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Original Article
Online State Prediction of Clinker Kiln Based on LMD and LIELM
Jong Hyok Kim1
Kum Il Choe2
Un Sim Ri3
1 2 3 Faculty of Automation Engineering, Kim Chaek University of Technology, Pyongyang, Democratic People’s Republic of Korea.
Published Online: November-December 2025
Pages: 01-09
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250506001References
1. P. A. Alsop, Cement Plant Operations Handbook, International Cement Review, 3rd Ed., November 2001.
2. P.V. Barr, J.K. Brimacombe, A heat transfer model for the rotary kiln: part I, Pilot kiln trials, Metallurgical and Material Transactions B
20B (1989) 391–402.
3. R. Saidur, M. S. Hossain, M. R. Islam, H. Fayaz, and H. A. Mohammed, A review on kiln system modelling, Renewable and Sustainable
Energy Reviews. 2011.
4. K. S. Mujumdar, A. Arora, and V. V. Ranade, Modelling of rotary cement kilns: Applications to reduction in energy consumption, Ind.
Eng. Chem. Res, 2006.
5. K. S. Mujumdar, K. V. Ganesh, S. B. Kulkarni, and V. V. Ranade, Rotary Cement Kiln Simulator (RoCKS): Integrated modelling of
preheater, calciner, kiln and clinker cooler, Chem. Eng. Sci, 2007.
6. Shizeng Lu, Single-step prediction method of burning zone temperature based on real-time wavelet filtering and KELM, Engineering
Applications of Artificial Intelligence 70 (2018) 142–148
7. M.A. Martins, L.S. Oliveira, A.S. Franca, Modeling and simulation of petroleum coke calcination in rotary kilns, Fuel 80 (2001) 1611–
1622.
8. M. Fallahpour, Designing of ANFIS Controller and Expert Operator for Rotary Cement Kiln, M.Sc. Thesis, K.N. Toosi University of
Tech, Tehran, Iran, 2007.
9. Donoho, D. L, De-noising by Soft-Thresholding, IEEE Transactions on Information Theory, Vol. 42, Number 3, pp. 613–627, 1995.
10. Smith J S, The local mean decomposition and its application to EEG perception data [J], Journal of the Royal Society Interface,2005.
2(5). 443-454.
11. G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: theory and applications, Neurocomputing 70 (2006) 489–501.
12. G.B. Huang, D.H. Wang, Y. Lan, Extreme learning machines: a survey, Int. J. Mach. Learn. Cybern. 2 (2) (2011) 107–122.
13. Sherman J,Morrison J.Adjustment of an inverse matrix corresponding to changes in the elements of a given c01umn or a given row
of the original matrix[J] The Annals of Mathematical Statistics,1949,20(4).620–624.
14. Sherman J,Morrison J.Adjustment of an inverse matrix corresponding to a change in one elements of a given matrix[J].The Annals
of Mathematical Statistics,1950,21(1).124–127
2. P.V. Barr, J.K. Brimacombe, A heat transfer model for the rotary kiln: part I, Pilot kiln trials, Metallurgical and Material Transactions B
20B (1989) 391–402.
3. R. Saidur, M. S. Hossain, M. R. Islam, H. Fayaz, and H. A. Mohammed, A review on kiln system modelling, Renewable and Sustainable
Energy Reviews. 2011.
4. K. S. Mujumdar, A. Arora, and V. V. Ranade, Modelling of rotary cement kilns: Applications to reduction in energy consumption, Ind.
Eng. Chem. Res, 2006.
5. K. S. Mujumdar, K. V. Ganesh, S. B. Kulkarni, and V. V. Ranade, Rotary Cement Kiln Simulator (RoCKS): Integrated modelling of
preheater, calciner, kiln and clinker cooler, Chem. Eng. Sci, 2007.
6. Shizeng Lu, Single-step prediction method of burning zone temperature based on real-time wavelet filtering and KELM, Engineering
Applications of Artificial Intelligence 70 (2018) 142–148
7. M.A. Martins, L.S. Oliveira, A.S. Franca, Modeling and simulation of petroleum coke calcination in rotary kilns, Fuel 80 (2001) 1611–
1622.
8. M. Fallahpour, Designing of ANFIS Controller and Expert Operator for Rotary Cement Kiln, M.Sc. Thesis, K.N. Toosi University of
Tech, Tehran, Iran, 2007.
9. Donoho, D. L, De-noising by Soft-Thresholding, IEEE Transactions on Information Theory, Vol. 42, Number 3, pp. 613–627, 1995.
10. Smith J S, The local mean decomposition and its application to EEG perception data [J], Journal of the Royal Society Interface,2005.
2(5). 443-454.
11. G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: theory and applications, Neurocomputing 70 (2006) 489–501.
12. G.B. Huang, D.H. Wang, Y. Lan, Extreme learning machines: a survey, Int. J. Mach. Learn. Cybern. 2 (2) (2011) 107–122.
13. Sherman J,Morrison J.Adjustment of an inverse matrix corresponding to changes in the elements of a given c01umn or a given row
of the original matrix[J] The Annals of Mathematical Statistics,1949,20(4).620–624.
14. Sherman J,Morrison J.Adjustment of an inverse matrix corresponding to a change in one elements of a given matrix[J].The Annals
of Mathematical Statistics,1950,21(1).124–127
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