THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

online first only

Feature selection for coal heating level estimation in thermal power plants

ABSTRACT
Several recently signed environmental agreements and protocols emphasize the global need to reduce greenhouse gas emissions, with a focus on limiting coal consumption due to high NOX and CO2 emissions. However, many countries, including those in the Western Balkans, rely heavily on coal for electricity generation. The outdated thermal power plant infrastructure in these regions poses a major challenge when it comes to meeting modern environmental standards while maintaining efficiency. This study is part of the more comprehensive research which aims to develop an expert system that utilizes existing measurements to estimate key parameters crucial for both energy production and pollution reduction. The focus is on Serbian thermal power plants, particularly plant "Nikola Tesla" unit B1. One of the critical parameters for optimizing thermal power plant control loops is the heating value of coal, which is challenging to measure in real time due to the coal's varying chemical compositions and caloric values. This paper examines 74 different parameters measured in 59 instances to estimate the hating value of coal at unit B1. Through detailed analysis and feature selection methods, including linear regression, this research aims to identify the most informative parameters for estimating the heating value of coal, which will improve the control system that enables more efficient and environmentally friendly power generation in coal fired thermal power plants.
KEYWORDS
PAPER SUBMITTED: 2024-01-24
PAPER REVISED: 2024-05-02
PAPER ACCEPTED: 2024-05-15
PUBLISHED ONLINE: 2024-05-25
DOI REFERENCE: https://doi.org/10.2298/TSCI240124124V
REFERENCES
  1. Manisalidis, I., et al., Environmental and health impacts of air pollution: a review, Frontiers in public health, 8 (2020), pp. 505570
  2. Kuyper, J., et al., The Evolution of the UNFCCC, Annual Review of Environment and Resources, 43 (2018), pp. 343-368
  3. EURACOAL, European Association for Coal and Lignite, Coal Industry across the Europe 8-th edition, (2024), ISSN 2034-5682
  4. Henderson, C., Upgrading and efficiency improvement in coal-fired power plants, CRC/221 IEA Clean Coal Centre (2013)
  5. Wynn, G., Coghe, P., Europe's coal-fired power plant: rough times ahead, Institute for Energy Economics and Financial Analysis, (2017)
  6. Đoković, N., et al., Petrographical and organic geochemical study of the lignite from the Smederevsko Pomoravlje field (Kostolac Basin, Serbia), International journal of coal geology, 195 (2018), pp. 139-171
  7. Stupar, G., et al., Assessing the impact of primary measures for NOx reduction on the thermal power plant steam boiler, Applied Thermal Engineering, 78 (2015), pp. 397-409
  8. Lu, S., Hogg B. W., Dynamic nonlinear modelling of power plant by physical principles and neural networks, International Journal of Electrical Power & Energy Systems, 22 (2000), 1, pp. 67-78
  9. Mesroghli, S., et al., Estimation of gross calorific value based on coal analysis using regression and artificial neural networks, International Journal of Coal Geology, 79 (2009), 1-2, pp. 49-54
  10. Mladenović, M. R., et al., Criteria selection for the assessment of Serbian lignites tendency to form deposits on power boilers heat transfer surfaces, Thermal Science, 13 (2009), 4, pp. 61-78
  11. Hasler, D., Coal-Fired Power Plant Heat Rate Reduction, Sargent&Lundy, Report No. 13276-001, Chicago, USA, 2009
  12. Williams, A., Combustion and gasification of coal. Routledge, 2018
  13. Wang, L., et al., A method for in-situ measurement of calorific value of coal: a numerical study, Thermochimica Acta, 703 (2021), pp. 179011
  14. Rencher, A. C., Christensen, W. F., Methods of Multivariate Analysis, John Wiley and Sons Inc., New York, USA, 2012
  15. Hayes, A. F., et al., Cautions regarding the interpretation of regression coefficients and hypothesis tests in linear models with interactions, Communication Methods and Measures 6, (2012), 1, pp. 1-11
  16. Zhou, J., Zhang, W. Coal consumption prediction in thermal power units: A feature construction and selection method, Energy, 273 (2023), pp. 126996
  17. Liu, Y., et al., Feature selection method reducing correlations among features by embedding domain knowledge, Acta Materialia, 238 (2022), pp. 118195
  18. Chandrashekar, G., Sahin, F., A survey on feature selection methods, Computers & electrical engineering, 40 (2014), 1, pp. 16-28