Process analysis and product quality estimation by Self-Organizing Maps with an application to polyethylene production

Computers in Industry

Volumn 52, Issue 3, 2003, Pages 221-234

  Abonyi, Janos ^a   Nemth, Sandor ^a   Vincze, Csaba ^a   Arva, Peter ^a  

^a UNIVERSITY OF PANNONIA   (Hungary)

Author keywords

Operating regime based modeling; Process monitoring; Product analysis and design; Self Organizing Map (SOM); Voronoi diagram

Indexed keywords

MATHEMATICAL MODELS; POLYETHYLENES; QUALITY CONTROL; SELF ORGANIZING MAPS;

PROCESS MONITORING;

PROCESS ENGINEERING;

EID: 0141636588     PISSN: 01663615     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0166-3615(03)00128-3     Document Type: Article

References (20)

1. Yamashita Y., Supervised learning for the analysis of the process operational data. Computers and Chemical Engineering. 24:2000;471-474.
2. Zhang J., Martin E., Morris A., Process monitoring using non-linear statistical techniques. Chemical Engineering Journal. 67:1997;181-189.
3. X. Wang, Data Mining and Knowledge Discovery for Process Monitoring and Control, Springer, Berlin, 1999.
4. MacGregor J., Kourti T., Statistical process control of multivariate processes. Control Engineering Practice. 3(3):1995;403-414.
5. E. Martin, A. Morris, M. Papazoglou, C. Kiparissides, Batch process monitoring for consistent production, Computers and Chemical Engineering 20 (1996) S599-S605.
6. Lakshminarayanan S., Fujii H., Grossman B., Dassau E., Lewin D., New product design via analysis of historical databases. Computers and Chemical Engineering. 24:2000;671-676.
7. Y. Moteki, Y. Arai, Operation planning and quality design of a polymer process, IFAC DYCORD, 1986, pp. 159-165.
8. Jeackle C., MacGregor J., Product design through multivariate statistical analysis of process data. American Institute of Chemical Engineering Journal. 44(5):1998;1105-1118.
9. Borosy A., Quantitative composition-property modelling of rubber mixtures by utilizing artificial neural networks. Chemometrics and Intelligent Laboratory Systems. 47:1998;227-238.
10. J. Abonyi, Fuzzy Model Identification for Control, Birkhauser Boston, Cambridge, MA, 2003.
11. Pal N., Soft computing for feature analysis. Fuzzy Sets and Systems. 103:1999;201-221.
12. Kohonen T., The self-organizing map. Proceedings of the IEEE. 78(9)(9):1990;1464-1480.
13. M. Kassalin, J. Kangas, O. Simula, Process state monitoring using self-organizing maps, in: Artificial Neural Networks, vol II, North-Holland, Amsterdam, pp. 1531-1534.
14. V. Tryba, K. Goser, Self-organizing feature maps for process control in chemistry, in: Artificial Neural Networks, North-Holland, Amsterdam, 1991, pp. 847-852.
15. J. Alander, M. Frisk, L. Holmstom, A. Hamalainen, J. Tuominen, Process error detection using self-organizing feature maps, in: Artificial Neural Networks, vol II, North-Holland, Amsterdam, 1991 pp. 1229-1232.
16. J. Principe, L. Wang, M. Motter, Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control, Proceedings of the IEEE 86 (11) 2241-2258.
17. J. Vesanto, 2000. Neural network tool for data mining: Som toolbox, in: Proceedings of Symposium on Tool Environments and Development Methods for Intelligent Systems (TOOLMET2000), Oulun yliopistopaino, Oulu, Finland, pp. 184-196.
18. Roubos J., Setnes M., Abonyi J., Learning fuzzy classification rules from labeled data. Information Sciences. 150(1-2):2003;77-93.
19. Nagy G., The polyethylene. Magyar Kemikusok Lapja (MKL). 52(5)(5):1997;233-242. in Hungarian.
20. O. Simula, J. Vesanto, E. Alhoniemi, J. Hollomen, Analysis and modeling of complex systems using the Self-Organizing Map, in: Physica Verlag, Springer, Verlag, 1999.