Modeling of Steelmaking Processes

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.

Data-Driven Bi-Objective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain

Data-driven modeling and optimization are now of utmost importance in computational materials research. This chapter presents the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are particularly suitable for modeling and optimization tasks pertinent to noisy data. In both the approaches a tradeoff between the accuracy and complexity of the candidate models are sought, ultimately leading to some optimum tradeoffs. These novel strategies are tailor-made for constructing models of right complexity, excluding the non-essential inputs. They are constructed to implement the notion of Pareto-optimality using a predator-prey type genetic algorithm, providing the user with a set of optimum models, out of which an appropriate one can be easily picked up by applying some external criteria, if necessary. Several materials related problems have been solved using these algorithms in recent times and a couple of typical examples are briefly presented in this chapter.

Multi-Objective Evolutionary Algorithms

The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component. This chapter exemplifies an effective approach to design particle reinforced mould materials by solving the inherent multi-objective optimization problem associated with soft tooling process using evolutionary algorithms. In this chapter, first a brief introduction of multi-objective optimization problem with the key issues is presented. Then, after a short overview on the working procedure of genetic algorithm, a well- established multi-objective evolutionary algorithm, namely NSGA-II along with various performance metrics are described. The inherent multi-objective problem in soft tooling process is demonstrated and subsequently solved using an elitist non-dominated sorting genetic algorithm, NSGA-II. Multi-objective optimization results obtained using NSGA-II are analyzed statistically and validated with real industrial application. Finally the fundamental results of this approach are summarized and various perspectives to the industries along with scopes for future research work are pointed out.

Computational Design of Microstructure

During the last couple of decades, treatment of microstructure in materials science has been shifted from the diagnostic to design paradigm. Design of microstructure is inherently complex problems due to non linear spatial and temporal interaction of composition and parameters leading to the target properties. In most of the cases, different properties are reciprocally correlated i.e., improvement of one lead to the degradation of other. Also, the design of microstructure is a multiscale problem, as the knowledge of phenomena at range of scales from electronic to mesoscale is required for precise composition-microstructure-property determination. In the view of above, present chapter provides the introduction to computationally driven microstructure engineering in the framework of constitutive length scale in microstructure design. The important issues pertaining to design such as phase stability and interfaces has been explained. Additionally, the bird-eye view of various computational techniques in order of length scale has been introduced, with an aim to present the picture of combination of various techniques for solving microstructural design problems under various scenarios.

Artificial Neural Network and Its Application in Steel Industry

The recent developments in computational intelligence has enhances the applicability of empirical modelling in different areas particularly in the area of machine learning. These new approaches are based on analysing the data about a system, in particular finding connections between the system state variables (input, internal and output variables) without having precise knowledge about the physical behaviour of the system. These data driven methods explain advances on conventional empirical modelling and include contributions from many overlapping fields like Artificial Intelligence (AI), Computational Intelligence (CI), Soft Computing (SC), Machine Learning (ML), Intelligent Data Analysis (IDA), and Data Mining (DM). The most popular computational intelligence techniques used in process modelling of steel industry includes neural networks, fuzzy rule-based systems, genetic algorithms as well as approaches to model integration. This chapter describes mainly the application of Artificial Neural Network (ANN) in steel industry. ANN has extensively used in improving and controlling different processes of steel industry like steel making, casting and rolling which lead to indirect energy savings through reduced product rejects, improved productivity and reduced down time. The efficiency of artificial neural network tool in handling steel plant processes has been discussed in detail. ANN based models are found to be very potential to handle very complex, dynamic and non-linear problems.

Imprecise Knowledge and Fuzzy Modeling in Materials Domain

This chapter highlights the usage of imprecise knowledge of materials systems using fuzzy inference systems. Experts have knowledge of complex materials systems in the imprecise linguistic form. But due to lack of phenomenological relations, material engineers are compelled to depend on empirical models for practical complex systems. This limitation could be overcome to a certain extent through the method of utilizing this imprecise knowledge with the help of fuzzy logic. The case studies presented here have demonstrated that systems with imprecise knowledge but with sparse data could be modeled successfully in this approach.

Informatics-Based Approaches for Accelerated Discovery of Functional Materials

This chapter is aimed at readers interested in the topic of informatics-based approaches for accelerated materials discovery, but who are unfamiliar with the nuances of the underlying principles and various types of powerful mathematical tools that are involved in formulating structure–property relationships. In an attempt to simplify the workflow of materials informatics, we decompose the paradigm into several core subtasks: hypothesis generation, database construction, data pre-processing, mathematical modeling, model validation, and finally hypothesis testing. We discuss each task and provide illustrative case studies, which apply these methods to various functional ceramic materials.

Integrated Computational Materials Engineering for Determining the Set Points of Unit Operations for Production of a Steel Product Mix

Manufacturing a steel product mix (bar, rod, sheet) involves a series of unit operations - primary steel making, secondary steel making (ladle refining and tundish operation), continuous casting, reheating, rolling and annealing. The properties of the final product depend significantly on how each unit operation is carried out. Each unit operation must be operated to meet the requirements of the subsequent operations. The requirements imposed on a particular unit operation are often conflicting and compromises must be made. Also, there is high degree of uncertainty in the operating parameters of each unit operation, which may lead to considerable deviations from the anticipated performance. To ensure that the final quality specifications of the product is not sacrificed and the customer requirements are met, it is essential to manage the conflict and uncertainty involved in each unit operation of the manufacturing process. In this chapter, we illustrate the use of compromise Decision Support Problem (cDSP) construct and ternary plots to overcome the challenges involved in one of the unit operations, namely, the tundish. The construct can be instantiated for other unit operations to cover the entire manufacturing cycle. Exploring the effects of system variables for each process step through experiments and plant trials is time consuming and very costly. The proposed method allows for faster design exploration of the process and thereby provides a reduced search space to a process designer. The process designer, with reduced experimentation requirements, can explore the narrowed search space to find the operating set points for a tundish. This, in turn, reduces the time and cost involved in production of a steel product mix with a new grade of steel in industry.

Micromechanical and Finite Element Modeling for Composites

The major areas of applications of the composite materials today encompass fields as wide as wind energy to marine to construction to aerospace to strategic areas. Apart from such specialized fields the major composite market blooms out of their extensive exploitations in the automotive, sporting goods, pipes, tanks, chemicals, fertilizers and many other industries. As compared to the conventional materials, the composites offer several unique advantages. The list of such advantages may include as a typical example the higher tensile strength, the lighter weight, the greater corrosion resistance, the better surface finish and the easier processability. It is interesting to note that in 2011, the global composite materials market size was $19.6 billion and the same is estimated now to reach approximately $34.1 billion in 2018. This amounts to a Compounded Annual Growth Rate (CAGR) of about 10.5% percent. This huge market growth of composites will require a thorough knowledge of the mechanics and mechanical properties of a wide variety of composites under a still wider variety of application dependent constraints. That is why, just to make an humble beginning in this direction, the basics of the composite materials and it's varieties, are briefly described in this Chapter. In addition, the analytical methods of continuum micromechanics have been touched upon. Keeping in view the complex microstructure of most of the composites, and the effect that such complexities can have in the resultant properties, the numerical approaches to continuum micromechanics has been highlighted with emphasis on the fundamentals of the Finite Element Analysis (FEA) based methods. Further, the concepts involved in modelling of the “Failure of composites” have been elucidated with some interesting case studies.

Ab Initio-Based Stochastic Simulations of Kinetic Processes on Surfaces

We briefly present the theoretical framework of a hierarchical multi-scale approach, which is an ab initio-based stochastic method, and its applications to several chemical/physical kinetic processes on metallic surfaces. We first introduce necessary theoretical basis of ab initio and Monte Carlo (MC) methods, and then illustrate different Monte Carlo algorithms for important ensembles, including canonical and grand canonical ensembles. In the following section, we describe two important protocols which are essential to integrate ab initio data and MC models. Two examples are presented in order to elucidate the power of this multi-scale approach. The first example focuses on the combination of kinetic Monte Carlo and transition state theory. We discuss the detailed processes of performing kinetic Monte Carlo simulation on atomic diffusion on alloyed surface, including some technical aspects. In the second example, we presents a different way to account for the local environment-sensitive metal-catalyzed O2 dissociation reactions using combinatory techniques including cluster expansion and grand canonical Monte Carlo methods. This approach provides steady-state rates and rate derivatives that are comparable with experiments. Moreover, the connection between the feasible mechanisms and the observed kinetic behaviors can now be built.