Foundry Machinery Metallurgical Plants and Rolling Mills Thermo Process Technology INDUSTRY 4.0

The virtual Achenbach Rolling Mill - enhancing product development and commissioning

„An impressive extension of simulation to a real-time control environment.“ M. Greif, Manager BU Industry, Bachmann electronic GmbH

Initial situation

Solution

Benefits in practice

Highest productivity is the core expectation of mill operating companies all around the world. Nevertheless, improvement needs modification first.

In parallel to the development of a new model based gauge-control-algorithm a simulation environment for cold-rolling mills was created within MATLAB/ SIMULINK.

• Improvement of gauge quality by factor two

Starting up with any modification the risk of damage is high due to previously untested behaviour of the system. Especially optimizations which target strip thickness have been risky in the past. However, strip thickness is a core quality criterion for flat rolled materials. Oszillations of the control circuits could lead to scrapping material or even to severe damages in the machine. Due to the wide spread of different materials and various widths and thicknesses rolled in a mill it took long to optimize the whole production process to full range - even if no severe errors occured.

At first, the new controller-concept was sucessfully tested inside the offlineenvironment and its performance was compared to other approaches. Then, using the toolchain supplied by the PLC-supplier, the digital model of the machine, which was written in SIMULINK, was uploaded to the PLC-environment and the new controller was tested within the real-time environment.

• Period of optimization on the real machine reduced by optimizing the virtual machine • Error probability decreased. • HIL-environment for future developments • Reduced time and risk of development by using a sophisticated toolchain allowing direct code generation from Simulation-environment into the PLC-platform

‚Virtual coils‘ generated from previously recorded real production situations can now be used to optimize the controller performance and to adapt the system to different material properties. Without interfering with current production the optimization of the gauge-control is now possible in a real-time simulation-environment.

Achenbach Buschhütten GmbH & Co.KG • Roger Feist • Head of Automation Siegener Straße 152 • D - 57223 • Kreuztal, Germany Phone +49 2732-799 760 • Fax +49 2732-799 699 • [email protected]

Foundry Machinery Metallurgical Plants and Rolling Mills Thermo Process Technology INDUSTRY 4.0

ANDRITZ METALS Level 2 - Advanced Furnace Control

„Precise process models are an important part of Industry 4.0.“ Dipl.-Ing. Martin Fein, ANDRITZ METALS

Initial situation

Solution

Benefits in practice

New and modern production environments have significantly raised the expectations for quality, efficiency and reliability of industrial processes during the last decade. It became necessary to replace traditional schemes of control with smarter solutions to obtain results with competitiveness in today’s markets. Part of these smarter solutions is gaining more knowledge on the process state, on the dynamic process behavior and on analyzing historical trends for which traditional measures are not adequate. ANDRITZ accepts the Industry 4.0 challenge in being one of the leading companies to implement plant automation technologies of the future.

With widespread technological knowhow in heating and cooling processes Andritz offers sophisticated physical modeling for any kind of industrial furnace. The model predictive control fully supports the ability to make a long time prediction, to optimize future energy flows and production speed and to show operators the future process behavior. This allows Andritz to fulfill the highest quality demands together with capacity optimization and resource efficiency. The advantage of this physical precise model is a very simple configuration with a self-adaption function to react on different product material parameters.

• Achivement of high-end quality requirements at a minimum level of resources • Easy introduction of new products due to a self-adapting physical model • Offline Simulation for product sequencing or virtual furnace operator trainings • Smart tablet and wireless support with intuitive touch-based user interface • Maximisation of production

All Andritz Metals Level 2 systems are fully designed for latest technologies and intuitive controlling such as touch and wireless devices. The quality data can be easily integrated in any kind of data mining and analysis.

ANDRITZ METALS • Dipl.-Ing. Michael Böck-Schnepps Director Electric and Automation • Eibesbrunnergasse 20 • 1120 Wien Phone +43 5 0805 55501 • Fax +43 5 0805 81075 [email protected]

Foundry Machinery Metallurgical Plants and Rolling Mills Thermo Process Technology INDUSTRY 4.0

Intelligent heat treatment cuts cost and boosts flexibility bitte laden Sie in dieses Feld wahlweise ein weiteres Bild

„Mathematical modelling of the heat treatment allows coils to be annealed straight off the rolling mill, saving 45% energy.“ Olaf Trepels, Teamleiter, Reliability Engineer, Aluminium Norf GmbH

Initial situation

Solution

Benefits in practice

Germany‘s aluminium rolling mills produce 2 million tonnes of semi-finished products in the form of strip every year. Before a coil is shipped, the metal is heat-treated in various ways, usually in furnaces holding several coils. By the end of the heat treatment, each coil must have undergone the same annealing practice to obtain identical metallurgical properties. As the strip temperature in the furnace cannot be measured nondestructively, it used to be necessary to formulate an empirically modelled recipe for each annealing practice by means of special measuring strip. The recipe contained the furnace parameters needed to meet the annealing specification. However, the approach had the one drawback of requiring a homogeneous composition of each furnace batch. This meant delays in the material flow resulting in a loss of valuable residual heat from the rolling process.

All types of strip available for heat treatment are summarized in a database. An „off-line“ module of the mathematical model is installed in central production planning. It allows each furnace batch, consisting of up to four strip coils, to be made up in a pre-optimized manner by picking strips with best matching characteristics from the database. The heat treatment of these strips in one batch is then computed in advance and a proposal is generated comprising four recipes. Once this proposal is accepted, a job (batch, recipes) is sent to the furnaces. Before heat treatment starts, the strip weight and temperature are measured automatically. If deviations are noted, the „on-line module“ adapts the recipes. Throughout the heat treatment, it then controls the furnace actuators separately in real time for each recipe.

• 45% reduced energy demand as residual heat from the rolling process can be utilized • Better equipment utilization by realtime management of temperature and weight variations • Improved flexibility as heat treatment can be performed in-process when needed • Early identification of maintenance needs by the model‘s setpoint/actual data output

OTTO JUNKER GMBH • Dr.-Ing. Günter Valder • Technischer Leiter Jägerhausstr. 22 • 52152 Simmerath Phone +49 2473 601 328 • Fax +49 2473 601 610 • [email protected]

Foundry Machinery Metallurgical Plants and Rolling Mills Thermo Process Technology INDUSTRY 4.0

Outotec Digital Advisory Solutions to enhance metallurgical production

Initial situation

Solution

Benefits in practice

Today’s operation of metallurgical plants is challenging, particularly when handling varying raw material inputs. Strong fluctuations in thermal and electrical energy consumption are observed (example is for an iron ore pelletizing plant over one month). Days with increased energy consumption indicate that lowering to at least the level of the mean consumption is possible. Days with considerable low energy consumption indicate that even more energy saving should be possible.

For the given pelletizing plant example substantial energy savings can be achieved by Outotec Digital Advisory Solutions. Increasing the operator‘s awareness ensures optimised plant performance and reduces the risk of plant malfunctions.

• Reduced thermal and electrical energy consumption

Outotec Digital Advisory Solutions display advice to the operator in case the plant is not running in a recommended operational mode. Corrective action steps can then be applied by the operator who remains in charge of the plant operation.

• Up to $5million savings per year of operational costs due to reduced energy consumption

One reason for fluctuations in the energy consumption was identified in insufficient awareness of operators regarding changes in process boundary conditions. It is challenging for the operators to find and extract the most relevant information from the numerous available plant measurements.

• Improved plant operation and increased plant safety • Increased situation awareness in plant operation

Additionally, Outotec Digital Advisory Solutions are able to indicate plant malfunctions. Combined with monitoring plant equipment it can detect faults early, increase the plant‘s availability and becomes an indispensable feature towards improved plant safety.

Outotec developed advisory solutions to enhance metallurgical production by supporting the operators and optimising the plant‘s day-to-day operation.

Outotec GmbH & Co KG • Tobias Stefan • Business Line Ferrous & Ferroalloys Ludwig-Erhard-Strasse 21 • 61440 Oberursel Phone +49-6171-9693-0 • Fax +49-6171-9693-275 • [email protected]

Foundry Machinery Metallurgical Plants and Rolling Mills Thermo Process Technology INDUSTRY 4.0

Quality Control System (QCS) Continuous Knowlege-Based Process Quality Control

„The way to zero fault production requires a radical step towards Cyber Physical Production Systems.“ Günther Winter, Technology and Innovation EA, Primetals Technologies Germany GmbH

Initial situation

Solution

Benefits in practice

Nowadays, leading steel producers face the challenge to add value to their product portfolio, e.g. to be able to manufacture high precision ultra-low carbon coils for the automotive industry.

Major Quality Control modules for the Smart Factory:

• Automated Root-Cause Analysis and corrective actions to reduce nonconformance products

By applying the concept of Industry 4.0, a new paradigm is required in the complete production chain, especially to fulfil high quality management requirements of automotive customers related to ISO/TS 16949. Overall quality management and control is not limited to the evaluation of the actual quality status during the manufacturing process, but - in case of deviations - to generate corrective actions to achieve maximum yield. All data sources need to be completely digitalized to achieve that goal.

• High frequency acquisition of detailed material and process data from automation/measurement systems and its transformation into valuable quality related information

• Support customer to improve his process know how • Support customer to improve existing products and to develop new products

• Seamless genealogy of production from final product back to raw materials and vice versa

• Support conformance with ISO/TS 16949

• Inline Quality conformance check after each important production step

• Data-driven Operational Excellence helps achieving product quality that was previously unreachable.

• Defect Classification and grading of each material piece • Detailed product disposition, this means depending on quality status, suggestion for next step, such as release, inspect, re-allocate, reject, re-route etc. • Quality related reporting and KPIs • Statistical Process Control (SPC) • Automated Root-Cause Analysis and suggestion of corrective action • Secure Quality related Know How Management

Primetals Technologies Germany GmbH • Werner Klein • Head of Sales IT4Metals Schuhstrasse 60 • 91052 Erlangen Phone +49 9131 724551 • Fax +49 9131 746579 • [email protected]

Foundry Machinery Metallurgical Plants and Rolling Mills Thermo Process Technology INDUSTRY 4.0

Condition Monitoring for AC-Electric Arc Furnaces based on Electrode Control

„The Condition Monitoring System is the link between Electrics, Process and Mechanics.“ Dr. Thomas Matschullat, Primetals Technologies Germany GmbH

Initial situation

Solution

Benefits in practice

The productivity and operating efficiency of the Electric Arc Furnace is influenced by various criteria. Besides the metallurgical part even the electrical, the process and the mechanical factors must be considered in order to reach the optimal way of furnace operation.

The integrated and continuous monitoring of electrical, mechanical and process-related properties enables fast identification and clearance of possible disturbances and cause of defects. Fundamentally, the system monitors the electrical equipment such as the voltage measurement incl. the furnace ground. Thus, unplanned downtimes and damages are avoided.

• Shorter downtimes due to preventive maintenance and faster troubleshooting

On the one hand, there are well-known causes for a bad furnace operation, like a wrong phase sequence (e.g. after a reconstruction) or a defect furnace ground cable. Mostly, these failures are recognized only after a serious damage has already appeared, such as broken electrodes in this case. On the other hand, it can be stated that the profitability has worsened but the reason cannot be found, e.g. in case of stiff hydraulics or when operating with short electrode.

In addition, the trends of the key performance indicators are recorded allowing to recognise changes early and fast, e.g. an increase of energy consumption.

• Higher productivity due to less time in adverse operating modes • Reduced energy and material consumption due to optimisation of operations • Clear presentation of complex data and e-mail alerts

By means of the Condition Monitoring System (CMS), possible causes for disturbances and even potential for improvement can be identified. Due to precise identification, adverse operating modes can be avoided and the preventive maintenance of specific components avoid failures. The modular Condition Monitoring System can be extended for further plant components.

Primetals Technologies Germany GmbH • Gerd Schelbert Sales Technological Products for EAF • Schuhstrasse 60 • 91052 Erlangen Telefon +49 9131 725660 • [email protected]

Foundry Machinery Metallurgical Plants and Rolling Mills Thermo Process Technology INDUSTRY 4.0

Intelligent data-driven models for endpoint prediction at the BOF converter

„We have to use the process- and production data of steelmaking more intensively than before.“ Dr. Karlheinz Blessing, Chairman of the Board, Saarstahl AG

Initial situation

Solution

Benefits in practice

The production of steel is tailored to customer needs. Steel users demand flexibility by the manufacturer (quantity, quality, etc.). Moreover, legislators are demanding greater sustainability in the steelmaking (climate, emissions, recycling, EEG surcharge), steel producers are challenged to develop innovative products for new markets (new steel grades for safe, lighter cars). The BOF converter is a core component in the steel making process in which the final quality is predetermined. In addition, the BOF process has high climate and environmental relevance. Metallurgical process models for optimizing the BOF blowing process are not always efficient. Many process data acquired by sensors remain unused in form of large amounts of data. Mechanisms for self-analysis, self-learning and selfoptimization in the models are missing. Thus, greater adaptability and efficiency are needed in the future.

In cooperation with the Dillinger Hütte Group and the Technical University of Dortmund, Chair for Artificial Intelligence, a data-driven prediction and control system for the BOF converter has been developed. The system is a new, intelligent, modular platform that can predict the status at end of blow. Powerful algorithms allow accurate predictions on the basis of large, consistently collected and processed data streams. The BOF process can be controlled in real time by optimization calculations. Due to the generally applicable structure of the data-driven system many kinds of prediction and control tasks can be realized. Perspectively, Industry 4.0 allows the development of an intelligent, adaptable and efficient automation also for other units of the steel value chain which then interconnect adaptively.

• Efficient modeling for different applications in steel production chain • Optimization of the process chain based on consistent data and process models • Offline process simulation on the PC, thereby allowing rapid adaptation to customer needs • Approx. � 100,000 / a savings with 1 ° C better accuracy in steel temperature prediction

SMS Siemag • Norbert Uebber • Deputy General Manager Models Eduard Schloemann Str. 4 • 40237 Düsseldorf • Phone +49 211 / 881 6521 Fax +49 211 / 881 4997 • [email protected]