Process control and automation
- Introduction to process control and automation
- Development of process automation
- Fibre process automation
- Chemical recovery as a control object
- Advances in paper machine automation
- Paper machine as a dynamical system
- Tasks in paper machine control and management
- Control of stock flow concentration and quality
- Machine direction control
- Cross-directional control – The static optimisation
- Cross-directional control – Dynamics
- Cross-directional control – further aspects
- Controlling functional paper properties
- Managing grade chances in the paper machine
- Managing disturbances caused by broke and recovered solids
- Millwide systems
- Modelling and control methods
Artificial neural networks The crucial step in the research on neural networks was McCulloch’s and Pitts’ computational model in 1943 1. This model was used both in the research on biological brain processes and on artificial neural networks (ANN). Hebbian learning, which is a form of unsupervised learning, was the next step – also during
Authors & references
Authors:
Professor Emeritus Kauko Leiviskä, University of Oulu
References:
- McCulloch, W. and Pitts, W. 1943. A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5(4):115–133.
- Hebb, D. 1949. The Organization of Behavior. New York: Wiley.
- Werbos, P. 1974. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvard University.
- Werbos, P. 1990. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE 78(10):1550 – 1560.
- Rajesh, K. and Ray, A.K. 2006. Artificial neural network for solving paper industry problems: A review. Journal of Scientific & Industrial Research 65(7):565—573.
- Haataja, K., Leiviskä, K. and Sutinen R. 1997. Kappa-number estimation with neural networks.1997 IMEKO World Congress Proceedings, Finnish Society of Automation, vol. XA, Tampere, p. 1–5.
- Leiviskä, K. 2006. Kappa number prediction with neural networks. Proceedings, Control Systems 2006, June 6–8, Tampere.
- Aguiar, H.C. and Filho, R.M. 2001. Neural network and hybrid model: a discussion about different modeling techniques to predict pulping degree with industrial data. Chemical Engineering Science 56(2):565-570
- Dayal, B.S., MacGregor, J.F., Taylor, P.A., Kildaw, R. and Marcikic, S. 1994. Application of feedforward neural networks and partial least squares regression for modelling Kappa number in a continuous Kamyr digester. Pulp & Paper Canada 95(1):26-32.
- Correia, F.M., d’Angelo, J.V., Almeida, G.M., and Mingoti. S.A. 2018. Predicting Kappa number in a Kraft pulp continuous digester: a comparison of forecasting methods. Brazilian Journal of Chemical Engineering 35(03):1081-1094.
- Keski-Säntti, J., Leiviskä, K. and Lampela, K. 1999. Production optimization of the pulp bleach plant – intelligent methods utilization approach. Conference: 6th International Conference on New Available Technologies, The World Pulp and Paper Week, Stockholm, Sweden, Proceedings, pp. 402-408.
- Ciesielski, K. and Olejnik, K. 2014. Application of Neural Networks for Estimation of Paper Properties Based on Refined Pulp Properties. Fibres & Textiles in Eastern Europe 2014; 22(5)(107):126-132.
- Gornik, M., Novak, G. and Govekar, E. 1997 Modelling coated paper properties: application of neural networks, International Journal of Systems Science 28)9):865-870.
- Ribeiro B., Dourado, A. and Costa, E. 1993. Lime Kiln Process Identification and Control: A Neural Network Approach. In: Albrecht R.F., Reeves C.R., Steele N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna
- Ribeiro, B., Dourado, A. and Costa, E. 1995. Lime Kiln Fault Detection and Diagnosis by Neural Networks. ICANNGA’95, International Conference on Artificial Neural Networks and Genetic Algorithms Volume 1.
- Järvensivu, M. and Seaworth, B. 1998. Neural Network Models Used for Quality Prediction and Control. IFAC Proceedings Volumes 31(29):179-184.
- Juneja, P.K., Ray, A.K. and Mitra, R. 2010. Fuzzy Control and Neural Network Control of Limekiln Process. International Journal of Electronics Engineering 2(2):305 – 306.
- Qian, Y and Tessier, P. 1995. Modelling of a woodchip refiner using artificial neural network. Chemical Engineering Technology 18(5):337-342.
- Sui, O. S., Sanche, L., Mills, C., Smith, W. and Douglas, T. 1998. Model Based Pulp Quality Control of TMP Refiner, 1998 TAPPI Pulping Conference Proceedings.
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This page has been updated 15.11.2020