Advanced Techniques for Process Optimization in Iron and Steelmaking: Modeling and Monitoring Innovations

Chair: Doc. Weihong Yang, KTH Kungliga Tekniska Högskolan

Members of the grading committee:

Prof. Baijun Yan, University of Science and Technology Beijing,  

Prof. Maria Aquareles Carrero, University de Girona,    

Dr. Thomas Helander, Kanthal AB

Abstract

The iron and steel industries are well-known contributors to global CO2 emissions, accounting for nearly 7% of the total, and are therefore in urgent need of technological advances to improve the efficiency of their current processes and support their transition to more sustainable practices. To address some of the needs, this study introduces advanced techniques aiming at two key objectives: first, pioneering computationally efficient gas-solid flow models for ironmaking blast furnaces to accelerate prediction of their interior state and enhance process understanding beyond current approaches; and second, advancing the functionality and practical application of infrared (IR)-based systems to optimize slag and process control during steelmaking processes.

An important step in modeling of the blast furnace interior state is the simulation of coupled gas-solid flow. Existing numerical-based models employing Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) approaches are limited in their application due to high computational demand, arising from the complexity of the blast furnace process. Alternatively, asymptotic methods were employed to simplify the Euler-Euler formulation for modeling gas-solid flow in an ironmaking blast furnace to yield essentially the same results as numerical methods, but at a much-reduced computational cost. As an initial step towards full-scale modelling, a one-dimensional (1D) gas-solid flow model was analysed in this work − first under steady-state conditions, and later for a transient case, which better represents the dynamic nature of the blast furnace process. A preliminary analysis of the nondimensionalized one-dimensional equations under steady-state conditions revealed the need for boundary layers near both the gas and solid inlets to ensure accurate flow predictions. Notably, the boundary layer near the gas inlet was significantly wider than that near the solid inlet. The method of matched asymptotic expansions was then used to derive asymptotic solutions in each layer, in addition to the leading-order solution in the bulk of the domain. A key finding from the 1D steady-state model is the strong influence of solid viscosity on the behavior of the solution, resulting in cases where the solution can be unique, non-unique or non-existent. Insights from the 1D steady-state model were instrumental in enhancing a previously developed 2D axisymmetric steady-state model and in laying the groundwork for an asymptotic framework for the subsequent transient model presented in this work. The transient model incorporated time-varying boundary condition at the solid inlet to mimic the blast furnace charging practice. The analysis of the transient formulation using asymptotic methods indicated the same boundary layer structure as in the steady-state case. The transient solution exhibited a quasi-steady state behaviour and simply alternated between two independent steady-state profiles, corresponding to the time-dependent boundary condition. The solid viscosity continued to influence the solution even in the transient model. Overall, the asymptotically reduced 1D models yielded results that showed good agreement with results from numerical simulations, providing the basis for a computationally efficient approach towards modeling the blast furnace process with increasing complexities. Further modifications in the transient Euler-Euler model formulation are recommended with a particular focus on its potential to capture the layered burden structure that is an integral feature of the blast furnace interior state.

This latter part of this thesis addresses the limitations of the currently existing IR-based systems in their functionality concerning slag composition assessment and in their applicability in slag and process control in small- and medium-scale industries. The potential of IR-based systems in slag composition assessment was explored by experimentally determining slag emissivities within the wavelength range of 7.5 − 14 μm at high temperatures, both in industry and in the laboratory. Slag emissivities in the range from 0.64 to 0.68, as evaluated at metal-making temperatures by the industrial trials, were likely affected by apparent surface inhomogeneities in slag composition and temperature that are difficult to control in industrial environments, emphasizing the need for laboratory experiments under well-controlled conditions. Consequently, lab-scale experiments were conducted to determine emissivity of three slag samples from the quaternary system of CaO-Al2O3-SiO2-MgO, representative of ladle slag, at 1773 K. The calculated emissivities from the lab-scale trials ranged from 0.75 to 0.87 at the target temperature, with repeatability best observed in the slag which was completely molten during the investigations. In contrast, the other samples exhibited variations in their emissivities, likely due to the presence of solid phases at the target temperature. The solid phases introduced compositional inhomogeneities on the surface, rendering the surface unstable leading to their emissivity variations. The findings from the lab-scale investigations therefore stressed the need for fully liquid slag samples, ensuring a stable surface with uniform composition and temperature for accurate emissivity determination. While the lab-scale experiments highlighted a discernible effect of slag composition on emissivity, further research is needed including investigations of other typical slag compositions at different temperatures to study the potential of IR-based systems for online slag composition assessment. Moreover, the final phase involving the enhancement of the applicability of IR-based systems was addressed via the development of an Operator Vision Assistance System (OVIAS), designed with key components to enhance operators’ vision in real-time visualization. Industrial testing of a prototype of OVIAS, aimed at optimal imaging of the molten steel surface in an induction furnace and a ladle demonstrated the potential of OVIAS in empowering operators with enhanced visualization and better decisions in optimizing slag control practices such as slag coagulant additions and process control operations such as de-slagging. Industrial calibration of OVIAS, although conducted in the study, met with challenges and needs further investigations using a laboratory scale furnace. Overall, OVIAS presents a cost-efficient, flexible alternative to current expensive IR camera systems in supporting small- and medium-scale industries with improved slag and process control practices during steelmaking.

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