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Projects / Programmes source: ARIS

Development of decision-support methods based on smart sensors for steel recycling process in electric arc furnace

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Research activity

Code Science Field Subfield
7.00.00  Interdisciplinary research     

Code Science Field
T125  Technological sciences  Automation, robotics, control engineering 

Code Science Field
2.02  Engineering and Technology  Electrical engineering, Electronic engineering, Information engineering 
Keywords
electric arc furnce, electric steelmaking, process optimization, smart sensors, decision support, process control
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (14)
no. Code Name and surname Research area Role Period No. of publications
1.  31982  PhD Matevž Bošnak  Systems and cybernetics  Researcher  2016 - 2018  50 
2.  30681  PhD Dejan Dovžan  Systems and cybernetics  Researcher  2016 - 2018  68 
3.  37412  PhD Vanja Hatić  Process engineering  Junior researcher  2016 - 2018  43 
4.  21381  PhD Miha Kovačič  Manufacturing technologies and systems  Researcher  2016 - 2018  245 
5.  33584  PhD Qingguo Liu  Process engineering  Researcher  2017 - 2018  34 
6.  27517  PhD Vito Logar  Systems and cybernetics  Head  2016 - 2018  210 
7.  38849  Marjan Maček  Materials science and technology  Researcher  2016 - 2017  0 
8.  36364  PhD Boštjan Mavrič  Process engineering  Researcher  2016 - 2018  105 
9.  05075  PhD Drago Resnik  Electronic components and technologies  Researcher  2017 - 2018  261 
10.  04101  PhD Božidar Šarler  Process engineering  Researcher  2016 - 2018  1,103 
11.  10742  PhD Igor Škrjanc  Systems and cybernetics  Researcher  2016 - 2018  735 
12.  35420  PhD Simon Tomažič  Systems and cybernetics  Researcher  2016 - 2018  39 
13.  23018  PhD Robert Vertnik  Manufacturing technologies and systems  Researcher  2016 - 2018  222 
14.  33167  PhD Andrej Zdešar  Systems and cybernetics  Researcher  2016 - 2018  56 
Organisations (2)
no. Code Research organisation City Registration number No. of publications
1.  0206  Institute of Metals and Technology  Ljubljana  5051622000  5,982 
2.  1538  University of Ljubljana, Faculty of Electrical Engineering  Ljubljana  1626965  27,774 
Abstract
Recycling of the steel in electric arc furnaces (EAFs) represents approximately one third of the global, one half of the European and complete Slovenian production of the steel. Increased demands regarding the quality of the steel, economical, ecologic and technological aspects together with fierce market competition dictate optimized and software-supported manufacturing processes. Due to the nature of the EAF steel-recycling process (high temperatures and electric currents), performance of the crucial process measurements is difficult if not practically impossible. Consequently, monitoring and control of the melting process is performed using the operator's experience and is based on indirect measurements (e.g. power-on time, consumed energy, arc stability etc.) and not on the actual conditions in the EAF (e.g. stage of melting, bath composition, bath temperature), which leads to suboptimal operation, i.e. lower energy and raw material efficiency, increased off gas and CO2 emissions, decreased quality of the steel; and consequently higher operational costs. Furthermore, operational efficiency is influenced also by variable composition of the input materials (steel scrap, non-metallic additives). The issue can be resolved using a combination of advanced process-modelling methods, smart sensors, optimization techniques and decision support methods. The afore mentioned methods involve available process measurements, optimization methods and decision support algorithms, while their integration into a complete software solution forms a so called embedded system for optimal operation of the EAF. The system uses process measurements as inputs, in order to provide a better insight into the current EAF conditions and to suggest the most appropriate action to the user, leading to more efficient operation of the EAF. Using smart sensors based on mathematical models, which are designed in compliance with the physical laws and using available measurements as inputs, crucial process values, which are not measured, can be estimated in parallel to the EAF process with high accuracy. Since the EAF processes are complex, nonlinear and time variant, the development of optimization methods represents a challenging task, which requires the implementation of the most efficient methods for this purpose, i.e. evolutionary and genetic algorithms, fuzzy inference systems and particle swarms. In the frame of this project, some of the already developed mathematical EAF models (electrical, chemical, heat- and mass-transfer) will be used and adopted for the needs of smart sensing and real-time optimization. Due to the complexity of the steel-bath behavior, heat transfers from liquid to solid steel and chemical reactions, some of the existent models will be re-validated, modified and re-parameterized using a more complex computational fluid dynamics (CFD) approach and a material-properties database. The final result of the project will be a simulation environment, comprising of EAF process models, smart sensors, complex optimization methods and decision support algorithms. The developed software will be tested and validated using simulation studies of the current (measured) and optimal (operation according to suggested actions) operation of the EAF, which will demonstrate the differences between both EAF controls and their effect on economic balance. A combination of a real process, decision support system based on computer simulation and process models cannot be found in the EAF operation up to date. The proposed project thus represents an original approach to optimization of the steel-recycling process (higher steel yield, lower energy, raw material and additive consumption, shorter production times, higher steel quality etc.). The introduction of the enhanced EAF control, based on online optimization and decision-support tools, indirectly leads to improved economic, ecological and technological aspects of the mills, with such system installed.
Significance for science
Over the past decades, development of science in the field of modelling, optimization and control has been characterized by the gap between theoretical findings and their transfer to practical applications. Similarly, the appearance of new modelling, simulation, control and optimization strategies is lately becoming very rare. Due to these facts and the case of the proposed project, where various connections between research work and applications exist, the main contribution of the project will be a combination of the existing theoretical principles and approaches to achieve their practical usability and added value (implementation technologies). International relevance of the proposed research is proved by the areas being defined as the priority ones in the EU Horizon 2020 programme (embedded systems, new production technologies, sustainable industry), in European technology platforms (manufacture, embedded systems), as well as in some programs of comparable European groups. Main contributions to the development of science are expected in the following areas: smart sensors, which use mathematical models and real-time measurements to estimate crucial process values, which are not measured, due to the nature of the process, in parallel to the EAF operation, online optimization of the EAF process, which uses smart-sensor estimations of the process values and determines optimal melting scenarios leading to enhanced operation of the EAF, decision support for EAF operation, which uses complex decision-making algorithms and online comparison of possible melting scenarios to determine optimal timings and optimal actions to be performed by the operator, embedded systems, which use all the prior technologies to form a smart EAF operation support system. Both research groups have notable achievements (outstanding scientific achievement for the year 2011 awarded by Slovene Research Agency - ARRS, describing the design and development of EAF process models and simulation environment) and publications (more than 20 papers in JCR SCI journals, book chapters and an international patent issued in the UK) related to modelling, optimization and process control in steelmaking.
Significance for the country
The importance of modelling, simulation and control technology in Slovenia can be seen from many basic Slovenian documents proclaiming the field as the priority one. The mentioned technology has multiplicative effects, which significantly influence energy, resource and pollution reduction, flexibility and quality of production, added value, aspects of sustainable development etc. In the case of this project, the research results will lead to increased competitiveness of the steel industry, as the outcome of the project represents a technological leap forward in the control of the EAF melting process from the current – non-optimal to optimal operation, resulting in either higher steel yields and quality, reduced energy consumption, higher production and thus higher production competitiveness. The field of embedded systems has lately been given special attention abroad and from the European Commission, as the pursuit of the modern industry are both state-of-the-art manufacturing configurations and adequate software (smart) support, since a considerable proportion of those systems are too complex to be optimally controlled by simple physical and computer components. Moreover, the developed methods and the results of this project are applicable to other areas, i.e. high-temperature processes lacking process measurements, where monitoring and control are due to this reason non-optimal (e.g. aluminum recycling). The proposed research has enormous potential, as current state-of-the-art in the field of the EAFs indicates a possibility of 10 – 15 % reduction in costs of electrical energy itself. Assuming an annual production of a smaller EAF (400,000 tons/year) this leads to more than 1 million EUR savings per year for electrical energy itself. In Slovenia approximately 100 companies, employing approximately 10000 employees are involved in production of different alloys besides steelmaking. Their annual revenue sums up to approximately 1.5 billion EUR. The technology developed in the frame of this project can be used also at other high-temperature processes besides EAF steelmaking. According to the current trends in steelmaking, predicting an increase of the EAF use and the simplicity of the proposed solution for implementation to either new or existent production sites, it can be concluded that the proposed project has a significant impact to the economy and society.
Most important scientific results Interim report, final report
Most important socioeconomically and culturally relevant results Interim report, final report
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