This paper represents the elements and the use of the upgraded simulation system, developed in the last half decade for Štore Steel billet continuous caster. The simulation system is used in the context of the state-of-the-art automation and information of the twenty five year old three strand Concast billet continuous caster for dimensions square 140 and 180 mm with the capacity of 160 000 tons/year. The simulation system is used in the off-line and on-line modes. The off-line mode is used in order to set the proper process parameters and to calculate the temperature field, macrosegregation, and grain structure of the billet. It is also used to calculate the changes in the caster design such as the secondary cooling and the position of the SEN. The on-line model is used in automatic casting control system. The paper represents an update of our BHM publication of 2005 (Application of Continuous Casting Simulation at Štore Steel, BHM, Vol. 150, No. 9, 300–306). Comment: Paper demonstrates leading results of the project group and their impact in industry regarding physical modelling of the metallurgical processes.
F.09 Development of a new technological process or technology
COBISS.SI-ID: 2761467Artificial neural networks (ANNs) are employed as an alternative to physical modelling for calculation of the relations between the production path process parameters (melting of scrap steel and alloying, continuous casting, hydrogen removal, reheating, rolling, and cooling on a cooling bed) and the final product mechanical properties (elongation, tensile strength, yield stress, hardness after rolling, necking) of steel semi products. They provide a much faster technique of response evaluation complementary to physical modelling. The Štore Steel company process path for production of steel bars is used as an example for demonstrating the new approach. The applied ANN is of a multilayer feedforward type with sigmoid activation function and supervised learning. The entire set of 123 process parameters has been reduced to 34 influential ones and 1879 data sets from the production line have been used for learning. The results of parametric studies performed on the ANN based model seem consistent with the expectations based on industrial experiences. However, further improvements in data acquisition and analytical procedures are foreseen in order to obtain a methodology, reliable enough for use in the everyday industrial practice. The methodology seems to be for the first time applied in the through process modelling of steel production. Comment: Paper demonstrates leading results of the project group and their impact in industry regarding the computational intelligence modelling of the metallurgical processes.
F.09 Development of a new technological process or technology
COBISS.SI-ID: 2601467The dissertation for the first time exposes solution of engineering turbulence models and models of solidification with turbulence in the melt by a meshless method. Numerous test cases are presented. The most nonlinear cases are verified by comparison with the results of the commercial package FLUENT. A model for continuous casting of steel is developed. The influence of process parameters on velocity and temperature field in the billet is simulated. Comment: The dissertation was proclaimed by ECCOMAS (European Committee for Computational Methods in Applied Sciences and Engineering) among the best in Europe in 2010 and awarded. This demonstrates the excellence of a project group member and excellence in education in the project group.
E.02 International awards
COBISS.SI-ID: 1516283A simple Lagrangean type traveling slice model has been applied for the prediction of the relations between process parameters, macrosegregation and solidification grain structure formation (equiaxed to columnar and columnar to equiaxed transition) during the continuous casting process of steel billets. The main advantage of the slice model is its very fast calculation time in comparison with the complete 3D heat and fluid flow model which might need calculation time, measured in days. The slice models thus allows for fast optimisation and even for on-line simulation. The heat and species transfer models are based on the mixture continuum assumptions with Lever solidification rule and enhanced thermal and solutal diffusivities for heuristic accounting of fluid flow effects. The grain structure evolution model is based on the Gaussian nucleation rule, and KGT growth model, coupled to the macroscopic heat and species transfer models. The heat and species transfer models are solved by the meshless technique by using local collocation with radial basis functions. The grain structure evolution model is solved by the point automata technique, a novel meshless variant of the cellular automata method. A comparison of the results with the experimental data for steel grade 51CrV4 is shown in terms of macrosegregation and grain structure across the billet. Simulations and comparisons have been carried out for nominal casting conditions, reduced casting temperature, and reduced casting speed. The model predicts surprisingly well the qualitative features of the macrosegregation and grain structure patterns. The model of hot rolling consists of the coupled thermal model, mechanical model and model for node positioning and manipulation. We present rolling of square steel billet to circular bar. Possible refinements of the model with respect to other physical mechanisms are discussed. Comment: We have in addition to the presented invited keynote lecture at APCOM2013 conference in Singapore, which will be published in EABE journal, organized a minisimposium "MS-86 Computational modelling of casting, rolling and heat treatment processes".
B.04 Guest lecture
COBISS.SI-ID: 3129339Štore Steel ltd faces a problem of production of a huge amount (approximately 1400) of different steel compositions in a relatively small quantities (approximately 15 tons). This production is performed in batches of predetermined quantities (50-53 tons). The purpose of this paper is to present the methodology for optimizing the production of predetermined steel grades in predetermined quantities before a customer's set deadline in such a way as to reduce the non-planned and ordered quantities with the date before the deadline and minimize the number of batches. The genetic algorithm was used for the optimization. The results of the research have been used in practice since 2006 with reducing the non-planned and ordered quantities from 17.17% to 10.12% since then. Comment: The paper demonstrates the use of the developed artificial intelligence methods in practice. Huge saving are demonstrated.
F.13 Development of new production methods and tools or processes
COBISS.SI-ID: 1817595