Model-based optimisation for efficient use of resources and energy.
Working points in the project
The project started at October 1st 2017 and ends at September 30st 2021. Involved partners in the research are:
|GRIPS Industrial IT Solutions GmbH|
|OPTIMIZACION ORIENTADA A LA SOSTENIBILIDAD SL|
|Maschinenfabrik Liezen und Gießerei GmbH|
|OUTOKUMPU STAINLESS OY|
|SSAB EUROPE OY|
|Teknologian tutkimuskeskus VTT OY|
Model-based offline and real-time optimisation tools for the whole process route to increase overall energy and resource efficiency as well as product quality in production of high-strength carbon steels, stainless steels and cast steels.
The research has received funding from the European Commission, funding reference Horizon 2020 (H2020) / SPIRE-07-2017 / 768652.
Cyber-Physical Production Optimization Systems for Long Production Factories
Cyber-POS project abstract and objectives
Production technology in steel industry has reached a level that significant improvements can only be reached by through process optimization strategies instead of improving each process step separately. Therefore the connection of suitable technological models to describe process and product behavior, methods to find solutions for typical multi-criterial decisions and a strong communication between involved plants becomes mandatory. Cyber-POS will develop a virtual simulation platform for the design of cyber-physical production optimization systems (CPPS) for long production facilities with special emphasis to thermal evolution and related material quality, leading to reduced energy consumption, shortened production time and improved product quality.
The main objectives within this project are:
This is achieved by applying the developed software and methods for the specific use cases. This implies the following sub-objectives:
The project started at July 1st 2016 and ends at December 31th 2019. Involved partners in the research are:
|Arcelor Mittal España|
|Scuola Superiore Sant’Anna di Studi Universitari e di Perfezionamento|
Cyber-POS project expected industrial impacts
The research has received funding from the European Union’s Research fund for Coal and Steel (RFCS) under grant agreement No. 709669.
The main motivation for introducing the methods of cyber-physical production optimization is essentially to preserve the economic performance and safety level in spite of faults and process changes that may occur over time. The CPS platform to be developed can be regarded as an assistance system that will support plant personnel/operator decisions, and thus can contribute to the improvement of working conditions. All involved processes can actively communicate with each other, know their field of activity and production conditions. The optimizations made are also more tailored to the human workforce.
Higher maintainability, reliability and efficiency of long production factories through cyber-physical production optimization will lead to improved product quality, reduced maintenance costs and decreased material and energy consumption. This will have a positive impact on preservation of natural resources, energy and environment. Needless to say, reducing energy consumption leads to reduced CO2 emissions.
At the two involved plants of ArcelorMittal España and Mannstaedt, the developed concept will be installed as assistance system and tested in the process route reheating, hot rolling and cooling. This leads to increased flexibility of process chain, higher productivity, better disturbance management and energy savings.
Real Time Monitoring of coal composition in closed systems for fast process control
RemoCoal project abstract
Nowadays for competitive hot metal production at a Blast furnace it is necessary to realize a high pulverised coal (PC) injection rate at a minimised coke rate. But actually there is a lack of real time analytical technologies to obtain reliable short time information of the actual composition of pulverized coal blend which is injected into the blast furnace (BF). A real time analysis of the pulverised coal blend opens up the opportunity to detect unexpected or prompt deviation in coal blend composition. It enables to run an optimized total BF fuel rate, a reduction of fuel cost for hot metal production and subsequently decreasing CO2 emissions.
Within the project the Pulsed Fast and Thermal Neutron Analysis (PFTNA) technology designed as borehole logging tool will be modified and applied in a closed silo of a pulverized coal injection (PCI) system. BF trials for real-time analysis of coal composition to adjust the coal injection rate near time will be performed.
The PFTNA-technique takes advantage of a switchable pulsed neutron generator for emission of fast and thermal neutrons and a gamma ray detector which record the characteristic gamma photons generated by interaction of the emitted neutrons with the nuclei of the surrounding coal material. It delivers characteristic spectra as basis for determination of the PC composition.
Additional the real time spectra will be processed by fingerprint analysis software (pattern recognition). The analysis is based on statistical data evaluation of the gamma spectra and delivers the operator principle parameter of the PC for Blast furnace optimization.
The involved Partners in the research project are:
|VDEh – Betriebsforschungsinstitut GmbH|
|Malvern Panalytical GmbH|
|thyssenkrupp Steel Europe AG|
This project receives funding from the Research Fund for Coal and Steel under grant agreement No. 754 200
The main objective of this project is to demonstrate the high benefit of the real time analysis of coal composition for industrial application in better adjusting/controlling the pulverised coal injection rate and improve the production process.
The implementation of the PFTNA technology and the finger print analysis software enables to:
The new technology shall be demonstrated at the pulverized coal injection plant of a BF at tk SE. Finally, a concept will be set up for the implementation of the PFTNA technology and the real-time data evaluation at European blast furnaces.
Remocoal research approach
The development of the online monitoring systems in RemoCoal will be achieved in four major steps. An overview of the concept is illustrated in the following figure:
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:
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:
|Centre for Research in Metallurgy|
|Feralpi Siderurgica S.p.A.|
|Centro Sviluppo Materiali|
|Peiner Träger GmbH|
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.