Josef Stetina

Bio: Josef Stetina is an academic researcher from Brno University of Technology. The author has contributed to research in topics: Continuous casting & Slab. The author has an hindex of 7, co-authored 79 publications receiving 226 citations.

Optimization of the quality of continuously cast steel slabs using the firefly algorithm optimizacija kakovosti kontinuirno lite jeklene plo(^e z uporabo algoritma "firefly"

Abstract: The ambition to increase both the productivity and the product quality in the continuous casting process, led us to study new, effective mathematical approaches. The quality of the steel produced with the continuous casting process is influenced by the controlled factors, such as the casting speed or cooling rates. The appropriate setting of these factors is usually obtained with expert estimates and expensive experimental runs. This paper describes an algorithm for obtaining a black-box-type solution which maintains a high production rate and the high quality of the products. The core of the algorithm is our original numerical model of 2D temperature field designed for the real caster geometry. The mathematical model contains Fourier-Kirchhoff equation and includes boundary conditions. Phase and structural changes are modeled by the enthalpy computed from the chemical composition of the steel. The optimization part is performed with a recently created heuristic method, the so-called Firefly algorithm, in which the principles of searching for optimal values are inspired by the biological behavior of fireflies. Combining the numerical model and heuristic optimization we are able to set the controlled values and to obtain high-quality steel that satisfies the constraints for the prescribed metallurgical length, core and surface temperatures. This approach can be easily utilized for an arbitrary class of steel only by changing its chemical composition in the numerical model. The results of the simulations can be validated with real historical data in order to compare the relationship between the temperature field and the final product quality. Ambicije za pove~anje produktivnosti in kakovosti kon~nega proizvoda pri kontinuirnem ulivanju sta nas pripeljala do {tudija novih u~inkovitih matemati~nih prijemov. Na kakovost jekla, proizvedenega s kontinuirnim ulivanjem, vplivajo {tevilni nadzorovani dejavniki, kot sta npr. hitrost ulivanja in ohlajanja. Ustrezno dolo~anje teh dejavnikov je navadno povezano s strokovnimi ocenami in dragimi poizkusi. Prispevek opisuje algoritem za vrsto re{itev za ohranjanje visoke stopnje proizvodnje in visoke kakovosti izdelkov. Jedro algoritma je na{ prvotni numeri~ni model 2D-polja temperature, namenjen ulivalni geometriji. Ta matemati~ni model vsebuje Fourier-Kirchhoffovo ena~bo in tudi robne pogoje. Fazne in strukturne spremembe so bile modelirane z entalpijo, izra~unano iz kemijske sestave jekla. Optimizacijski del je bil izveden z nedavno narejeno hevristi~no metodo, s tako imenovanim algoritmom Firefly, kjer na~ela iskanja optimalnih vrednosti temeljijo na biolo{kem

Optimal Control Algorithm for Continuous Casting Process by Using Fuzzy Logic

Abstract: A supervision algorithm for controlling of continuous casting (CC) process is presented. The control strategy is based on the observation of temperature distribution through the casting strand. The algorithm is composed of two parts, an original 3D transient numerical model of the temperature field and the fuzzy-regulation model. The numerical model calculates and predicts the temperature distribution while the fuzzy-regulation model tracks the temperature in specific areas and tunes the casting parameters such as the casting speed, the cooling intensities in the secondary cooling, etc. The main goal is to keep surface and core temperatures in the specific ranges corresponding with the hot ductility of steel and adequately reacts on the variable casting conditions. The results show good and robust control behavior, fast response to dynamic system changes and general applicability for any CC process.

High Quality Steel Casting by Using Advanced Mathematical Methods

Abstract: The main concept of this paper is to utilize advanced numerical modelling techniques with self-regulation algorithm in order to reach optimal casting conditions for real-time casting control. Fully 3-D macro-solidification model for the continuous casting (CC) process and an original fuzzy logic regulator are combined. The fuzzy logic (FL) regulator reacts on signals from two data inputs, the temperature field and the historical steel quality database. FL adjust the cooling intensity as a function of casting speed and pouring temperature. This approach was originally designed for the special high-quality high-additive steel grades such as higher strength grades, steel for acidic environments, steel for the offshore technology and so forth. However, mentioned approach can be also used for any arbitrary low-carbon steel grades. The usability and results of this approach are demonstrated for steel grade S355, were the real historical data from quality database contains approximately 2000 heats. The presented original solution together with the large steel quality databases can be used as an independent CC prediction control system.

Numerical optimization of the method of cooling of a massive casting of ductile cast-iron

Abstract: The numerical models of the temperature field of solidifying castings, according to various authors, have been observing two main goals ? directed solidification as the basic assumption for the healthiness of a casting and the optimization of the technology while maintaining the optimal product properties. The achievement of these goals is conditioned by the ability to analyze and, successively, to control the effect of the deciding factors, which either characterize the process or accompany it. An original application of ANSYS simulated the forming of the temperature field of a massive casting from ductile cast-iron during the application various methods of its cooling using steel chills. The numerical model managed to optimize more than one method of cooling but, in addition to that, provided serious results for the successive model of structural and chemical heterogeneity, and so it also contributes to influencing the pouring structure. The file containing the acquired results from both models, as well as from their organic unification, brings new and, simultaneously, remarkable findings of causal relationships between the structural and chemical heterogeneity (i.e. between the sizes of the spheroids of graphite, the cells, density of the spheroids of graphite, etc.) and the local solidification time in any point of the casting. The determined relations therefore enable the prediction of the face density of the spheroids of graphite in dependence on the local solidification time. The calculated temperature field of a two-ton 500x500x1000 mm casting of ductile cast-iron with various methods of cooling has successfully been compared with temperatures obtained experimentally. The casting was cast in sand mould. The calculated model of the kinetics of the temperature field of the casting was verified during casting with temperature measurements in selected points.This has created a tool for the optimization of the structure with an even distribution of the spheroids of graphite in such a way so as to minimize the occurrence of degenerated shapes of graphite, which happens to be one of the conditions for achieving good mechanical properties of castings of ductile cast-iron.

A comprehensive review of firefly algorithms

Abstract: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

Cuckoo Search and Firefly Algorithm

Abstract: Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while cuckoo search (CS) was developed by Xin-She Yang and Suash Deb in 2009. Both algorithms have been found to be very efficient in solving global optimization problems. This chapter provides an overview of both cuckoo search and firefly algorithm as well as their latest developments and applications. We analyze these algorithms and gain insight into their search mechanisms and find out why they are efficient. We also discuss the essence of algorithms and its link to self-organizing systems. In addition, we also discuss important issues such as parameter tuning and parameter control, and provide some topics for further research.

Weighted Superposition Attraction (WSA)

Abstract: A novel swarm intelligence based algorithm inspired by superposition principle and field attraction for global optimization.High converging capability.Extensive computational study is presented for solving many test problems with success. This paper is the first one of the two papers entitled Weighted Superposition Attraction (WSA), which is based on two basic mechanisms, superposition and attracted movement of agents, that are observable in many systems. Dividing this paper into two parts raised as a necessity because of their individually comprehensive contents. If we wanted to write these papers as a single paper we had to write more compact as distinct from its current versions because of the space requirements. So, writing them as a single paper would not be as effective as we desired.In many natural phenomena it is possible to compute superposition or weighted superposition of active fields like light sources, electric fields, sound sources, heat sources, etc.; the same may also be possible for social systems as well. An agent (particle, human, electron, etc.) may be supposed to move towards superposition if it is attractive to it. As systems status changes the superposition also changes; so it needs to be recomputed. This is the main idea behind the WSA algorithm, which mainly attempts to realize this superposition principle in combination with the attracted movement of agents as a search procedure for solving optimization problems in an effective manner. In this current part, the performance of the proposed WSA algorithm is tested on the well-known unconstrained continuous optimization functions, through a set of computational study. The comparison with some other search algorithms is performed in terms of solution quality and computational time. The experimental results clearly indicate the effectiveness of the WSA algorithm.