Neural networks in the production optimization of a kraft pulp bleach plant
1University of Oulu, Faculty of Technology, Department of Process and Environmental Engineering
|Online Access:||PDF Full Text (PDF, 2.3 MB)|
|Persistent link:|| http://urn.fi/urn:isbn:9789514285691
|Publish Date:|| 2007-10-02
|Thesis type:||Doctoral Dissertation
|Defence Note:||Academic dissertation to be presented, with the assent of the Faculty of Technology of the University of Oulu, for public defence in Kuusamonsali (Auditorium YB210), Linnanmaa, on October 12th, 2007, at 12 noon
Professor Raimo Alén
Professor Olli Dahl
Bleaching is an essential process in chemical pulp production for better pulp brightness and longer life expectancy. However, it causes costs such as chemicals, energy, equipment, and loss of yield. Non-linear reactions and several process variables, with interactions, make large plants complicated to model and optimize. As an expensive process bleaching has been a natural target of optimization, but there is still the need to either improve these methods or consider the optimization problem from a new point of view.
The aim of this thesis was to develop production optimization methods for pulp bleaching, so that they are practical, usable on-line, easy to tune, and transferable. According to our assumption, neural networks could provide a practical optimization method by combining analytical knowledge with real data. In this kind of problem, the load sharing concept, recognizing interactions in chemical usage and the serial multi-stage nature of the process can simplify the task.
The related work in bleaching optimization was studied as well as multi-stage serial process solving in principle, related optimization methods and especially neural networks in optimization. The data were collected during normal mill operation and modeled using neural networks. Optimization was performed based on visualizing the neural network models.
The results showed that backpropagation neural networks are capable of modeling parts of the bleach plant and also the entire bleaching operation to such an extent that they are useful in the optimization. The modeling and the tuning can be performed without a profound knowledge of the system, but the process is slower and less reliable. Moving a trained neural network to another mill is inadvisable. It is more reasonable just to transfer the knowledge of variables and network structure. The important factor in on-line production optimization is the stabilization of the disturbances and a well-controlled operation towards a more economical state. Generally, more than half of the total chemicals should be used in the first bleaching stage D0 and the remaining load should be divided so that the dosage at the D1 is about 30% higher than in the D2 stage.
Acta Universitatis Ouluensis. C, Technica
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