© 1998

Supervision and Control for Industrial Processes

Using Grey Box Models, Predictive Control and Fault Detection Methods


Part of the Advances in Industrial Control book series (AIC)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Björn Sohlberg
    Pages 1-6
  3. Björn Sohlberg
    Pages 7-43
  4. Björn Sohlberg
    Pages 45-72
  5. Björn Sohlberg
    Pages 73-104
  6. Björn Sohlberg
    Pages 105-122
  7. Björn Sohlberg
    Pages 123-168
  8. Björn Sohlberg
    Pages 169-202
  9. Björn Sohlberg
    Pages 203-218
  10. Back Matter
    Pages 219-230

About this book


The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The steel industry world-wide is highly competitive and there is significant research in progress to ensure competitive success prevails in the various companies. From an engineering viewpoint, this means the use of increasingly sophisticated techniques and state-of-the-art theory to optimise process throughput and deliver ever more exacting dimensional and material property specifications. Dr. Bjöm Sohlberg's monograph demonstrates this interplay between fundamental control engineering science and the demands of a particular applications project in the steel strip production business. It is an excellent piece of work which clearly shows how these industrial engineering challenges can be formulated and solved.


control fault detection identification industrial process modeling optimal control process control software

Authors and affiliations

  1. 1.Dalarna University CollegeBorlängeSweden

Bibliographic information