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
Genetic Algorithms Genetic algorithms (GA) are optimisation methods mimicking the natural evolution. The population consisting of chromosomes improves towards a better solution (not necessarily the optimal one), as the result of different selection, crossover and mutation operations. Figure 1 illustrates the main principles of Genetic Algorithms. Chromosomes are the main components in Genetic Algorithms
Authors & references
Authors:
Professor Emeritus Kauko Leiviskä, University of Oulu
References:
- Goldberg, D.E. 1989. Genetic algorithm in search, optimization and machine learning, New York Addison-Wesley. 432 p.
- Mitchell, M. 1998. Introduction to genetic algorithms. The MIT Press, 221 p.
Videos
Exercises
This page has been updated 15.11.2020