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Welcome
Themes
Introduction to forest-based bioeconomy
Wood products
Natural fibre products
Man-made bio-based fibre products
Bio-based nanomaterials
Recycled fibre
Pulping and biorefining
Energy and biofuels
Biomass chemistry and physiology
Material testing and product properties
Forests and other biomass resources
Supply chain
Process control and automation
Asset management
Business and investment planning
Environmental control and management
Learning paths
Learning path A
Learning path B
Other resources
Papermaking Science and Technology books
Podcasts
Webinars
Tools
How to use
Dictionary
Glossary
Contact
Contact us
Contributors
Profile
Introduction to process control and automation
After reading this article you will..
have a basic idea what to automate.
understand the benefits of automation.
have a basic idea about automation functions.
Content
Process control and automation
Introduction to process control and automation
Development of process
automation
Trends in today´s automation
IoT, CC and EC – What are these?
Big Data and Big Data analytics
Artificial Intelligence – yesterday and today
How Internet has changed the control hierarchy
Fibre process
automation
Wood handling
Control requirements in wood handling
Wood handling measurements
Wood handling control
Refiner control
Basic controls for TMP refiners
Quality control of TMP refiners
New concept for refining control
Pulp quality estimation using the soft sensor concept
Grinding control
Grinder control functions
Grinding control strategies
Continuous flow digester control
Basic control functions in continuous digesters
Quality control in continuous digesters
Transition control in continuous digesters
Batch digester control
Basic control functions in batch digersters
Kappa number control in batch digesters
Cooking plant scheduling
Brown stock washing control
Models and calculations
Control functions
Bleach plant control
Requirements for bleaching control
Optimisation in pulp bleaching
Oxygen delignification
Stagewise controls
Control of mechanical pulp bleaching
Chemical recovery as a control
object
Black liquor evaporation control
Black liquor measurements
Black liquor evaporation – Fouling
Black liquor evaporation – Modelling
Recovery boiler control
Recovery boiler — Control functions
Black liquor feed control
Temperature & char bed control
Combustion air controls
Dissolving tank control
Sootblowing control
Causticising control
Causticising control – functions
Lime mud filter control
Lime mud filter – Control functions
Lime kiln control
Lime kiln control – Control Functions
Lime kiln control – Intelligent control
Advances in paper machine
automation
Paper machine as a dynamical system
State of the papermaking processes and its measurements
Dynamics of paper/board production line
2D variations in web: characterisation and measurement
Full web measurements
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
Monitoring and data integration
Information systems in pulp mills
Information systems in paper mills
Balancing process operation in the fibre line
Production scheduling
Production scheduling in pulp mills
Production scheduling in paper mills
Energy management system
Boiler load allocation
Turbine load allocation
Purchased power optimisation
Steam levelling
Modelling and control
methods
Basic control algorithms
Adaptive control
Gain scheduling
Model reference control
Self-tuning controller
Performance monitoring
Multivariable control
Model Predictive Controller
MPC Algorithms
Fuzzy logic control
What does fuzziness mean?
Fuzzy logic controller
Artificial neural networks
ANN – structures
How do the networks learn?
Perceptron network
Recurrent neural networks
Self-organising maps
Intelligent optimisation
Genetic algorithms
Genetic Algorithm – Example
Particle Swarm Optimisation
Software sensors and sensor fusion
Statistical process control
SPC Basics
Cause- and -effect analysis
Pareto analysis
Control charts
Cusum charts
Capability indices
Principal Component Analysis (PCA)
Principal Component Analysis – Example
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Authors & references
Authors:
Professor Emeritus Kauko Leiviskä, University of Oulu
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This page has been updated 05.11.2020
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