Modeling of steelmaking process with effective machine learning techniques

Expert Systems with Applications

Volumn 42, Issue 10, 2015, Pages 4687-4696

  Laha, Dipak ^a   Ren, Ye ^b   Suganthan, P N ^b  

^a JADAVPUR UNIVERSITY   (India)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

5 Fold cross validation; Artificial neural networks; Dynamic evolving neural fuzzy inference system; Modeling; Prediction; Random forests; Steelmaking process; Support vector regression

Indexed keywords

ABILITY TESTING; ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; DECISION TREES; FORECASTING; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; IRON ORES; IRON SCRAP; LEARNING SYSTEMS; METAL CASTINGS; MODELS; NEURAL NETWORKS; SCRAP METAL; STEEL INGOTS; STEEL METALLURGY; STEEL SCRAP; STEELMAKING;

CROSS VALIDATION; NEURAL FUZZY INFERENCE SYSTEMS; RANDOM FORESTS; STEELMAKING PROCESS; SUPPORT VECTOR REGRESSION (SVR);

SCRAP METAL REPROCESSING;

EID: 84923831927     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2015.01.030     Document Type: Article

References (51)

1. U. Aich, and S. Banerjee Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization Applied Mathematical Modelling 38 2014 2800 2818
2. A. Azadeh, M. Saberi, A. Gitiforouz, and Z. Saberi A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation Expert Systems with Applications 36 2009 11808 11817
3. D. Basak, S. Pal, and D.C. Patranabis Support vector regression Neural Information Processing Letters and Reviews 11 10 2007 203 224
4. E.M. Bezerra, A.C. Ancelotti, L.C. Pardini, J.A.F.F. Rocco, K. Iha, and C.H.C. Ribeiro Artificial neural networks applied to epoxy composites reinforced with carbon and E-glass fibers: Analysis of the shear mechanical properties Materials Science and Engineering A 464 2007 177 185
5. G. Biau Analysis of a random forests model Journal of Machine Learning Research 13 2012 1063 1095
6. L. Breiman Bagging predictors Machine Learning 24 1996 123 140
7. L. Breiman Random forests Machine Learning 45 1 2001 5 32
8. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and regression trees 1984 Wadsworth & Brooks/Cole Advanced Books and software Monterey, CA
9. U. Caydas, A. Hascalik, and S. Ekici An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM Expert Systems with Applications 36 2009 6135 6139
10. C.-C. Chang, and C.-J. Lin LIBSVM: A library for support vector machines ACM Transactions on Intelligent Systems and Technology 2 2011 1 27
11. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee Choosing multiple parameters for support vectors machines Machine Learning 46 2002 131 159
12. J.S. Cooper, H. Kiiveri, E. Chow, L.J. Hubble, M.S. Webster, and K.-H. Muller Quantifying mixtures of hydrocarbons dissolved in water with a partially selective array random forests analysis Sensors and Actuators B: Chemical, B 202 2014 279 285
13. C. Cortes, and V. Vapnik Support-vector networks Machine Learning 20 3 1995 273 297
14. T. Erzurumlu, and H. Oktem Comparison of response surface model with neural network in determining the surface quality of moulded parts Materials and Design 28 2 2007 459 465
15. J. Falkus, P. Pietrzkiewicz, W. Pietrzyk, and J. Kusiak Artificial neural network predictive system for oxygen steelmaking converter Neural networks and soft computing 2003 Springer-Verlag 825 830
16. Genuer, R., Poggi, J. -M., & Tuleau-Malot, C. (2008). Random forests: Some methodological insights. arXiv: 0811.3619.
17. I. Gresovnik, T. Kodelja, R. Vertnik, and B. Sarler Application of artificial neural networks to improve steel production process Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing 2012 249 255
18. M.T. Hagan, and M.B. Menhaj Training feed forward networks with the Marquardt algorithm IEEE Transactions on Neural Networks 5 1994 989 993
19. M. Han, and C. Liu Endpoint prediction model for basic oxygen furnace steel-making based on membrane algorithm evolving extreme learning machine Applied Soft Computing 19 2014 430 437
20. S. Haykin Neural networks and learning machines 2009 Pearson Education Inc
21. Jaiantilal, A. (2009). Classification and regression by randomforest-matlab. [Online]. Available: < http://www.code.google.com/p/randomforest-matlab >.
22. M. Jalali-Heravi, M. Asadollahi-Baboli, and P. Shahbazikhah QSAR study of Heparanase inhibitors activity using artificial neural networks and Levenberg-Marquardt algorithm European Journal of Medicinal Chemistry 43 2008 548 556
23. Y. Kara, M.A. Boyacioglu, and O.K. Baykan Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul stock exchange Expert Systems with Applications 38 2011 5311 5319
24. S.R. Karnik, V.N. Gaitonde, J. Campos Rubio, A. Esteves Correia, A.M. Abrão, and J. Paulo Davim Delamination analysis in high speed drilling of carbon fiber reinforced plastics (CFRP) using artificial neural network model Materials and Design 29 2008 1768 1776
25. N. Kasabov, and Q. Song DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction IEEE Transactions on Fuzzy Systems 10 2002 144 154
26. A. Kavousi-Fard, H. Samet, and F. Marzbani A new modified firefly algorithm and support vector regression model for accurate short term load forecasting Expert Systems with Applications 41 2014 6047 6056
27. B. Lariviere, and D. Van den Poel Predicting customer retention and profitability by random forests and regression forests techniques Expert Systems with Applications 29 2005 472 484
28. M. Liu, M. Wang, J. Wang, and D. Li Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar Sensors and Actuators, B 177 2013 970 980
29. D.W. Marquardt An algorithm for least-squares estimation of non-linear parameters Journal Society Industrial Applied Mathematics 11 1963 431 441
30. D. Mazumdar, and J.W. Evans Modeling of steelmaking processes 2009 CRC Press
31. L.V. Monteiro, and A.M.O. Sant'Anna Application of neural network for modeling steelmaking process Congrso Latino-lberoameroamericano de investigacion Oprative 2012 1618 1627
32. I. Mukherjee, and S. Routroy Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process Expert Systems with Applications 39 2012 2397 2407
33. S. Oliveira, F. Oehler, J. San-Miguel-Ayanz, A. Camia, and J.M.C. Pereira Modeling spatial patterns of fire occurence in Mediterranean Europe using multiple regression and random forest Forest Ecology and Management 275 2012 117 129
34. M. Pal, S.K. Pal, and A.K. Samantaray Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals Journal of Materials Processing Technology 202 2008 464 474
35. G. Ratsch, T. Onoda, and K.-R. Miller Soft margin for AdaBoost Machine Learning 42 3 2001 287 320
36. F.J. Pontes, A.P. de Paiva, P.P. Balestrassi, J.R. Ferreira, and M.B. da Silva Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays Expert Systems with Applications 39 9 2012 7776 7787
37. M. Rizal, J.A. Ghani, M.Z. Nuawi, and C.H.C. Haron Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system Applied Soft Computing 13 2013 1960 1968
38. F. Sanchez Lasheras, P.J. Garcia Nieto, F.J. de Cos Juez, and J.A. Vilan Vilan Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process Applied Mathematics and Computation 227 2014 164 170
39. M. Seera, and C.P. Lim A hybrid intelligent system for medical data classification Expert Systems with Applications 41 2014 2239 2249
40. Shukla, A. K., & Deo, B. (2007). Mathematical modeling of phosphorus prediction in BOF steelmaking process: A fundamental approach to produce low phosphorus steels and ensure direct tap practices. In International conference on metal and alloys (METALL'07).
41. R. Slavkovic, Z. Jugovic, S. Dragicevic, A. Jovicic, and V. Slavkovic An application of learning machine methods in prediction of wear rate of wear resistant casting parts Computers and Industrial Engineering 64 2013 850 857
42. Smola, A., & Scholkopf, B. (2003). A tutorial on support vector regression. RSISE, Australian National University and Max-Planck-Institut fur biologische Kybernetik, Technical Report.
43. Song, Q. (2011). Users guide for DENFIS functions and GUI, University of Otago.
44. Song, Q., & Kasabov, N. (2000). Dynamic evolving neural-fuzzy inference system (DENFIS): On-line learning and application for time-series prediction. In Proceedings of 6th international conference on soft computing (pp. 696-670). Iizuka, Fukuoka, Japan.
45. T. Takagi, and M. Sugeno Fuzzy identification of systems and its applications to modeling and control IEEE Transactions on Systems, Man, Cybernetics 15 1985 116 132
46. A. Talei, L.H.C. Chua, and C. Quek A novel application of neuro-fuzzy computational technique in event-based rainfall-runoff modeling Expert Systems with Applications 37 2010 7456 7468
47. X. Tang, L. Zhuang, and C. Jiang Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization Expert Systems with Applications 36 2009 11853 11857
48. M.J. Tsai, C.H. Li, and C.C. Chen Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm Journal of Materials Processing Technology 208 2008 270 283
49. Y. Xie, X. Li, E.W.T. Ngai, and W. Ying Customer churn prediction using improved balanced random forests Expert Systems with Applications 36 2009 5445 5449
50. P. Yuvaraj, A.R. Murty, N.R. Iyer, S.K. Sekar, and P. Samui Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete Engineering Fracture Mechanics 98 2013 29 43
51. Q. Zhou, Y. Wu, C.W. Chan, and P. Tontiwachwuthikul Modeling of the carbon dioxide capture process system using machine intelligence approaches Engineering Applications of Artificial Intelligence 24 2011 673 685