Due to the complexity of the process operation and the requirements for high quality, low cost, safety and the protection of the environment, an increasing number of pulp and paper companies are in need of an advanced control technology to improve their process operation.
This publication presents, for the first time, the theory of such an advanced control technology as well as various industrial applications associated especially with Paper Making. The reader will gain a better understanding of the most popular and advanced process control techniques and applications of these techniques in an important real-time process industry. The contents are based on the authors' own research on modeling and advanced control in this field.

Inhalt
1 Background.- 1.1 Paper Making: Process Fundamentals.- 1.2 Paper Machine Control Problems.- 1.3 References.- 2 Process Dynamics and Modeling.- 2.1 Introduction.- 2.2 Pressurized Headboxes.- 2.3 Open Headbox.- 2.4 Wire and Press.- 2.5 Drying Section.- 2.6 Model Accuracy Test and Conclusions.- 2.7 References.- 3 Robust Control.- 3.1 Introduction.- 3.2 Multi-model Robust Control.- 3.3 Conclusions.- 3.4 References.- 4 Predictive Control.- 4.1 Adaptive Fading Kalman Filter.- 4.2 Adaptive Predictive Control.- 4.3 Model Algorithmic Control.- 4.4 Conclusions.- 4.5 References.- 5 Bilinear Control.- 5.1 Introduction.- 5.2 Bilinear Decoupling Control.- 5.3 Bilinear State Observers.- 5.4 Bilinear Suboptimal Control.- 5.5 Conclusions.- 5.6 References.- 6 Fault-Tolerant Control.- 6.1 Introduction.- 6.2 Fault-tolerant Control of Headboxes.- 6.3 Fault-tolerant Control of Drying Section.- 6.4 Conclusions.- 6.5 References.- 7 Fuzzy Control.- 7.1 Fuzzy Optimal Control.- 7.2 Fuzzy-Precise Combined Control.- 7.3 Conclusions.- 7.4 References.- 8 Expert Systems.- 8.1 Introduction to Expert Systems.- 8.2 IDIS for Process Control System Design.- 8.3 Application to Headbox Control System Design.- 8.4 Conclusions.- 8.5 References.- 9 Modeling via Artificial Neural Network.- 9.1 Introduction.- 9.2 Fundamentals of Artificial Neural Network.- 9.3 Backpropagation Learning Paradigm.- 9.4 Application to Paper Machine.- 9.5 Conclusions.- 9.6 References.- 10 IOMCS for Pulp and Paper Processes.- 10.1 Introduction.- 10.2 System design and implementation.- 10.3 Conclusions.- 10.4 References.