Avatar ByDr. Martin Schlautmann



Continuous Performance Monitoring and Calibration of Model and Control Functions for Liquid Steelmaking Processes

PerMonLiSt project objectives

The main objective of the research project is to improve, for the different stages of the liquid steelmaking process route, the continuous monitoring of the process performance as well as to ensure the permanent reliability of used dynamic process models and control rules. For this purpose, methods and tools will be developed involving the application of innovative and comprehensive performance indexes and strategies for automatic calibration of model and control parameters.

By these developments the following benefits shall be achieved for the liquid steelmaking processes:

  • Improved on-line monitoring of the process performances, to be used by engineers and operators to decide about necessary countermeasures. Moreover, the increased knowledge about the process behaviour can be used to improve the operating practices.
  • Long-term reliable operation of dynamic process models and rule based set-point calculations used for off-line process optimisation as well as on-line monitoring and process control, by continuous monitoring of model and control performance with automatic adaptation of related parameters – e.g. by least-squares-fitting, Kalman filter and machine learning approaches.
  • Improved reliability and stability of the liquid steelmaking processes by enhanced performance of model- and rule-based control of analysis and temperature of the steel melt with reduced scatter and deviations from the desired target values.
  • Minimisation of energy and resources consumption as well as treatment duration by enhanced reliability of level-2 automation and process control functions.

The developed tools will be coupled to an integrated approach and tested exemplarily for the most important liquid steelmaking facilities of the electric steelmaking route, i.e. for EAF, LF, VD and AS plants.

The project started at July 1st 2016 and ends at December 31st 2019. Involved partners in the research are:

VDEH-Betriebsforschungsinsitut GmbH
Centre for Research in Metallurgy
Feralpi Siderurgica S.p.A.
Centro Sviluppo Materiali
Peiner Träger GmbH
Horizon 2020

The research has received funding from the European Union’s Research fund for Coal and Steel (RFCS) under grant agreement No. RFSR-CT-2016-709620.

PerMonLiSt achieved results

The available process models and required process data have been described and assessed regarding current accuracies for the EAF and secondary metallurgical ladle treatment processes at PTG, Feralpi/Lonato and Tata/Aldwarke, respectively. The related data acquisition and model functions have been completed where necessary.

Process and model performance indexes have been defined for assessment of process behaviour and related model calculations in electric steelmaking processes. The analysed correlations between process performance indexes and operating practices show different significances. The most significant correlation is given between metallic yield and specific oxygen consumption in EAF. The relation of specific energy consumption decreasing with increasing productivity in EAF depends on the characteristics of the furnace and its operation. The desulphurisation efficiency in ladle treatment shows positive correlation with the volume of applied stirring gas. Analysed correlations between model and process performance indexes reveal systematic errors of the respective model for certain ranges of process operation.

Regular ranges for defined process performance indices have been defined which shall be used within enhanced monitoring and alert functions. At Feralpi Lonato steel plant the newly installed on-line system already provides first enhanced monitoring functions regarding process behaviour and performances. At PTG steel plant suitable operating practices have been defined within the existing manufacturing execution system and model based dynamic adaptions of selected set-points of operating practices have been assessed for the ladle treatment process.

A least-squares-fit approach has been implemented and used for automatic off-line calibration of EAF model parameters of the furnaces at PTG steel plant. A concept for use of a Kalman filter method for parameter estimation of the EAF model of CRM has been proven within first tests. The identifiability of parameters of the ladle treatment model developed by BFI has been proven. Thus, the Kalman filter method can be applied for on-line estimation of these parameters. Furthermore, a concept for a machine learning system to be used for auto-calibration of operating practices at Feralpi Lonato site has been set up and first steps of realisation have been carried out.

About the author


Dr. Martin Schlautmann editor