Yearly Archive 2019

Avatar ByKersten Marx

MinSiDeg

Minimise sinter degradation between sinter plant and blast furnace exploiting embedded real-time analytics (MinSiDeg) project abstract

Sinter with high and consistent quality, produced with low costs and emissions is very important for iron production. Transport and storage degrade sinter quality, generating fines and segregation effects.

Conventional sinter quality monitoring is insufficient: Slow and expensive. Consequently, the impact of sinter quality on daily BF operation is extremely intransparent.

In MinSiDeg, new transfer systems and procedures will minimise degradation during transfer to save return fines and stabilise particle size distribution.

New on-line measurements will be established, combined and exploited with Big Data technologies. This break-through in continuous quality monitoring will enable combined optimisation of sinter plant and blast furnace.

Kick-off-Meeting for „Minimise sinter degradation between sinter plant and blast furnace exploiting embedded real-time analytics“ (MinSiDeg) in Linz

MinSiDeg objectives

Major objective of the project MinSiDeg is to clearly decrease costs and environmental impact of sinter plants and blast furnaces. To achieve this, the sinter quality will be optimised along the production chain improving both, sinter plant and blast furnace working.

The following general technical objectives are defined:

  • quantify sinter quality fluctuations (minutes to several hours)
  • intensify the exploitation of data by Big Data methods
  • minimise sinter degradation by material handling
  • make (physical) sinter quality transparent and more stable
  • improve BF shaft permeability

MinSiDeg will realise the objectives by 3 main approaches (cf. Figure 1):

  1. Online monitoring of physical sinter quality by new measurements
  2. New equipment and material handling procedures along the transfer to the blast furnace
  3. Real-time analytics of existing and new data streams for machine supported decisions

                                                                               Figure 1: Main approaches within the MinSiDeg concept.

MinSiDeg research approach

The project work will be organised within 5 technical work packages:

  1. Improve sinter stability
  2. Minimise sinter degradation along transport and storage
  3. New online methods for sinter quality determination
  4. Improve value of sinter for the blast furnace
  5. Real-time machine supported decisions on sinter quality

The involved partners in this research project are

VDEh-Betriebsforschungsinstitut GmbH
thyssenkrupp Steel Europe AG
voestalpine Stahl GmbH
DK Recycling und Roheisen GmbH
K1-MET GmbH
Montanuniversität Leoben
The project leading to this application has received funding from the Research Fund for Coal and Steel under grant agreement No. 847334.

Project duration: 1 July 2019 – 31 December 2022 (42 months)

 

Avatar ByKersten Marx

RealTimeCastSupport

RealTimeCastSupport project abstract

Thermal and fluid-mechanical conditions in continuous casting moulds are only roughly known although highly relevant for the product quality. Manual process control is difficult due to the great number of influencing factors. Therefore, the aim of the research is the digitalisation and optimised control of continuous casting machines. Large data streams will considered online and assist the caster operators with a real-time support system. This system will provide suggestions for an optimised process control in real-time. It will be developed with application of new measuring techniques and representation of the casting machine by a digital twin.

The kick-off-meeting for the RFCS project “Embedded real-time analysis of continuous casting for machine-supported quality optimisation” (RealTimeCastSupport) took place on 1st and 2nd of October 2019 at premises of the coordinator BFI.

VDEh-Betriebsforschungsinstitut GmbH
AG der Dillinger Hüttenwerke
voestalpine Stahl GmbH
Materials Processing Institute (MPI)
Minkon SP ZOO

RealTimeCastSupport objectives

The main objective of the proposed research project is:

  • Improved product quality in terms of reduction of hard spots on heavy plates and slivers on cold-rolled strips.

The main objective is accompanied by several sub-objectives which can be assigned to the already mentioned main components of the research project:

Online monitoring of tundish and mould with implementation of new measuring techniques

  • Simultaneous temperature measurements at different positions in the tundish as well as in the mould and monitoring of the casting powder coverage.
  • Online application of new measurement technologies FOTS and DynTemp® for temporally high resolving temperature.
  • Implementation of IR-based 2D casting powder monitoring.

Exploitation of various CC data and surface inspection to predict reliability of steel production

  • Offline material tracking, synchronisation of data streams and statistical analysis by application of big data technologies.
  • Identification of defect promoting scenarios by correlation of casting powder monitoring, statistical results and hard spot as well as sliver detection.
  • Realisation of an offline 3D digital twin of the CC tundish and mould considering transient steel melt flow including turbulence, filling level changes, heat transfer, inert gas feeding and solidification.
  • Offline reproduction of the identified defect promoting scenarios with the 3D digital twin in order to find thermal and fluid mechanical reasons for the detected behaviour.

Advanced CC process control in real-time offering machine supported decisions

  • Development of countermeasures against the defect promoting scenarios aiming at the adjustment of the thermal and fluid-mechanical caster status in order to strengthen the options for real-time process control. Assessment of their potential with the digital twin.
  • Adjustment of operational windows for continuous caster operation aiming at an advanced process control.
  • Development and testing of new mould powders and intumescent coatings aiming at modification and improved control of heat transfer in the mould.
  • Modification of electromagnetic actuator’s operation mode.
  • Offline identification of rules for the operation of the casting machine based on conclusions from measurements, statistical analysis and application of the 3D digital twin.
  • Online application of a real-time support system with implementation of the defined rules.
  • Online implementation of advanced real-time CC process considering large data streams.
  • Verification of the effectivity of real-time support system during operational application.

RealTimeCastSupport research approach

Online monitoring of tundish and mould with implementation of new measuring techniques

Available measurement techniques

An important research approach of this project is the simultaneous temperature measurements at different positions in tundish as well as in the mould and the monitoring of the casting powder coverage. This will provide a deeper insight of the conditions in the casting machine depending on time, i.e. transient conditions like ladle or tundish changes can be analysed in detail. The results can then be connected to quality information, i.e. hard spots appearance on heavy plates as well as sliver appearance on cold-rolled strips. The online application of the new measurement technologies FOTS and DynTemp® for temporally high resolving temperature is scheduled as well as the implementation of IR-based 2D casting powder monitoring system. The figure below illustrates the availability and position of the utilised measuring techniques.

Additionally, already available measurements, analysis and online modelling results systems will be utilised for the real-time machine support system:

  • Melt temperature in the ladle.
  • Temperature in the copper mould plates measured with thermocouples.
  • Sliver detection on the cold-rolled strips.

Exploitation of various CC data and surface inspection to predict reliability of steel production

A self-evident element of this project component is the material tracking and the synchronisation of the available data streams. It has to be ensured that the quality information, i.e. hard spots and sliver occurrence, can be assigned to the corresponding casting conditions. But the casting conditions are not only valid for a certain time. They were taken at different positions, i.e. measurements with regard to the determined product quality have to be taken at different times, e.g. melt temperature in the tundish and in the mould, casting powder cover and copper plate temperatures. They have to be synchronised knowing well that different techniques show different idleness, e.g. temperature measurements in the copper plates react slower on melt temperature changes than the DynTemp® measurements. Material tracking algorithms are already available at the steel plants of the industrial partners. They will be used in the frame of the research project. Synchronisation of the measured data will be worked out in the frame of the comprehensive statistical analysis.

For the analysis and assessment of the mentioned data different methods from Data Mining and Big Data analytics will be used. For the computations with the 3D digital twin the analysis of influencing factors of casting is necessary in order to find the target parameter, e.g. the occurrence of hard spots. Therefore, a common analysis of the casting parameters, i.e. the various temperature measurements in tundish and mould and the results of image processing, will be executed by means of Data Mining methods in a first step. Several methods like Decision Tree analysis, artificial neural networks, e.g. Self Organising Map or Deep Learning methods, and others will be applied to detect relationships between the input parameters and the target one. The aim is to identify those inputs – or derived features- which are influencing mainly the target parameter. By the derived subset of input values a digital twin of the casting machine, i.e. a transient CFD model of the considered casting machine, will be developed in order to estimate the impact of altered parameters on product quality features. The findings will be integrated in the real-time support system by the definition of a set of rules describing possible countermeasures. The real-time support system will provide information about possible critical process conditions causing defects and will support operators to find appropriate countermeasures, i.e. it supports the decision making.

Based on these findings measures for an improved thermal and fluid-mechanical process control will be worked out and their potential for thermal and fluid-mechanical process control will be checked with the digital twin. These developed countermeasures will be tools which strengthen the options for real-time process control in the machine support system.

Advanced CC process control in real-time offering machine supported decisions

The chart below shows the organisation of the scheduled real-time support system with the different modules contributing to this system. Comprehensive temperature measurements in tundish and mould as well as the monitoring of the casting powder cover provide the basis for this approach. On the one hand, these data will be utilised for the offline statistical data analysis aiming at an assessment of the casting process and correlations with the corresponding product quality. On the other hand, measurements and monitoring will provide an online basis for the real-time support system. Here the defined rules for an advanced process control will be evaluated in real-time and the status of the casting machine will be judged, e.g. realised as a traffic light.

                                                    Organisation of the real-time support system

The project leading to this application has received funding from the Research Fund for Coal and Steel under grant agreement No. 847334. On 1./2. October 2019 was the kick-off meeting in the BFI. http://www.bfi.de/en/2019/10/16/kick-off-meeting-realtimecastsupport-october-1st-2nd-in-dusseldorf/

 

Avatar ByGerald Stubbe

LowCarbonFuture

LowCarbonFuture

Exploitation of Projects for Low-Carbon Future Steel Industry

LowCarbonFuture project abstract

The project “LowCarbonFuture” has the objective to collect, summarize and evaluate research projects and knowledge dealing with CO2-mitigation in iron and steelmaking.

As final result, LowCarbonFuture will generate a roadmap stating research needs, requirements and boundary conditions for breakthrough technologies and a new CO2 lean steel production to guide the EU steel industry towards the world’s climate contract and the EU climate goals, e.g. by implementing the key findings in the strategic research agenda of the European Steel Technology Platform (ESTEP). Furthermore, “LowCarbonFuture” will contribute to an update of the steel roadmap for a low carbon Europe 2050 and the current BIG-Scale initiative of EUROFER.

LowCarbonFuture initial situation

According to the steel roadmap edited by the European Steel Association (EUROFER), CO2 emission must be decreased by at least 80 % until 2050 (based on 1990’s level).

Only by means of incremental improvement of ironmaking and steelmaking processes the reduction target cannot be reached, since European production routes already perform at their physical thermodynamic limits (black and blue curves in Figure). A complex mix of actions are necessary (curves orange, red and brown in Figure) to reach the ambitious reduction target.

LowCarbonFuture technological pathways

Current pan-European research is focused on the two main pathways Carbon Direct Avoidance (CDA), and Smart Carbon Usage (SCU). SCU is further divided into the pathways Process Integration (PI) and Carbon Capture, Storage and Usage (CCU).

CDA means the production of steel without direct release of carbon emissions based on hydrogen and electricity. Regarding the energy supply, steel production is shifted from carbonaceous sources to hydrogen based sources with electricity from renewable energies. The pathway PI covers the existing steelmaking routes (BF / BOF and DRI / EAF) using fossil fuels (coal, natural gas, etc.) and how these processes must be adopted to release less CO2. Carbon Capture and Usage (CCU) covers the usage of CO2 i.e. all the options for utilizing the CO and CO2 in steel plant gases or fumes as raw material for production of/integration into valuable products.

The involved Partners in the research project are:

VDEH-Betriebsforschungsinsitut GmbH  
Centre de Recherches Metallurgiques (CRM)  
Rina Consulting Centro Sviluppo Materiali S.P.A. (CSM)  
K1-MET GmbH (K1-MET)  
Swerim AB (SWERIM)  

This project receives funding from the Research Fund for Coal and Steel under grant agreement No. 800643.

LowCarbonFuture objectives

The main objectives of this project are:

  • Collection of knowledge dealing with CO2-mitigation within the steel industry
  • Dissemination of the gained knowledge from current research activities (workshops, seminars, webinars, participation in conferences, scientific journal articles)
  • Definition of building blocks for a successful technology implementation
  • Generation of a roadmap stating research needs, requirements and boundary conditions for breakthrough technologies and a new CO2lean steel production
  • Strategies for technology transfer between the steel companies and stakeholders from other industrial sectors

Further information and news of the LowCarbonFuture project can be found on www.lowcarbonfuture.eu/