Einsatz neuronaler Netze zur Optimierung der Prozeßführung bei der Blasstrahlerzeugung

Basic Oxygen Steelmaking (BOS) is a current technology of refining iron to steel. Efficiency and quality depend on exact calculation of actual and estimated process parameters. Artificial neural networks (ANN) provide the facility of improving accuracy of process control. Within this thesis, a conception for the use of artificial neural networks for process control systems of BOS converters is presented. The study aims for development of universal ANN modules to enhance existing process models on the basis of process data. Conventional process models are used as a substructure to include process knowledge that is already available. The ANN structures are applied to predict the error of the conventional process model and hence improve the performance of the process control system. The study includes theoretical investigations to achieve methodical structure of ANN modules and to make sure transferability of this technique for similar applications. Key figures are determined to benchmark quality and stability of the control system is analysed. A practical example for this technique is given based on existing process control models and steel-plant data sets.

Grantor dc:publisher

Publikationsserver der RWTH Aachen University

Year dc:date

2000

Author dc:creator

• Poschmann, Michael

Contributors dc:contributor

• Seeliger, Andreas

• info:eu-repo/classification/ddc/660
• Technische Chemie
• Sauerstoffaufblasverfahren
• Prozessführung
• Konverter
• Prozesssteuerung
• Neuronales Netz

Statement dc:rights

• info:eu-repo/semantics/openAccess

Language dc:language

ger

Identifier

https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-122986%22

OAI identifier oai:identifier

oai:publications.rwth-aachen.de:61312