Development and comparison of neural network based soft sensors for online estimation of cement clinker quality

ISA Transactions

Volumn 52, Issue 1, 2013, Pages 19-29

  Pani, Ajaya Kumar ^a   Vadlamudi, Vamsi Krishna ^a   Mohanta, Hare Krishna ^a  

^a BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE   (India)

Author keywords

Back propagation neural network; Cement kiln modeling; Radial basis function neural network; Regression neural network; Soft sensor

Indexed keywords

BACKPROPAGATION; CEMENTS; KILNS; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; TORSIONAL STRESS;

BACK PROPAGATION NEURAL NETWORKS; CEMENT KILN; RADIAL BASIS FUNCTION NEURAL NETWORKS; REGRESSION NEURAL NETWORKS; SOFT SENSORS;

QUALITY CONTROL;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BUILDING MATERIAL; COMPUTER SIMULATION; HARDNESS; MATERIALS TESTING; METHODOLOGY; ONLINE SYSTEM; THEORETICAL MODEL; TRANSDUCER;

ALGORITHMS; COMPUTER SIMULATION; CONSTRUCTION MATERIALS; HARDNESS TESTS; MATERIALS TESTING; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); ONLINE SYSTEMS; TRANSDUCERS;

MLCS; MLOWN;

EID: 84871925041     PISSN: 00190578     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isatra.2012.07.004     Document Type: Article

References (37)

1. B. Lin, B. Recke, J. Knudsen, and S.B. Jorgensen A systematic approach for soft sensor development Computers and Chemical Engineering 31 2007 419 425
2. Qiao J, Fang Z, Chai T. SVR L.S. based soft sensor model for cement clinker calcination process. International conference on Measuring Technology and Mechatronics Automation (ICMTMA), vol. 2; 2010. p. 591-4.
3. J.C.B. Gonzagaa, L.A.C. Meleirob, C. Kianga, and R.M. Filho ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process Computers and Chemical Engineering 33 2009 43 49
4. M.R. Delgado, E.Y. Nagai, and L.V.R. Arruda A neuro-coevolutionary genetic fuzzy system to design soft sensors Soft Computing 13 2009 481 495
5. H. Liu, S. Shah, and W. Jiang Online outlier detection and data cleaning Computers and Chemical Engineering 28 2004 1635 1647
6. R.K. Pearson Exploring process data Journal of Process Control 11 2001 179 194
7. R.K. Pearson Outliers in process modeling and identification IEEE Transactions on Control Systems Technology 10 2002 55 63
8. D. Wang, J. Liu, and R. Srinivasan Data driven soft sensor approach for quality prediction in a refining process IEEE Transactions on Industrial Informatics 6 2010 11 17
9. J. Sola, and J. Sevilla Importance of input data normalization for the application of neural networks to complex industrial problems IEEE Transactions on Nuclear Science 1997 1464 1468
10. S. Samarasinghe Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition 2007 Auerbach Publications, Taylor & Francis Group
11. E. Marengo, M. Bobba, E. Robotti, and M.C. Liparota Modeling of the polluting emissions from a cement production plant by partial least-squares, principal component regression, and artificial neural networks Environmental Science and Technology 40 2006 272 280
12. K.A. Al-Shayji, and Y.A. Liu Predictive modeling of large-scale commercial water desalination plants: data-based neural network and model-based process simulation Industrial and Engineering Chemistry Research 41 2002 6460 6474
13. A.H. Mohammadi, and D. Richon Use of artificial neural networks for estimating water content of natural gases Industrial and Engineering Chemistry Research 46 2007 1431 1438
14. A.M. Elsharkwy, and R.B.C. Gharbi Comparing classical and neural regression techniques in modeling crude oil viscosity Advances in Engineering Software 32 2001 215 224
15. O. Kisi, and H.K. Cigizoglu Comparison of different ANN techniques in river flow prediction Civil Engineering and Environmental Systems 24 2007 211 231
16. N.K. Roy, W.D. Potter, and D.P. Landau Polymer property prediction and optimization using neural networks IEEE Transactions on Neural Networks 17 2006 1001 1014
17. S. Linko, J. Luopa, and Y.H. Zhu Neural networks as 'software sensors' in enzyme production Journal of Biotechnology 52 1997 257 266
18. H. Tian, Z. Mao, S. Wang, and K. Li Application of genetic algorithm combined with BP neural network in soft sensor of molten steel temperature WCICA The Sixth World Congress on Intelligent Control and Automation 2 2006 7742 7745
19. C.Ozgen Bahar Artificial neural network estimator design for the inferential model predictive control of an industrial distillation column Industrial and Engineering Chemistry Research 2004 6102 6111
20. S. Chen, C.F.N. Cowan, and P.M. Grant Orthogonal least squares learning algorithm for radial basis function networks IEEE Transactions on Neural Networks 1991)302 309
21. K.A. Gurumoorthy, and Kosanovich Improving the prediction capability of radial basis function networks Industrial and Engineering Chemistry Research 37 1998 3956 3970
22. B. Samanta Radial basis function network for ore grade estimation Natural Resources Research 19 2010 91 102
23. H. Sarimveis, A. Alexandridis, G. Tsekouras, and G. Bafas A fast and efficient algorithm for training radial basis function neural networks based on a fuzzy partition of the input space Industrial & Engineering Chemistry Research 41 2002 751 759
24. A. Ghodsi, and D. Schuurmans Automatic basis selection techniques for RBF networks Neural Networks 16 2003 809 816
25. M. Marinaro, and S. Scarpetta On-line learning in RBF neural networks: a stochastic approach Neural Networks 13 2000 719 729
26. Li C, Ye H, Wang G. Nonlinear time series modeling and prediction using RBF network with improved clustering algorithm. IEEE International Conference on Systems, Man and Cybernetics, vol. 4; 2004. p. 3513-8.
27. Steve A. Billings, and G.L. Zheng Radial basis function network configuration using genetic algorithms Neural Networks 8 1995 877 890
28. D.F. Specht A general regression neural network IEEE Transactions on Neural Networks 2 1991 568 576
29. A.T.C. Goh Soil laboratory data interpretation using generalized regression neural network Civil Engineering and Environmental Systems 16 1999 175 195
30. H.K. Cigizoglu, and M. Alp Generalized regression neural network in modeling river sediment yield Advances in Engineering Software 37 2006 63 68
31. L. Peishenga, X. Youhuia, Y. Dunxia, and S. Xuexin Prediction of grindability with multivariable regression and neural network in Chinese coal Fuel 84 2005 2384 2388
32. B. Kim, M. Kwon, and S.H. Kwon Modeling of plasma process data using a multi-parameterized generalized regression neural network Microelectronic Engineering 86 2009 63 67
33. S. Palani, S.Y. Liong, and P. Tkalich An ANN application for water quality forecasting Marine Pollution Bulletin 56 2008 1586 1597
34. S. Dehghan, Gh. Sattari, C.S. Chelgani, and M.A. Aliabadi Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks Mining Science and Technology 20 2010 41 46
35. Zheng L, Yu S, Yu M. Monitoring NOx emissions from coal fired boilers using generalized regression neural network. The 2nd International Conference on Bioinformatics and Biomedical Engineering, ICBBE; 2008. p. 1916-9.
36. Y. Erzin, B. Hanumantha Rao, and D.N. Singh Artificial neural network models for predicting soil thermal resistivity International Journal of Thermal Sciences 47 2008 1347 1358
37. R. Rallo, J. Ferre-Gine, A. Arenas, and F. Giralt Neural virtual sensor for the inferential prediction of product quality from process variables Computers and Chemical Engineering 26 2002 1735 1754