huang, A. Afzal, L. Changhe, L. Anh Tuan, et al. 2022. Recent advances in machine learning research for nanofluid-based heat transfer in renewable energy system. Energy & Fuels American Chemical Society 36 (13):6626–58. doi:10.1021/acs.energyfuels.2c01006.
  • Sungur, B., and B. Topaloglu. 2019. An experimental investigation of the effect of smoke tube configuration on the performance and emission characteristics of pellet-fuelled boilers. Renewable Energy 143:121–29. doi:10.1016/j.renene.2019.05.006.
  • Sungur, B., and C. Basar. 2023. Experimental investigation of the effect of supply airflow position, excess air ratio and thermal power input at burner pot on the thermal and emission performances in a pellet stove. Renewable Energy 202:1248–58. doi:10.1016/j.renene.2022.12.042.
  • Surono, U. B., and H. Saptoadi. 2022. Pellet combustion characteristics and emission of cocoa pod shell and coal blends. Biomass Conversion and Biorefinery. doi:10.1007/s13399-022-02469-2.
  • Wong, K. I., P. Kin Wong, C. Shun Cheung, and C. Man Vong. 2013. Modeling and optimization of biodiesel engine performance using advanced machine learning methods. Energy 55:519–28. doi:10.1016/j.energy.2013.03.057.
  • Yang, L., and A. Shami. 2020. On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing Elsevier. 415(November):295–316. doi:10.1016/j.neucom.2020.07.061.
  • Yildizay, H. D., and A. Esiyok. 2021, October. Experimental investigation of coal and pellet combustion in a manual loaded stove. In Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–15. Taylor & Francis. doi:10.1080/15567036.2021.1983085.
  • Yilmaz, H., M. Topakci, D. Karayel, and M. Çanakci. 2020, November. Comparison of the physical properties of cotton and sesame stalk pellets produced at different moisture contents and combustion of the finest pellets. In Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–19. Taylor & Francis. doi:10.1080/15567036.2020.1850931.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.