Soft Computing Applications in Pulp and Paper Industry

  • Kauko Leiviskä
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 71)

Abstract

In Scandinavia, paper industry has been in the pioneering role in the development of process automation that started already in the 1950s with centralised control rooms and standardised signal systems. Computerised process automation dates from the early 1960s with paper machine and digester control systems, when business computers of that time were the first computer control systems (Leiviskä 1999a).

Keywords

Fuzzy Logic Expert System Pulp Mill Fuzzy Logic Control Kappa Number 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahola, T., Ruuska, J., Juuso, E., Leiviskä, K. (1999): Web Break Sensitivity Indicator. In: Proceedings of TOOLMET99 Symposium — Tool Environments and Development Methods for Intelligent Systems. University of Oulu, Oulu, pp. 202–209Google Scholar
  2. Amyot, R., Yeung, L., Phan, S., Henzell, R., Futcher, J., Kricorissian, K., McLellan, F. (1998): Papermaker’s advisor. In: 84th Annual Meeting Preprints. Technical Section CPPA, Montreal, pp. B177–179Google Scholar
  3. Back, B., Irjala, M., Sere, K., Vanharanta, H. (1997): Competitive financial benchmarking using self-organising maps. Pap. Puu 79 (1), 42–49Google Scholar
  4. Baines, G.H., Hayes, R.L., Stabell, J.L. (1997): Prediction boiler emission with neural networks. Tappi J. 80(5), 57–61,87, 95, 103Google Scholar
  5. Basri, H., Stentiford, E.I. (1997): Expert system for environmental monitoring of sanitary landfills. In: 1997 Environmental Conference Proceedings. Tappi Press, Atlanta, pp. 381–384Google Scholar
  6. Brack, C.L., Marshall, P.L. (1996): Test of knowledge-based forest operation scheduling procedures. Can. J. For. Res. 26 (7), 1193–1202CrossRefGoogle Scholar
  7. Broeren, L.A., Smith, B.A., Conte, T.O., Stapley, C. (1995): Deinking process optimization using neural networks. In: Proceeding of Wastepaper VI Conference. Paper no. 14, 25 pGoogle Scholar
  8. Brooks, T. R., Edwards, L. L. (1992): A real-time, model-based expert system for multiple effect evaporators. Tappi J. 75 (11), 131–135Google Scholar
  9. Chiu, C.C., Occena, L.G. (1995): Neural models for characterization and interpretation of hardwood internal defect configuration. In: Proceedings of the 1995 Artificial Neural Networks in Engineering. ASME Press, pp. 905–910Google Scholar
  10. Diaz, A. C., Orchard, R. A., Amyot R., Braham, J.W. (1992): Qualitative modelling techniques for process control and diagnosis. Tappi J. 75 (11), 149–155Google Scholar
  11. Edwards, P.J., Murray, A.F., Papadopoulos, G., Gordon, M.F., Wallace, A.R., Barnard, J. (1999): Paper curl prediction. IEE Conf. Publ 1, (470), 335–340Google Scholar
  12. Ells, A., Bulte, E., van Kooten, G. (1997): Uncertainty and forest land use allocation in British Columbia: Vague priorities and imprecise coefficients. For. Sci. 43 (4), 509–520Google Scholar
  13. Fadum, O. (1996): Introduction to artificial intelligence. In: Fadum, O. (ed.): Process, Quality and Information Control in the Worldwide Pulp and Paper Industry. Miller Freeman Inc., New York, pp. 16–17Google Scholar
  14. Fadum, O. (1996): Expert systems in action. In: Fadum, O. (ed.): Process, Quality and Information Control in the Worldwide Pulp and Paper Industry. Miller Freeman Inc., New York, pp. 23–34Google Scholar
  15. Finchem, K. (1998): Neural network technology moves toward mainstream control use. Pulp Pap. 72(4), 63–64, 67–68Google Scholar
  16. Furumoto, H. (1995): Advanced control strategies for digester: analytical model-fuzzy logic-neural networks. In: 28° Congresso Anual de Celulose e Papel. ABTCP, Sao Paolo, pp. 241–251Google Scholar
  17. Haataja, K.; Leiviskä, K.; Sutinen, R. (1997): Kappa number estimation with neural networks. In: Proceedings of XIV IMEKO World Congress: New measurements — Challenges and Visions. Finnish Society of Automation, Helsinki, Volume XA, pp. 1–5Google Scholar
  18. Haataja, K., Ruotsalainen, J. (1994): Fuzzy Logic in Lime Kiln Control. In: Proceedings, MEPP’94 (Reports on Computer Science and Mathematics, Abo Akademi, Ser. B, No 17 )Google Scholar
  19. Hergert, D., Ahner, R.J. (1999): Using neural networks to improve smoothness on a paper coating process. In: Dagli, C. (ed.): Intelligent Engineering Systems through Artificial Neural Networks vol. 9. ASME Press, 727–732Google Scholar
  20. Horton, R., Denlinger, M., Lien, S., Schmidl, W., Grace, T. (1998): Artificial neural network modeling of a kraft recovery boiler. In: 1998 International Chemical Recovery Conference Proceedings. TAPPI Press, Atlanta, pp. 283–298Google Scholar
  21. Hosti, T., Leiviskä, K., Sutinen, R. (1994): Burning Expert System for Recovery Boilers, In: 1994 Control Systems Preprints. SPCI, Stockholm, Sweden, pp. 313–319Google Scholar
  22. Hsieh, H., Cheng, C., Chen, Y. (1997): Building expert system for forest-land classification. Taiwan J. For. Sci., 3, 255–268Google Scholar
  23. Jofriet, P., Seppala, C., Harvey, M., Surgenor, B., Harris, T. (1996): Expert System for control-loop performance. Pulp Pap. Can. 97 (6), 70–74Google Scholar
  24. Jordan, R., Feeney, F., Evertsen, J.A. (1997). Classification of wood species by neural network analysis of ultrasonic signals. Ultrasonics 36 (1–5), 219–222CrossRefGoogle Scholar
  25. Juuso, E..K., Ahola, T., Leiviskä, K. (1997). Fuzzy Modelling of a Rotary Lime Kiln Using Linguistic Equations and Neuro-fuzzy Methods. In: Foulloy, L. (ed.): 3rd IFAC Symposium on Intelligent Components and Instruments for Control Applications (SICICA’97) Preprints. IFAC, Annecy, France, pp. 579–584Google Scholar
  26. Koepenick, M. (1996): Clear results from fuzzy logic. PIMA 78 (3), 38–41Google Scholar
  27. Kosunen, J., van Fleet, R., Forslund, O. (1996): How can new control methods improve pulp-mill performance. In: 5th International Conference on Available New Technologies. SPCI, Stockholm, pp. 285–304Google Scholar
  28. Lampela, K., Mikkonen, J., Prantner, M., Leiviskä, K. (1997): Experiences of the fuzzy logics in pulp mill. In: 51st Appita Annual General Conference Proceedings. Appita, Carlton, Victoria, Australia, pp. 527–534Google Scholar
  29. Lampela, K., Kuusisto, L., Leiviskä K. (1996): Dstage bleaching control with fuzzy logic. Tappi 79 (4), 93–97Google Scholar
  30. Lanouette, R., Thibault, J., Valede, J.L. (1997): Improvement of Modelling D-optimal design data with neural networks. In: 83rd Annual Meeting Preprints. Technical Section CPPA, Montreal, Al-7Google Scholar
  31. Leiviskä, K. (1999a): Control Systems. In: Leiviskä, K. (ed.): Process Control. Papermaking Science and Technology, Book 14. Fapet Oy, Jyväskylä, Finland, pp. 1317Google Scholar
  32. Leiviskä, K. (1999b): Control Methods. In: Leiviskä, K. (ed.): Process Control. Papermaking Science and Technology, Book 14. Fapet Oy, Jyväskylä, Finland, pp. 1947Google Scholar
  33. Leiviskä, K., Huttunen, R., Uronen, P. (1988): Expert Systems in the lime kiln control. In: 1988 Conference on The Use of Computers in Chemical Engineering (CHEMDATA 88) Proceedings 2. EFCE, Gothenburg, p. 379Google Scholar
  34. Malmkvist, K., Verikas, A., Malmkvist, L., Bergman, L. (1996): New method for color measurements in multicolor newspaper pictures and its use for printing control. In: International Printing and Graphic Arts Conference Proceedings. Tappi Press, Atlanta, pp. 181–186Google Scholar
  35. Mikkonen J., Fuzzy logic dips into active sludge. Pulp Pap. Eur. 1(9), 26–27Google Scholar
  36. Mikkonen, J., Koskinen, J., and Hanhinen, J. (1994): Fuzzy control in the recovery boiler. Paperi ja Puu 76 (5): 5Google Scholar
  37. Miyahishi, T., Shimada, H. (1997): Neural networks in web-break diagnosis of a newsprint paper machine. In: 1997 Engineering and Papermakers Conference Proceedings. Tappi Press, Atlanta, pp. 605–612Google Scholar
  38. Mori, Y., Nishimura, M., Chujima, T., Yokoyama, H., Abe, T., Yasuda, Y., (1997): Paper production scheduling system using an expert system. Tappi J. 80 (11), 121–164Google Scholar
  39. Morin, F., Coderre, A. (1996): Experiences with “in-house” expert systems. In: CPPA Control Systems ‘86 Preprints, Forum on Industry. Technical Section CPPA, Montreal, pp. 221–228.Google Scholar
  40. Morin, F., Coderre, A. (1996): Also Przegl. Papier 53 (4), 231–235Google Scholar
  41. Murtovaara, S., Juuso, E.K., Sutinen, R. (1996): Fuzzy logic edge detection algorithm. In: 1996 IWISP Proceedings. Elsevier Science, Manchester, U.K., p. 423–427Google Scholar
  42. Murtovaara, S., Juuso, E., Sutinen, R.; Leiviskä, K. (1998): Modelling of pulp cooking characteristics by using fuzzy logic and linguistic equation models. In: Proceedings of TOOLMET ‘88 Symposium — Tool Environments and Development Methods for Intelligent Systems. University of Oulu, Oulu, Finland, pp. 90–96Google Scholar
  43. Murtovaara, S., Sutinen, R., Leiviskä, K. (1998): Neural networks modelling of pulp digester. In: Dourado, A. (ed.): Proceedings of CONTROLO’98, 3rd Portuguese Conference on Automatic Control. APCA, Portugal, pp. 627–630Google Scholar
  44. Musavi, M.T., Smith, G., Li, J.X., Domnisoru, C. (1999): Neural network for modelling paper optical properties. In: Dagli, C. (ed.): Intelligent Engineering Systems through Artificial Neural Networks vol. 9. ASME Press, 1177–1182Google Scholar
  45. Myllyneva, J., Kortelainen, J., Sutinen, R., Leiviskä, K. (1999): Fuzzy Quality Control of TMP Refiner Line. In: 6th International Conference on New Available Technologies. SPCI, Stockholm, pp. 280–283Google Scholar
  46. Nakatani, H. (1995): Optimized combustion control system for chemical recovery boilers. In: 28° Congresso Anual de Celulose e Papel. ABTCP, Sao Paolo, pp. 983–992Google Scholar
  47. Nault, G., Maltais, D. (1997): Application of an on-line expert system to optimize dryers and press section at Domtar. Pulp Pap. Canada 98 (12), 111–113Google Scholar
  48. Nelson, D. (1997): Energy management optimizations using multivariable fuzzy control. In: Engineering and Papermakers: Forming Bonds for Better Papermaking Conference. Tappi Press, Atlanta, 319–322Google Scholar
  49. Nilsson, L., Langkjaer, T. F. (1996): High level control of a causticizing process. In: Zimmermann,H.-J. (ed.): Proceedings of EUFIT’96 — Fourth European Congress on Intelligent Techniques and Soft Computing. Verlag Mainz, Aachen, pp. 1901–1905Google Scholar
  50. Nilsson, L., Dobel, M. (1998): High-level control of the lime kiln and the causticizing process. In: 1998 International Chemical Recovery Conference Proceedings. TAPPI Press, Atlanta, pp. 537–546Google Scholar
  51. Nyborg, L., Crowell, B. (1997): Computer modelling of paper and paperboard for design of expert systems. In: 1997 Technical Conference, Pacific Paper Expo Proceedings, pp. 97–100Google Scholar
  52. Ollila, M. (1997): Online Kappa measurement and control. Pap. S. Afr. 17 (3), 19–34Google Scholar
  53. Ostergaard, J. -J. (1993): Fuzzy control of cement kilns a retrospective summary. In: Zimmermann, H.-J. (ed.): Proceedings of EUFIT’93 — First European Congress on Intelligent Techniques and Soft Computing. Verlag Mainz, Aachen, pp. 552–562Google Scholar
  54. Oyama, Y., Matumoto, T., Ogino, K. (1997): Pattern-based fault classification system for the web-inspection process using a neural network. In: 1997 Japan TAPPI Annual Meeting Proceedings. Japan TAPPI, pp. 575–580.Google Scholar
  55. Oyama, Y., Matumoto, T., Ogino, K. (1997): Abstract in ABIPST 68 (10), 1120Google Scholar
  56. Ozaki, N., Yamazu, S., Tateoka, K. (1994): Recovery boiler intelligent control. In: Control Systems Proceedings. SPCI, Stockholm, pp. 307–312Google Scholar
  57. Powell R.E. (1996): Software for process analysis. In: 1996 TAPPI Engineering Conference Proceedings. Tappi Press, Atlanta, pp. 617–620Google Scholar
  58. Pringle, J.B. (1992): Application of automated vibration signature analysis — concerns and benefits. In: Proceedings Pulp and Paper Maintenance ‘82, Paper no. 22, 16 pGoogle Scholar
  59. Pulkkinen, M., Saastamoinen, M., Skyttä, M. (1997): Neuronet and fuzzy logic control oxygen-delignification. Pap. Puu 136 (3), 152–154.Google Scholar
  60. Pulkkinen, M., Saastamoinen, M., Skyttä, M. (1997): Also Pulp Pap. Eur. 2(10), 30. 32Google Scholar
  61. Ribeiro, B., Dourado, A., and Costa, E. (1993): Lime kiln process identification and control: A neural network approach. In: International Conference on Artificial Neural Networks and Genetic Algorithms Proceedings. Springer, Berlin Heidelberg New York, pp. 117Google Scholar
  62. Rudd, J. B. (1991): Using a neural network system for advanced process control. Tappi J. 74 (10): 153–159Google Scholar
  63. Rudd, J. (1995): Prediction and control of paper machine parameters using neural network models. In: 28° Congresso Annual de Celulose e Papel. ABTCP, Sao Paolo, pp. 281–286Google Scholar
  64. Samad, T., Gorinevsky, D., Heaven, M., Green, A., Su, T. (1998): Neural network technology for tuning process control systems. In: 1998 Conference on Control Systems Proceedings. Finnish Society of Automation, Helsinki, pp. 195–202Google Scholar
  65. Sasaki, T. (1996): Position correspondence identification algorithm in basis weight profile control. In: XIV IMEKO World Congress Proceedings: New measurements — Challenges and Visions. Finnish Society of Automation, Helsinki, Volume XB, pp. 139–144Google Scholar
  66. Scharcanski, J., Dodson, C.T.J. (1995): Simulating paper forming. In: 1995 International Paper Physics Conference Proceedings. CPPA Technical Section, Montreal, pp. 87–89Google Scholar
  67. Smith, D. B., Edwards, L. L., and Damon, R. A. (1991): Development and application of a real-time recovery boiler expert system. Tappi J. 74 (11): 93–96Google Scholar
  68. Sotelo, Junior J., Porchia, L.F.S., Plaut, R.L. (1996): Press simulator used in the development of expert diagnostic system for predictive maintenance. Papel 57(8), 36, 62–64Google Scholar
  69. Stapley, C., Butner, R., Kangas, M., Broeren, L., Smith, B. (1995): Tool for strategic analysis. In: 28° Congresso Annual de Celulose e Papel. ABTCP, Sao Paolo, pp. 331–341Google Scholar
  70. Suojärvi M. (1997): Neural network for estimation of quality. In: XIV IMEKO World Congress Proceedings: New measurements — Challenges and Visions. Finnish Society of Automation, Helsinki, 5 pGoogle Scholar
  71. Wilks, R. (1997): Production rate optimization, chemiwasher expert system. In: 51st Appita Annual General Conference Proceedings. Appita, Carlton, Victoria, Australia, pp. 535–540Google Scholar
  72. Woollard, R., Jury, J., Clark, C. (1996): Expert system advises operators on Process-Control Conditions. Tappi J., 79 (4), 137–141Google Scholar
  73. Xia, Q., Rao, M., Farzadeh, H., Danielson, K., Henriksson, C., Olofsson, J. (1993): Integrated intelligent control system for peroxide bleaching processes. In: IEEE Conference on Control Applications Proceedings. IEEE, Piscataway, NJ, pp. 593–598Google Scholar
  74. Yeager, B. (1997): Fuzzy controls yield clear results at Finnish pulp mill. Pulp Pap. Int. 58(3), 58, 60–61Google Scholar
  75. Zhang, D., Sun, Q., Wadsworth, L. (1997): Model for evaluation of quality of nonwovens and polymeric composites. In: Nonwovens Conference Proceedings. Tappi Press, Atlanta, pp. 223–228Google Scholar
  76. Zhu, J.B., Xia, Q.J., Rao, M., Zurcher J. (1997): Neural Network Modelling Pulp Processes. Pulp Pap. Canada 98 (9), 35–38Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Kauko Leiviskä
    • 1
  1. 1.Control Engineering LaboratoryUniversity of Oulu, Oulun yliopistoOuluFinland

Personalised recommendations