Modeling of Steelmaking Processes

Modeling of Steelmaking Processes

Seppo Louhenkilpi (Aalto University, Finland) and Subhas Ganguly (National Institute of Technology Raipur, India)
DOI: 10.4018/978-1-5225-0290-6.ch013
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In the field of experiment, theory, modeling and simulation, the most noteworthy progressions applicable to steelmaking technology have been closely linked with the emergence of more powerful computing tools, advances in needful software's and algorithms design, and to a lesser degree, with the development of emerging computing theory. These have enabled the integration of several different types of computational techniques (for example, quantum chemical, and molecular dynamics, DFT, FEM, Soft computing, statistical learning etc., to name a few) to provide high-performance simulations of steelmaking processes based on emerging computational models and theories. This chapter overviews the general steps and concepts for developing a computational process model including few exercises in the area of steel making. The various sections of the chapter aim to describe how to developed models for various issues related to steelmaking processes and to simulate a physical process starts with the process fundaments. The examples include steel converter, tank vacuum degassing, and continuous casting, etc.
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1. Introduction

Computational models are nowadays increasingly accepted in steel industry to simulate metallurgical processes, such as converters, ladle treatments, continuous casting etc. These models are typically used for process designing, process optimization, process control, forecasting etc. All these day to day largely perfumed industrial metallurgical processes are obviously involved many complex physicochemical reactions. A lot of specific single models for this field have been evolved for specific purposes (Yu & Louhenkilpi 2013; Yu, Miettinen & Louhenkilpi 2013; Yu, Miettinen, & Louhenkilpi n.d.; Yu, Miettinen, & Louhenkilpi 2014; Miettinen, Louhenkilpi & Holappa 1996; Braun & Pfeifer 2007; Louhenkilpi et. al 2005; Clark, Wagner & Trousset 2003; Sahai & Emi 1996; Mazumdar, Yamanoglu & Shankarnarayanan, & Guthrie 1995; Kuzmin & Turek 2004; Charkraborty & Sahai 1991), and stochastic and/or hybrid nature of models based on physicochemical laws but including these non-physical features.

In the very recent times, more attention has been attracted on developing real-time models which can be available for on-line use in industry for development of reliable process control system(Louhenkilpi, Laitinen & Nieminen 1993; Louhenkilpi et. al 2005a; Louhenkilpi et al 2005b. Real-time calculation of an industrial process offers many new possibilities in on-line process control (Allendorf et al 1998). In real-time calculation, many practical requirements will be set for the model. The computing time must, for instance, be short enough and special process conditions must be included in the model. Due to the increasing power of modern computers available, the requirements concerning the computing time can today, however, be met more and more easily.

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