International Scientific Journal

External Links


This study presents a soft sensing model of coal quality for utility boilers. This model is based on the coal quality information obtained from exhaust gas. The mechanism modeling method combined with data driving theory is used in the modeling process. The procedure for solving the nonlinear equations for coal quality applies the inner loop iteration of dry ash-free basis of S (Sdaf) and outside loop iteration of dry ash-free basis of N (Ndaf) within a limited range, and dry ash-free basis of C (Cdaf) is searched from the entire range during outside loop iteration. The upper and lower limits of Ndaf are defined according to the NOX content in the exhaust gas, thereby solving the iterative initial value selection problem. Finally, the effectiveness of the proposed method is verified via several simulations and comparisons, the results show that this method is credible and effective and it can be used in power plant for control system optimization.
PAPER REVISED: 2014-02-25
PAPER ACCEPTED: 2014-01-27
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2015, VOLUME 19, ISSUE Issue 1, PAGES [231 - 242]
  1. Yao, H.M.,, Artificial neural network-based prediction of hydrogen content of coal in power station boilers, Fuel, 84(2005), pp.1535-1542.
  2. Chelgani, S.C.,, Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network, Int J Coal Geol, 83(2010), pp.31-34.
  3. Wallis, F.J.,, Analysis of lignite using laser-induced breakdown spectroscopy, J Appl Spectrosc, 54(2000), pp.1231-1235.
  4. Ponte, D.G.,, Determination of moisture content in power station coal using microwaves, Fuel, 75(1996), pp.133-138.
  5. Kim, D.W.,, Application of near infrared diffuse reflectance spectroscopy for on-line measurement of coal properties, Korean J Chem Eng, 26(2009), pp.489-495.
  6. Tanno, T.,, Estimation of water content in coal using terahertz spectroscopy, Fuel, 105(2013), pp.769-770.
  7. Odgaard, P.F., Mataji, B., Observer-based fault detection and moisture estimating in coal mill, Control Eng Pract, 16(2008), pp.909-921.
  8. Kortela, J., Jamsa-Jounela, S.L., Fuel moisture soft-sensor and its validation for the industrial BioPower 5 CHP plant, Appl Energy, 105(2013), pp.66-74.
  9. Shakil, M.,, Soft sensor for NOx and O2 using dynamic neural networks, Computers and Electrical Engineering, 35(2009), pp.578-586.
  10. Mika, L.,, Dynamic soft sensors for NOx emissions in a circulating fluidized bed boiler, Appl Energy, 97(2013), pp.483-490.
  11. Kadlec, P.,, Data-driven soft sensors in the process industry, Computers and Chemical Engineering, 33(2009), pp.795-814.
  12. Zhao, Z.,, Research on soft-sensing of oxygen contend based on data fusion, Proceedings of the CSEE(in Chinese), 25(2005), pp.7-12.
  13. Gao, Z., Dai, X., From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis, IEEE Trans Industr Inform, 2013, pp.1-12.
  14. Liu, F.G., Real time identification technique for ultimate analysis and calorific value of burning coal in utility boiler, Proceedings of the CSEE(in Chinese), 25(2005), pp.139-145.
  15. Lang, F.D., The input/loss technology, Power Engineering(in Chinese), 20(2000), pp.847-862.

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence