Summary of the context and overall objectives of the project

Is a hot rolling mill secured against intentional attacks? Could a re-heating furnace or accelerated cooling be used to sabotage the quality of a European steel producer? How can attacks be separated from fault behaviour? AutoSurveillance will provide a solution for detecting anomalies in re-heating furnaces, hot-rolling mills and accelerated cooling, a solution that can announce a threat and distinguish between faults and intentional attacks in parallel. It focuses on the process-oriented treatment of such occurrences rather than the IT perspective. AutoSurveillance has proposed three different “in-deep” views, ranging from control components in a plant, plant level, and facility level, where time frameworks for detection and reaction are consistently different.

First, the definition of faults and attacks and their interplay with model uncertainties and disturbance was discussed. And how these can be distinguished in principle from a set-theoretical point of view. And how, from a set-theoretical point of view, these are, in principle, distinguishable. Subsequently, plant-specific details were discussed. Accordingly, a risk assessment of the faults considered, and their financial impact was attempted for each use case. The plant’s physical /and data-driven models and controls were set up according to this. These models have been extended to include actuator, sensor, and parameter faults models. In addition, models for Denial of Service (DoS) and Hidden Attacks have been added. For example, it was possible to show that a DoS attack on the control of the hot strip lines leads to a destabilisation of the control and, thus, to a strip break. It is possible to adjust the strip thickness unnoticed using a hidden attack. Different methods of detecting faults and attacks were investigated.

On the one hand, model-based approaches such as different observer concepts (Kalman filter, extended Kalman filter and banks of Kalman filter) whose results were evaluated using a One-Class Support Vector machine to distinguish the root cause of the faults and attacks. Using the Support Vector machine also ensures the robustness of the decision-making against disturbances and uncertainties. On the other hand, data-driven methods were investigated. Here, the focus was non-linear mapping to low dimension and machine learning methods based on auto-encoders. With each of these methods, it was possible to detect faults and attacks and to determine the root cause of the faults and attacks.

Attack techniques that focus on stealth and redundant access make them challenging to detect and stop. Intrusion detection systems monitor networks and alert security authorities to potential intruders, but many alerts go unanswered due to high false alarm rates.  Similarly, in fault detection, the possible response to faults depends on fault detection and isolation reliability. Based on these considerations, we propose a three-step approach. First, the user is alerted. This is always activated as soon as an anomaly is detected. Second, suppose it is possible to determine the type of fault or attack (e.g., isolation) and depending on the accuracy and impact of the fault and attack, a manual countermeasure is taken, i.e. in that case, a human reacts, or third, an automatic countermeasure is taken to move the system into a safe area.

We introduce a Hybrid Distributed Batch-Stream (HDBS) architecture explicitly designed for real-time data anomaly detection. The hybrid architecture combines the accuracy of batch processing with the speed and real-time capabilities of stream processing. In our proposed architecture, we specifically emphasise the algorithmic aspects of hybrid processing, including the characteristics of machine learning algorithms and the principles of integrating results from different processing units.

The automatic reaction strategies for DoS-Attack were demonstrated for a single control loop, the looper and the thickness controller of a finishing mill. Hardware in the loop simulation for faults and attack detection was carried out for reheating furnaces and accelerated cooling lines at the Prisma facility. Finally, data-driven methods were demonstrated for a whole production line at one of Sidenor’s long product lines.

For full utilisation of the project’s solutions, staff at SIDENOR and PRISMA is trained to operate the developed software modules and to integrate them further into other areas of their businesses. The staff training focuses on the developed applications’ uses, transferability, adaptability, and dissemination.

Ten publications have been made on this project, including one conference workshop.

Work performed and main results achieved

At the beginning of the project, the AutoSurveillance consortium developed an abstract definition of faults and attacks compared to model uncertainties and disturbances. In the next step, a risk assessment was performed. Different analysis methods were compared and evaluated for the three use cases: reheater and advanced cooling, hot rolling mill for flat products (see Figure 1) and hot rolling mill for long products (see Figure 2)

In the three use cases, we focused on different aspects. We covered the examination of controllers in a plant, on the plant level and up to the facility level.

• At Plant Level: We concentrated on a looper and thickness control in the use case finishing mill and analysed it in detail.
• On Plant Level: In the use case of the reheating furnace and rapid cooling, we investigated the automation of the entire plant.
• Facility Level: Process In the use case of plant products, we investigated methods suitable for the entire process chain.

Fault and Attacks

The faults can be classified as follows:

• Plant Faults: Such faults change the dynamical I/O properties of the system.
• Sensor faults: The plant properties are unaffected, but the sensor readings have substantial errors.
• Actuator faults: The plant properties are unaffected, but the controller’s influence on the plant is interrupted or modified.

Furthermore, the faults can be divided into the following types:

• Stepwise change of actuator or sensors values.
• Slowly varying actuator or sensor values
• Stepwise change of process parameter
• Process faults like drifting or creeping

In this project, we focus on the following attacks:

• Denial-of-service (DoS) attack: A denial-of-service attack occurs when legitimate users are unable to access information systems, devices, or other network resources due to the actions of a malicious cyber threat actor. A denial-of-service condition is accomplished by flooding the targeted host or network with traffic until the target cannot respond or crashes, preventing access for legitimate users. DoS attacks can cost an organisation time and money while its resources and services are inaccessible.
• Man-in-the-middle attacks: The attacker stands either physically or – today mostly – logically between the two communication partners, has complete control of the system over the data traffic between two or more network participants and can view and even manipulate the information at will. This attack’s danger lies in the fact that the attacker pretends to be the respective counterpart to the communication partners.
  • Bias injection: Incorrect data is added to an actuator or sensor, causing the process to malfunction,
  • Covert attack: Incorrect data is added to an actuator or sensor, causing the process to malfunction without the operator or monitoring system noticing.

Cyber-attacks can cause these attacks.  A cyber-attack is a targeted attack on one or more information technology systems to impair the IT and OT systems entirely or partially by information technology means. This way, the attacker can access and change the process control system. The attack takes place exclusively in virtual cyberspace.

Fault and Attack detection and isolation

Driven by the industrial partners of the consortium, an estimation of the costs that faults and attacks might originate was created. Several physical and data-driven models were developed based on defined attacks and faults for the three use cases. The physical models were based on observer strategies; the data-driven techniques were based on projection methods into lower dimensional spaces (see Figure 3), and the ML approach was based on an autoencoder. The combined strategy uses the observer-based approach to pre-process the subsequent cause determination using the one-class support vector machine for the fault or attack, see Figure 4.

Reaction strategies

Attack techniques focusing on stealth and redundant access make them challenging to detect and stop. Intrusion detection systems monitor networks and alert security agencies to potential intruders, but many alerts receive no security response due to the high false alarm rate.

Similarly, when it comes to fault detection, the possible response to faults depends on the reliability of fault detection and isolation. Considering the expected conditions and capabilities on site in the factories, a three-stage concept was developed to respond to the identified anomalies. The first stage is an alarm that will always be triggered as an immediate response. Then, based on the available information, one of the two other stages will be applied. The next stage, a manual countermeasure, will have a human react to the anomaly. As the third stage, an automatic countermeasure will bring the system to a safe state for further examination if necessary.

Figure 5 shows the structure of the response to faults and attacks. After a fault or attack has been detected and isolated, a reaction strategy is triggered via a risk evaluation table. Three types of reactions are triggered:

• Alarm: Rise alarm to the user. This is always activated as soon as an anomaly is detected.

If it is possible to determine the type of fault or attack (e.g., isolation) and depending on the accuracy and impact of the fault and attack,

• a manual countermeasure is taken, i.e. a human reacts or
• an automatic countermeasure is taken to move the system into a safe area,

The manual countermeasures strategy was implemented for reheating furnaces and Accelerated cooling. In the simulation, we test an automatic counter measurement on a DoS attack of the thickness measurement system of a finishing mill.

IoT / Streaming architecture

Effective anomaly detection relies on both recent and historical data. Therefore, a balance between speed and accuracy is needed. We introduce a Hybrid Distributed Batch-Stream (HDBS) architecture designed to achieve the desired real-time data anomaly detection results, see Figure 6. The hybrid architecture combines the accuracy of batch processing with the speed and real-time capabilities of stream processing. In our proposed architecture, we specifically emphasise the algorithmic aspects of hybrid processing, including the characteristics of machine learning algorithms and the principles of integrating results from different processing units. Such a structure makes total sense because of the designed scope of the analysis, where the other use cases have a different exploratory perspective (component level for case I, “Hot strip mill, looper and thickness control”, production line for case II “hot strip mill: reheating furnace and accelerated cooling” and system level (something in the middle of the other two cases) for case III “Whole productions line for long products”).  Therefore, for case I, the design needs to be pure streaming, whereas for the second, since the ambition is monitoring, the scale is a hybrid between batch and streaming.

When detecting anomalies requires a balance between speed and accuracy, relying solely on either stream processing or batch processing paradigms fails to yield satisfactory outcomes. This is because effective anomaly detection relies on both recent and historical data and becomes clear from the lab work carried out and because of the different scopes adopted. In such scenarios, a viable solution to achieve the desired results is to employ hybrid processing, which combines the strengths of both batch and stream processing approaches. Therefore, the proposed architecture can use big data infrastructures in batch and stream processing units and identify anomalies with high speed and accuracy.

Figure 6 illustrates the processing infrastructure of the Hybrid Distributed Batch-Stream (HDBS) system, drawing inspiration from the Lambda architecture. It comprises three distinct units: the batch processing unit (BPU), the stream processing unit (SPU), and the combination unit (CU).

The first processing unit of the HDBS is Batch Processing Unit (BPU). The main task of this unit is to create accurate models from a large amount of incoming training stream data. This unit’s primary purpose is to increase anomaly detection accuracy. Since the training data is streamed in the system, the batch processing unit should have a master database (MDB) for storing incoming training data. Different solutions have been tested, and InfluxDB alternative suits very well for this purpose.

In addition to the MDB, the unit incorporates a component dedicated to constructing batch models as an offline detector. This component leverages the data stored in the MDB to build precise batch models over time. Each model uses a specific period of recent data available in the MDB. Once a model is created, a new model is generated using the next period of current data, which may include a combination of new and existing data within the MDB. This iterative and periodic process ensures the continuous reconstruction of models by the component.

The stream processing unit (SPU) is the second parallel processing unit in the HDBS architecture, operating alongside the batch processing unit (BPU). Its primary function is to handle the continuous data flow by utilising a portion of newly arrived data. The learning model employed by the SPU is designed to be incremental, gradually becoming more comprehensive as time progresses. This unit promptly reports its processing outcomes in real-time, primarily aimed at enhancing the response time for anomaly detection. Unlike the batch processing unit, the stream processing unit does not store the incoming stream data.

The third unit within the HDBS architecture is the combination unit (CU). Its primary function is to facilitate the rapid loading of processing models generated by the batch processing unit (BPU) and the stream processing unit (SPU) and subsequently merge them. This merging process is essential for achieving a fast and efficient combination of models, as implemented in the proposed hybrid algorithm (HDT). To fulfil its role effectively, the combination unit is equipped with databases that enable swift data retrieval and access. Consequently, the batch processing unit and the stream processing unit store their respective models, represented as trees or other appropriate structures, in dedicated databases designed for batch and stream models. The combination unit then presents these stored models to the merging component (MC), utilising the fast databases. The merging feature is responsible for merging the processing models to create the final hybrid detector.

The cloud-oriented architecture will be analysed to facilitate transferability while maintaining the solution’s reliance and resilience attributes, including Apache Kafka producer-broker-consumer architecture and server-less microservice container-oriented architectures.

Overview of the results and their exploitation and dissemination

The RFCS project AutoSurveillance has implemented a framework for fault and attack detection, isolation, and reaction strategies. The applicability of this framework was demonstrated in three use cases ranging from single control loops to a plant and finally to a production chain.

A Hardware-In-the-Loop (HIL) application has been designed and developed during the AutoSurveillance project for the case studies related to the reheating furnace and the accelerated cooling lines. The HIL application makes it possible to simulate in detail the processes’ dynamic behaviour during normal and abnormal operations and assess the control system’s robustness to anomalies and cyber-attacks. In addition, a software solution provides a graphical user interface for monitoring users to evaluate the current operation of the processes and to use anomaly detection algorithms helpful in guiding the monitoring task. Both applications form the basis for developing and testing future algorithms and control systems for industrial use. Furthermore, the applications can be continuously improved and transferred to additional use cases.

The automatic reaction strategies for DoS-Attack were demonstrated for a single control loop, the looper and the thickness controller of a finishing mill. Hardware in the loop simulation for faults and attack detection was carried out for reheating furnaces and accelerated cooling lines at the Prisma facility. Finally, data-driven methods were demonstrated for a whole production line at one of Sidenor’s long product lines.

For full utilisation of the project’s solutions, staff at SIDENOR and PRISMA is trained to operate the developed software modules and to integrate them further into other areas of their businesses.

The staff training focuses on the developed applications’ uses, transferability, adaptability, and dissemination.

Additionally, the consortium conducted several dissemination activities at international conferences and in international journals, as the following list demonstrates.

• Neuer, M., Kretschmer M., Wolff A., “Automation security in the era of Industry 4.0”, Future Steel Forum, September 2019, Budapest
• Neuer M., Wolff A, Holzknecht N., “Cyber-attacks for breakdown or intentional quality reduction – how secure is the European steel production in the era of digitalization?”, Conference: European Steel Technology and Application Days (METEC & 4th ESTAD) June 2019
• Wolff , Neuer M, Holzknecht N., Deprez J.Ch., Monton St., Colla V, Iannino V., Bottazzi C. Ferraris F., Ordieres-Mere J., Arteaga A., “Automatic surveillance of hot rolling area against intentional attacks and faults (AutoSurveillance). Overview and first results of the RFCS-funded Project”, ESTAD 2021 Stockholm
• Ordieres-Meré J, Arteaga A., Matskanis N., Wolff A., Iannino V., “Process Supervision of Long Products Hot Rolling Mill. Attack or Failure Identification”, IFAC-PapersOnLine 55(2):72-77, January 2022
• Ordieres-Mere J., Wolff A., Pacios-Alvarez A., “Cybersecurity challenges in downstream steel production processes”, 1st IFAC Workshop on Control of Complex System Cosy 2022: Bologna, Italy, 24-25 November 2022
• Ordieres-Mere J., Wolff A., Bello-García A., Dettori St., “Contributions from cloud-based OT solutions for downstream steel processes”, ESTAD 2023, Düsseldorf
• Wolff A., Neuer M, Holzknecht N., Deprez J.Ch., Monton St., Colla V, Dettori St., Bottazzi C. Ferraris F., Ordieres-Mere J., Arteaga A., “Reliable digital twins for the transition to a CO2-free steel industry” ESTAD 2023, Düsseldorf
• Liu, Q. Zeng, J. Ordieres Meré, H. Yang, “Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach”, Complexity, 2019
• Villalba-Diez J., Ordieres-Meré J., González-Marcos A., Soto Larzabal A., “Quantum Deep Learning for Steel Industry Computer Vision Quality Control.”, IFAC-PapersOnLine, Volume 55, Issue 2, 2022, Pages 337-342
• Ordieres-Meré J., Gutierrez M., Villalba-Díez J., “Toward the industry 5.0 paradigm: Increasing value creation through the robust integration of humans and machines”, Computers in Industry, Volume 150, September 2023

Additionally, a workshop was organised by the partners BFI and SSSA. This workshop elucidates the steel industry concerning the dangers of attacks on automation systems. It had to be clarified why solid fault detection a mandatory element of any attack prevention approach is and how to establish attack detection and mitigation on the existing computational infrastructure of the plants.

The following workshop was held at Word CIST 2023 on April 4-6, Pisa, Italy:

• 1st Workshop on Information Systems and Technologies for the Steel Sector.

Progress beyond the state of the art and potential impacts

The main progress beyond the state of the art was reached by defining fault and attack types using a unified framework. Methods for detecting faults and attacks tailored to steel production processes were developed. Observer-based methods, data-driven methods for the projection into lower dimensional spaces, and ML methods for non-linear dimension reduction were used.

The development and enhancement of European technological, strategic, and digital autonomy must go through the development and enhancement of the industrial and technological European cybersecurity, facilitating synergies between EU industry and the EU academic, furthermore reducing the dependence on non-EU technology.

The threat of cyberattacks against industrial plants is real, and their frequency constantly increases. The comprehensive protection of industrial plants against internal and external cyberattacks requires an approach that covers all levels simultaneously – from the operational to the field level.

The technical and economic potential to use the results of the AutoSurveillance project are:

• Reduces risks and impacts on the plant/process/production in case of attack; after the mitigation actions of the risk response planned and described in the Deliverable D1.2
• Reduces costs derived by damages on the plant/process/production in case of attack.
• Reduces costs derived by loss of know-how and confidential information.
• Increase the operational technology safety in the industrial plants through the reaction strategies planned.
• Increase the operational technology safety in the industrial plants by training the planned plants’ engineers and operators.
• The transferability and adaptability of the AutoSurveillance system described ensure that the AutoSurveillance system can be installed and used in other companies and plants.

The technologies, hardware and software used for engineering and developing the software prototype of the AutoSurveillance system are the most modern and safe currently usable and purchased on the European market; the engineered and acquired technologies,

Images are attached to the Summary for publication.

Acknowledgement

This project receives funding from the Research Fund for Coal and Steel under the Grant Agreement Number 847202.

REUSteel

Dissemination of results of the European projects dealing with reuse and recycling of by-products in the steel sector

REUSteel project abstract

European Research dedicated a lot of time and efforts in the development of innovative sustainable solutions for the steel industry. The aim is to provide bridging solutions to lead the steel industry towards a reduction of its environmental impact with an obvious saving of natural resources, hence in being closer to a virtuous objective of “zero waste” goal. These thematic fields are perfectly framed in a concept that is strongly emphasized at a European level and that is receiving increasing attention in the scientific and technical community in the latest years, namely the so called Circular Economy. The circularity concept pushes researchers and industries to look for synergies with other industrial sectors to analyze and investigate solution for improving by-product reuse and recycling both inside and outside the steelmaking cycle, by thus developing examples of industrial symbiosis. However, the joint efforts of the EU steel industries on this theme are still not widely known.

REUSteel project aims at extensively disseminating and valorizing important research results on the reuse and recycling of byproducts, based on an integrated critical analysis of many list of EU-funded projects, in order to promote the results exploitation and increase the synergies with other sectors.

REUSteel project overview

“Dissemination of results of the European projects dealing with reuse and recycling of by-products steel sector (REUSteel)” is a project co-founded by the Research Fund for Coal and Steel (RFCS).

The project is framed in the context of more eco-friendly and sustainable solutions in the steel industry. The reuse and recycling of by-products of the steelmaking cycles as well as on the exploitation of by-products from other activities outside the steel production cycle, such as alternative carbon sources (e.g., biomasses and plastics) are the proper actions to move towards a saving of the natural resources together with a reduction of the environmental impact, hence being closer to a “zero-waste” goal.

“REUSteel” aims at an extensive action of dissemination and valorisation of the most important research results on by-products reuse and recycling, based on an integrated critical analysis of many EU-funded projects, in order to promote results exploitation and increase the synergies with other industrial sectors. This analysis will allow identifying the most urgent needs and ambitions of the European steel sector and defining a sequence of future research topics in this field.

A joint critical analysis, carried out by all the partners, belonging to different institutions, will provide new insights and guidelines for future research topics in this field, in order to promote the dissemination and, consequently, the implementation of the achieved results. The project will also organise the results, in order to present selected groups of topics at planned workshops and seminars. This will provide a clearer vision of the outcomes to stakeholders and new audiences, in order to get new and deeper indications for a new roadmap, future synergies with other sectors and industrial trends.

“REUSteel” will contribute to an update of the steel roadmap for a low carbon Europe 2050 and the current BIG-Scale initiative of EUROFER.

The involved Partners in the research project are:

Sant’Anna School of Advanced Studies (Coordinator)	www.santannapisa.it/en
VDEh-Betriebsforschungsinstitut GmbH (BFI)	www.bfi.de/en/
FEhS – Institut für Baustoff-Forschung e.V.	www.fehs.de/en/
Rina Consulting Centro Sviluppo Materiali S.P.A. (CSM)	www.rina.org/en
SWERIM	www.swerim.se/
European Steel Technology Platform (ESTEP) is also involved in the project as subcontractor of Sant’Anna School of Advanced Studies	www.estep.eu

The Project Coordinator is Dr. Eng. Valentina Colla (valentina.colla@santannapisa.it)

REUSteel dissemination action

The project was finalized in January 2022. Further information about events, videos and public deliverables as the roadmap of the REUSteel project can be found on https://www.reusteel.eu/index.html

• Events:
  https://www.reusteel.eu/events.html
• Documents and Videos
  https://www.reusteel.eu/documents.html
• Public Deliverables
  https://www.reusteel.eu/deliverables.html

This project is carried out with the financial support of the Research Fund for Coal & Steel – Grant Agreement Number: 839227 (2019)​

PerMonLiSt

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

PerMonLiSt project objectives

The main objective of the research project was 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 have been 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 should 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 have been coupled to an integrated approach and tested exemplarily for the most important liquid steelmaking facilities of the electric steelmaking route, i.e. for electrical arc furnace (EAF), ladle furnace (L)F, vacuum degasser (VD) and argon stirring plants.

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

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

In a first step, 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. Data acquisition and model functions for EAF and ladle treatment processes at PTG as well as EAF process at Feralpi/Lonato and Tata/Aldwarke steel plant have been completed.

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 for all studied EAFs of the industrial partners is given between metallic yield and specific oxygen consumption (see Figure A). 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. Such correlations were used to appropriately adapt related operating practices. Analysed correlations between model and process performance indexes reveal systematic errors of the respective model for certain ranges of process operation.

Figure A: Correlation between specific O2 gas consumption and metallic losses for different EAFs of industrial partners

Regular ranges for defined process performance indices have been defined which are used within enhanced monitoring and alert functions regarding process behaviour and performances. At Feralpi Lonato steel plant such functions have been included in the newly installed on-line system EAFPro covering the EAF as well as the subsequent ladle treatment processes until teeming in tundish. The implemented monitoring functions comprise also new control charts with statistical evaluations of relevant process parameters and performance indices. Regarding enhancement of process monitoring, PTG has laid the focus on the EAF process. A suitable new human machine interface (HMI) has been designed and installed within their manufacturing execution system (MES) to support the operator in end-point control of the EAF process (see below). Furthermore, operating practices for EAF and secondary metallurgy have been adapted via appropriate configuration of the MES. For the secondary metallurgical ladle treatment, the specification of target temperature and time for delivery of a heat to the caster has been extended to take into account the position of the heat in the casting sequence. This information is used for dynamic adaptions of defined variable set-points in the operating practices (e.g. electrical energy input in LF) based on predictive model calculations.

The operating instructions at Feralpi Lonato plant have been adapted regarding practices with charging of 2 or 3 scrap baskets, charging and injection of carbon as well as injection of oxygen in the EAF. Charging of 2 scrap baskets results in lower electrical energy consumption for cases with sufficiently high scrap densities. The charging and injection of carbon is reduced in cases of too high carbon contents analysed in LF for previous heats. Additional increase of injected oxygen during EAF refining phase reduces carbon content further on with lowering electrical energy consumption and tap to tap time but also with decrease of metallic yield. The management of optimised oxygen injection as well as charge and injection of carbon in order to achieve maximum metallic yield, target carbon content in steel and sufficient slag foaming in EAF is supported by related control rules. Figure B shows an example for control of oxygen injection using the calculated slag oxidation status (SOS = calculated slag weight / reference slag weight) and appropriate thresholds for adaption of the injection practice (based on reference slag weight of 5 t).

Figure B: Logics of control function implemented at Feralpi Lonato for adaption of oxygen injection using slag oxidation status (SOS)

For applications with large number of model and control parameters, auto-calibration methods based on regression analysis with least-squares fitting and artificial neural network have been chosen, while approaches using Unscented Kalman filters (UKF), moving average corrections or KPI (key performance index) based heuristics were found to be suitable for on-line heat-to-heat adaptions of few parameters with rare measurements.

BFI has set-up an off-line calibration procedure for the parameters of the dynamic EAF model installed at PTG furnaces based on regression analysis with least-squares fitting on a defined number (e.g. 200) of last produced heats. This procedure can be called after a certain number (e.g. 100) of newly produced heats in order to keep the parameters adapted to the respective scrap, plant and process conditions. For the secondary metallurgical temperature model an on-line batch-to-batch adaption of identified (significant) parameters distinguished for 3 different routes (with 3 different LFs) has been developed using an Unscented Kalman filter (UKF) approach. Figure C exemplarily shows the resulting evolution of the electrical energy efficiency for one ladle furnace with an initial decrease of this efficiency which is later recovered again.

Figure C: Batch-to-batch adaption of electrical energy efficiency at ladle furnace of PTG steel plant

CRM has set-up a parameter calibration for their EAF model based on the same Kalman filter technique. It is important to select the most significant parameters and to adapt not more parameters in one step than the available number of temperature measurements for the related heat. The convergence speed of the Kalman filter should be adjusted by appropriate selection of the covariance matrix of applied process noise, so that the parameters converge on average over 10 -20 adaption steps.

CSM and Feralpi have developed an artificial neural network solution (ANN) for auto-calibration of the scrap basket composition at Feralpi Lonato plant (see Figure D). The sterile content of scrap also has to be taken into account in the slag weight which is estimated by the used EAFPro model to assess the slag oxidation status (SOS). This is a KPI mainly used at Feralpi Lonato plant within control rules for oxygen injection during the EAF refining phase (cf. Figure B). Another developed method for auto-calibration of the sterile content in scrap uses a moving average over the difference between calculated and measured tap weights of steel.

Figure D: Auto-calibration loop for scrap basket composition at Feralpi Lonato plant using an ANN

Furthermore, a temperature model for ladle treatment between tapping at EAF and teeming in tundish has been set-up by CSM and Feralpi which is also based on a calibration of model parameters via statistical analysis with moving average adaptions. The auto-calibrated model calculations are used for predictions of arrival temperatures in LF and CC. Together with defined acceptable limits for these arrival temperatures, the model predictions are used to support the operator in control of the thermal state evolution along the whole electric steelmaking route. This kind of temperature control together with the already mentioned adaptions of oxygen injection as well as carbon charge and injection based on evaluation of slag foaming via an acoustic sensor in EAF build an expert system of control rules for process management at Feralpi Lonato plant. For the used control rule parameters (e.g. limits for SOS and acoustic foaming index) there have been derived heuristics based on related KPIs for suitable auto-calibrations. For the auto-calibration of the SOS limits used within the control rules for oxygen injection (which were in the main focus for the EAF process at Feralpi Lonato), the heuristic uses information from the adapted sterile content in scrap.

At PTG as well as at Feralpi Lonato steel plant the installed tools support the operator in on-line monitoring and control of the EAF and secondary metallurgical ladle treatment processes, especially regarding electrical and chemical heating. Figure E gives an example of the newly installed HMI for online monitoring of the EAF processes at PTG steel plant. For support of end-point control of the heating process, the horizontal red line in the upper half indicates the target tapping temperature to be met by the actual temperature calculated cyclically by the EAF model.

Figure E: HMI for online monitoring and control of EAF processes at PTG steel plant

The installed least-squares fitting procedure for periodic adaption of the EAF model parameters mainly improves the mean model error (from mainly negative values to values around 0), whereas the standard deviation of the model error more or less is unaffected (cf. Figure F). The improvement of the mean model error results in an avoidance of systematic over- or underestimation of the melt temperature by about 5 – 10 K. Cases with systematic underestimation as shown in Figure F cause an overheating of the melt with related increase of

• specific electrical energy consumption by about 3.5 – 7 kWh/t
• and of tap-to-tap times by about 15 – 30 s

as well as negative effects on the lifetime of the furnace refractory. Cases with systematic overestimation of the melt temperature lead to need of

• further heating in EAF after a temperature measurement with related increase of tap-to-tap times
• or additional heating in LF (if such a measurement is not taken before tapping) with related longer treatment times and thereby lower productivity.

Figure F: Evolution of moving averages and standard deviations of errors in model temperatures at PTG with and without recalibration of parameters at the DC-EAF

The secondary metallurgical temperature model installed at PTG steel plant turned out to be already well calibrated and quite stable regarding the plant and process conditions. Thus, the batch-to-batch auto-calibration procedure based on the UKF approach has been tested after detuning of model parameters. This proved an automatic re-calibration of the model by the installed UKF procedure within about 20 heats resulting in reduced mean values and standard deviations of the model errors compared to the detuned cases.

The off-line application of the UKF approach for batch-to-batch auto-calibration of the CRM EAF model provided significant improvements of calculation accuracies (see Figure G). The mean error is decreased from 18,7 K to 3,5 K and the standard deviation from 58,8 K to 23,1 K.

Figure G: Correlation between calculated and measured temperature without (left) and with (right) auto-tuning of parameters for CRM EAF model

Thus, the on-line application of the CRM EAF model with batch-to-batch auto-calibration and thereby ensured improved model accuracies will lead to similar benefits as estimated above for the BFI EAF model applied at PTG steel plant.

For improved process monitoring and control of the EAF process at Feralpi Lonato steel plant, the auto-calibration of sterile content in scrap has been identified as one of the most important components. A moving average adaption of this parameter yields improved results for mass balance calculations including slag oxidation status (SOS) used for control of oxygen injection. Figure H gives an example for monitoring of the auto-calibrated sterile content of scrap and the related steel weight error.

Figure H: Monitoring of auto-calibrated sterile content of scrap and related steel weight error

The SOS based expert rules for on-line control of oxygen injection during refining in EAF lead to avoidance of overoxidation with reduced consumptions of electrical energy and oxygen and increased metallic yield. The on average achieved improvements at Feralpi Lonato plant comprise

• reduction of specific electrical energy:  10 kWh/ton
• reduction of SOS value at end of heat: 0,02
• reduction of used oxygen volume: 250 Nm³
• increase of tap steel weight: 500 kg
• increase of metallic yield: 0,5 %

Furthermore, the rules for alerts regarding arrival temperatures in LF and CC based on the installed temperature model for ladle treatment at Feralpi Lonato plant with adapted parameters have been proven to support the operator decisions for temperature control along the whole steelmaking route.

Slagreus

Reuse of slags from integrated steelmaking

Project abstract

In Europe 2016 steelmaking slags (SMS) amount to 18.4 Mio t/a, of those 10.4 Mio t/a are generated through BOF steelmaking. The majority of slags are used in road construction (46,0 %). Internal recycling for metallurgical use amounts to 15,3 % of the SMS. Unfortunately landfilling already makes up the third biggest share of life cycle destinations. This amount is prone to increase as future EU regulations will cause many SMS not to be permitted for use in road construction. Under the pretext of circular economy, it is desirable to increase internal recycling. That however is limited by the phosphorus content in the slag, due to the deterioration of material properties that would be caused by the phosphorus in steel. SLAGREUS, thus aims to increase internal recycling of primary BOF slag by generation of a high Fe- and low P-slag fraction ready for sintering or direct use in BF and a fraction containing high amounts of Ca and possible free lime for cement production.

This project receives funding from the Research Fund for Coal and Steel under grant agreement No. 847260. The project has started June 1st of 2019 and is set to be concluded on December 1st of 2022.

The research partners involved in the project are:

VDEh – Betriebsforschungsinstitut GmbH (BFI)
Voestalpine Stahl GmbH (voestalpine)
K1-MET GmbH (K1-MET)
Institut für Baustoff-Forschung e.V. (FEhS)
Oulun Yliopisto (Uni Oulu)

Objectives

In order to achieve the goal of saving natural resources and reduce landfilling the amount of internally recycled SMS must be increased. It is therefore necessary to develop a new BOF-slag treatment process that allows for the slag to be divided into a fraction of high iron and low phosphorus content and a fraction of high calcium and phosphorus content. The Fe-rich/P-poor fraction will be tested for metallurgical re-use in sinter plants. The Ca-/P-rich fraction will be evaluated as a cement additive, as well as its use in lime fertilizers.

The additional objective of the project is the development of an off-line prediction tool for mass and energy balances of the recycling process, thereby providing information about expected quality and quantities of the slag fractions. The prediction tool will be based on the separation efficiency and specific energy demands of the individual unit operations within the process.

Research approach

The new process concept is divided into two phases. A primary liquid treatment and secondary solid treatment.

During primary liquid the BOF-slag is slowly cooled to allow for large calcium-silicate crystals to form and segregate to the top of the crucible. The liquid Fe-enriched part is then recirculated into the next slag melt. This facilitates segregation. The obtained calcium-silicate rich phase can already be used as a test material for addition into cement. The iron enriched fraction is slowly cooled to generate large grain sizes, which benefits the secondary solid treatment.

The secondary solid treatment only focusses on the iron enriched fraction obtained from the primary liquid treatment. The first unit operation is the microwave heating of the slag, improving liberation of the individual mineral phases by generating thermally induced tension within the particles. The slag is than ground which the goal to complete phase liberation. The last step of the solid treatment consists of dry magnetic separation, after which the final Fe-rich/P-poor fraction will be separated from the Ca-/P-rich fraction.

During the project the process concept will be implemented and investigated at laboratory and pilot scale. The concept will also be tested on a small industrial scale. During each stage the processing parameters will be evaluated and will contribute to the development of the prediction tool.

First results

After investigation into grain size distribution and composition via SEM, XRD and XRF of BOF-slag samples, it can be stated that the slow cooling of the slag has the predicted and desired effect of creating large crystal grains with complementary distribution of iron in some phases and phosphorus in other phases. This can be observed in the fluorescence maps depicted in the images below. Iron is marked blue and phosphorous is marked white.

That indicates, if the mineral phases can be separated, so can iron and phosphorous.

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)

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 1^st and 2^nd 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/

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

Morse

Model-based optimisation for efficient use of resources and energy.

Initial situation

• Process industry is continuously looking for new ways to improve resource efficiency
• Model based control systems are established for the single process units
• Integration of unit control systems into plant wide coordinated optimisation applications taking into account also overlying logistic constraints, optimisation criteria and production targets can increase savings in   energy and raw material consumptions

Working points in the project

• Plant-wide analysis of all material and energy flows in order to identify the bottlenecks and main potentials for savings in energy and resource consumptions
• Adaption and enhancement of single unit components for real-time monitoring and control along the production route, especially regarding a coordinated optimisation of the phases with high potentials for energy and resource savings (like electrical heating, oxygen refining, chromium oxide reduction)
• Integration of components into a comprehensive, through-process control system with the help of a suitable framework with common interfaces and communication structures
• Validation of integrated approach for optimisation of resource efficiency within three use cases for carbon steel, stainless steel and cast steel

The project started at October 1st 2017 and ends at September 30st 2021. Involved partners in the research are:

VDEH-Betriebsforschungsinsitut GmbH
Cybernetica AS
GRIPS Industrial IT Solutions GmbH
OPTIMIZACION ORIENTADA A LA SOSTENIBILIDAD SL
Maschinenfabrik Liezen und Gießerei GmbH
OUTOKUMPU STAINLESS OY
SSAB EUROPE OY
SW-Development OY
Teknologian tutkimuskeskus VTT OY
Horizon 2020

Expected results

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-POS

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:

• Optimization of throughput and reduction of energy consumption for the production of complex profiles in Mannstaedt’s hot processing line and
• Optimization of material quality and properties for rail mills at ArcelorMittal España.

This is achieved by applying the developed software and methods for the specific use cases. This implies the following sub-objectives:

• Virtual simulation platform for the design of cyber-physical production optimization systems (CPPS) for long production facilities; with special emphasis to thermal the evolution and related material quality, leading to reduced energy consumption, shortened production time and improved product quality;
• Merging of process models (thermal, rolling, transport), material-quality models, logistics/scheduling models and communication models (computers, software, networks);
• Strategies and methods for cooperative production optimization, enabling fast dynamic and flexible reaction on quality variations, critical states, measurement errors, and changes in set-points, production routes, process disturbances or interruptions;
• New and comprehensive, model-based (simulation) software for design of CPPS for long product factories, with a cyber-physical library for “drag-and-drop” implementation.

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

VDEH-Betriebsforschungsinsitut GmbH
Arcelor Mittal España
ASINCO GmbH
Fundación ITMA
Mannstaedt GmbH
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.

PowGETEG

Recent results of the RFCS research project PowGETEG (Power generation from hot waste gases using thermoelectrics)

PowGETEG project abstract

Industries involve a huge amount of energy consumption. A considerable amount of this energy is lost and escapes to ambient as waste heat. Energy recovery from industrial waste-heat streams attracts interest for commercial and strategic reasons. Main drivers are international competition and technological opportunities, combined with geopolitical issues such as security of energy supply, energy consumption and greenhouse gas emission. In recent years, numerous ideas have been suggested either for better process integration, reuse in other settings, or for power generation. For an efficient use of waste heat generally following order is essential:

1. Prevention / reduction of waste heat e. g. by thermal insulation
2. Recycling of waste heat into the process e. g. by combustion air preheating
3. In-house use of the waste heat e. g. for heating purposes
4. Conversion of waste heat into other forms of energy e. g. electricity or cooling energy
5. External use of waste heat e. g. in district heating networks

In the iron and steel industry the points 1 to 3 are usually state of the art. Thermoelectric (TE) devices have the ability of directly convert waste heat into electricity and can be located under point 4.

TE materials are semiconductors which exhibit a strong relationship between a current flow in the material and the passage of heat through the material. This is due to the Seebeck effect. The Seebeck effect shows itself as the generation of electrical power from the semiconductor when opposite ends of a piece of the material are subjected to hot and cold temperatures respectively. TE modules consist of arrays of N and P type semiconductors in which electrical energy can be produced. TE systems have well known advantages: no moving parts, simple configuration and long-run unattended operation for thousands of hours. Additionally, they are scalable and do not release any pollutant to the environment during operation. Hence, they could be suited for many applications at different scales. Proved applications of thermoelectric power production are in the Aero and Space industry and for power supply in remote areas e.g. at pipelines, on offshore platforms or in nature protection areas. Until now waste heat recovery from industrial plants by TE devices is just demonstrated in research projects in prototype scale applications.

The RFCS PowGETEG research project aims to investigate the possibilities of TE power generation using industrial gaseous waste heat at temperatures well above 550 °C in order to verify the techno-economic feasibility of TE systems for industrial scale waste heat utilization.

The project started at July 1^st 2015 and ends at December 31^st 2018. Involved partners in the research are

VDEH-Betriebsforschungsinsitut GmbH
University of Glasgow
Gentherm GmbH
thyssenkrupp Steel Europe AG
Fundacion Cetena

The research has received funding from the European Union’s Research fund for Coal and Steel (RFCS) research programme under grant agreement No°RFSR-CT-2015-00028.

PowGETEG objectives

Waste heat recovery by TE systems in industrial scale is not known until now. Just a few research projects investigate TE waste heat recovery, mainly in low temperature range with common BI₂Te₃ modules. Knowledge and studies about high temperature waste heat recovery by thermoelectrics in industrial plants and industrial scale are rare.

Aim of the project is to develop a TE demonstrator with a power output of 1 kW_el for utilization of high temperature industrial waste gases with temperatures well above 550°C. The demonstrator will be tested in an industrial environment for several months to determine the techno-economic feasibility of such a system and to make statements about the possibility to use the technology in non-iron and steel industries.

PowGETEG research approach

Main aim is the long-term testing of a newly developed TE demonstrator in an industrial environment. Thus, several waste heat sources of an integrated steel mill will be studied, supported by both tests and data evaluation to determine their suitability for such a long-term test.

Since the TE system will be installed in the waste gas of an iron and steel manufacturing process, advanced components, materials and solutions need to be integrated in the TE system and the electrical power subsystem. These requirements are determined by the high temperature level at which TE power generation will now be applied and the nature of such waste gases, that are produced when combusting iron and steel process gases. For that reason surface coatings for antifouling will be investigated to protect the heat exchanger of the TE system from damages.

To optimize the performance and power output of the TE system a tailor made power converter and new MPPT (Maximum Power Point Tracking) algorithm will be developed. The goal of the MPPT algorithm is to set the TE system to operate at its optimum power output according to the temperature conditions.

By testing a bench scale unit in the laboratory under near-service conditions, which will be able to produce about 200 W_el, conclusions can be drawn about the requirements to process control, power conversion, heat exchanger design and the construction that supports the TE system in the waste heat stream.

Based on the results of the bench scale test a 1 kW_el demonstrator will be developed and tested at the selected industrial plant for several months. The results will then be used to study the techno-economic feasibility of implementing TE systems in high temperature waste gases. This includes a comparison with other steam based power producing technologies and an extrapolation of the research results to other industries.

PowGETEG recent results                                                           

Main results obtained until now are:

A suitable waste heat source at TKSE steel plant was selected and the connection for the demonstrator installed.

A thermoelectric cartridge with an expected power output of 250 W was assembled and tested in the laboratory under near-service conditions.

A power conversion system was developed and assembled. A new MPPT algorithm was designed with an increased power output of 3.7 % compared to other MPPT algorithms.

Coatings for antifouling were investigated and suitable coatings selected

PowGETEG – TEG for high temperature waste heat recovery