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MEASUREMENT AND CALCULATION OF CALORIFIC VALUE OF RAW COAL BASED ON ARTIFICIAL NEURAL NETWORK ANALYSIS METHOD

ABSTRACT
The calorific value of coal is the basic technical basis for calculating parameters such as boiler heat balance, thermal efficiency, and boiler output. The calorific value of coal has different meanings, such as the calorific value of the cartridge, the high calorific value of coal, and the low calorific value of coal to generate heat at a high level of constant humidity and no ash. This paper focuses on the analysis of the structure and algorithm characteristics of artificial neural network and RBF neural network. On this basis, the predictive modelling of the received low-level calorific value is carried out. Through the test summary, the predictiveness of the neural network is better than the empirical formula. For the prediction problem with small sample size, the RBF network has better prediction performance. In addition, the quality of the sample, including its quantity and comprehensiveness, has an important impact on the predictive performance and generalization ability of the model.
KEYWORDS
PAPER SUBMITTED: 2019-11-06
PAPER REVISED: 2019-12-18
PAPER ACCEPTED: 2020-01-16
PUBLISHED ONLINE: 2020-02-29
DOI REFERENCE: https://doi.org/10.2298/TSCI191106087L
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THERMAL SCIENCE YEAR 2020, VOLUME 24, ISSUE 5, PAGES [3129 - 3137]
REFERENCES
  1. Kumari, A., Das, S. K., & Srivastava, P. K. Estimation of scale deposition in the water walls of an operating indian coal fired boiler: predictive modeling approach using artificial neural networks. Journal of the Institution of Engineers, 97(2016),1, pp.39-46.
  2. Shaofei Wu. Study and evaluation of clustering algorithm for solubility and thermodynamic data of glycerol derivatives, Thermal Science, 23(2019), 5, pp.2867-2875.
  3. Xiaoqian, Dameng, & Yanbin. Prediction of youngmodulus of coal using artificial neural networks in qinshui basin, china. Acta Geologica Sinica, 89(2015), s1, pp.339-341.
  4. B.K. Sahoo, S. De, & B.C. Meikap. Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment. International Journal of Mining Science & Technology, 27(2017),2, pp.379-386.
  5. Wen, X., & Jian, S. Modeling of coal consumption rate based on wavelet neural network. International Journal of Modeling Simulation & Scientific Computing, 08(2017), 03, pp.233-261.
  6. Montgomery, M., & Larsen, O. H. Field test corrosion experiments in denmark with biomass fuels. part 2: co‐firing of straw and coal. Materials & Corrosion, 53(2015),3, pp.185-194.
  7. Shaofei Wu,A Traffic Motion Object Extraction Algorithm,International Journal of Bifurcation and Chaos, 25(2015),14,Article Number 1540039.
  8. Oko, E., Wang, M., & Zhang, J. Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant. Fuel, 151(2015),26, pp.139-145.

© 2020 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, 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