Supervision and Control for Industrial Processes: Using Grey Box Models, Predictive Control and Fault Detection Methods

ISBN-13: 9781447115601 / Angielski / Miękka / 2011 / 230 str.

Bjorn Sohlberg

Opis

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. Bjom 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.

Spis treści

1 Background.- 1.1 Introduction.- 1.2 Rinsing process.- 1.3 Outline of the chapters.- 2 Grey Box Modelling.- 2.1 Introduction.- 2.2 Background for grey box modelling.- 2.3 Model construction procedure.- 2.3.1 Basic modelling.- 2.3.2 Experiment.- 2.3.3 Identification.- 2.3.4 Model analysis.- 2.3.5 Expanded modelling.- 2.3.6 Model appraisal.- 2.4 Software tools.- 2.4.1 CYPROS software.- 2.4.2 MoCaVa/IdKit software.- 2.5 Summary.- 3 Application of Basic Modelling.- 3.1 Introduction.- 3.2 Basic modelling of the rinsing process.- 3.2.1 Process description.- 3.2.2 Process structure.- 3.2.3 Process hypotheses.- 3.2.4 Basic equations.- 3.2.5 State space model.- 3.2.6 Model preparation.- 3.2.7 General form.- 3.3 Summary.- 4 Application of Augmented Modelling.- 4.1 Introduction.- 4.2 Experiment.- 4.3 Searching for an appropriate model.- 4.3.1 Basic model M1.- 4.3.2 Expanded model M121.- 4.3.3 Expanded model M122.- 4.3.4 Expanded model M131.- 4.3.5 Expanded model M132.- 4.3.6 Expanded model M133.- 4.3.7 Expanded model M134.- 4.3.8 Expanded model M141.- 4.3.9 Expanded model M142.- 4.3.10 Expanded model M143.- 4.3.11 Cross validation.- 4.4 Modelling aspects.- 4.5 Alternative tank models.- 4.6 Summary.- 5 Real-Time Estimation.- 5.1 Introduction.- 5.2 Data acquisition.- 5.3 Off-line identification.- 5.4 On-line estimation.- 5.5 Summary.- 6 Process Control.- 6.1 Introduction.- 6.2 Combined feedforward and feedback controller.- 6.3 Application of the combined controller.- 6.3.1 Adaptation to the rinsing process.- 6.3.2 Simulation of the control system.- 6.3.3 Control of the process.- 6.4 Non-linear optimal controller.- 6.5 Application of the optimal controller.- 6.5.1 Adaptation to the rinsing process.- 6.5.2 Simulation of the control system.- 6.5.3 Control of the process.- 6.6 Summary.- 7 Process Supervision.- 7.1 Introduction.- 7.2 Terminology.- 7.3 Supervision methods.- 7.3.1 On-line system.- 7.3.2 Process models.- 7.3.3 On-line estimation.- 7.3.4 Preventive maintenance.- 7.4 Application of supervision.- 7.4.1 System design.- 7.4.2 Decision basis for replacing worn parts.- 7.4.3 Useful life of the squeezer rolls.- 7.4.4 Economic aspects of changing the squeezer rolls.- 7.4.5 Evaluation of process components.- 7.5 Summary.- 8 Fault Detection, Diagnosis & Identification.- 8.1 Introduction.- 8.2 Fault-related methods.- 8.2.1 Fault detection.- 8.2.2 Fault diagnosis.- 8.2.3 Fault identification.- 8.3 Application of fault-related methods.- 8.4 Summary.- References.