Steel is the mother of industry, and is also an energy consumption intensive industry. Since the energy crisis, the various countries iron and steel plants positively take each energy frugal measure in order to reduce the fuel and the electric power consumption. In the iron and steel plant comparatively consumes the energy the system regulation equipment is the reheating furnace, so to save energy in a reheating furnace and reduce the energy consumption become one of important topics. The reduction consumes energy the countermeasure aspect may by analyze the heat transfer model and the change reheating furnace characteristic begins. In this thesis, we will build a simulation system of reheating furnace to analysis the temperature change of slab in a reheating furnace and discussion energy consumption factor. And then we use the thermal balance model to analysis the situation of fuel consumption. According to different conditions, we want to discuss the relationships energy consumption and increasing temperature of slab inside furnace.

Nowadays, many industries are searching new and effective substitutes for traditional energy resources. Heavy industries are not excluded. In order to use the most of the limited resources, they look for new techniques and new energy management systems which are efficient in energy consumption. Studying in saving energy for reheating furnace and in approving operation conditions may help heavy industries to reduce costs and to enhance quality of products. ¡@¡@Inevitably, the inner compositions of alloy ingots are not uniform during continuous casting. To solve this problem, we usually do heat treatment on alloy ingots before casting. Besides, to ensure the quality and stability of alloy ingots in heat treatment, we need an effective tool to monitor the temperature distribution on ally ingots. Moreover, the precise control over energy consumption can improve the efficiency of reheating furnace and can reduce costs. ¡@¡@This study focuses on alloy ingots. We simulate heat transfer by numerical methods and construct the integrated software to simulate the characteristics of reheating furnace in batch type operation. Analyses in the temperature distribution and energy consumption of alloy ingots in reheating process are also included. Besides, we choose some parameters, which might affect the energy consumption and try to find the best level parameter composition in reheating furnace analysis by Taguchi method. Some results are shown below: ¡]1¡^When temperature rising process starts, our model provides ¡@ ¡@reasonable and exact prediction in one or two hours. ¡]2¡^Reheating furnace treatment ability is the most important ¡@ ¡@control factor (out of eight) in this study. ¡]3¡^We can reduce around 3% of the consumption with the ¡@ ¡@parameters obtained by Taguchi method. ¡]4¡^The soaking degrees are all below 0.01¢J after heat treatment ¡@ ¡@on alloy ingots. ¡]5¡^We change the temperature rising rate to simulate the situation ¡@ ¡@of heated alloy ingots. We find that the higher temperature ¡@ ¡@rising rate is, the more significant temperature difference ¡@ ¡@inside alloy ingots is. On the other hand, the lower rate would ¡@ ¡@increase the soaking degree.

Abstract Steel is the mother of industry, and is also an energy consumption intensive industry. Especially for the rolling mill, the energy consumption in a reheating furnace take a half, so to save energy in a reheating furnace and reduce the energy consumption become the major issue in the future. The reheating furnace used in general process of steel producing can divided into two types-Continuous type and Batch type- through its ability of steel rolling¡Napproach and its demand. In this thesis, our research target is the batch type reheating furnace, we based on theory of heat transfer in a reheating furnace to build a simulation system of reheating furnace and calculate the temperature-time curve of slab and its heat flux. And then we use the thermal balance model to analysis the situation of fuel consumption. According to different operated conditions, we want to discuss the relationships between energy consumption and increasing temperature of slab inside furnace¡Nsoaking degree¡C From analysis result, we can find that fixed the total time in furnace, the longer of heating time is, the lower of average temperature of slab and the higher of temperature difference of discharge slab are. But in the process of increasing temperature, the max temperature difference of slab is lower. Using the exhaust gas to preheat air through the heat exchanger, we can find that when the temperature of preheated air is increasing, the heat loss of exhaust gas and fuel consumption will be lower. When air-fuel ratio is getting higher, the temperature difference in the process of increasing temperature will be getting lower, and it will be higher as the slab soaks. When air-fuel ratio is increasing, the quantity of fuel consumption will increase too. In respect of refractory material, heat loss of furnace and accumulation of heat in refractory material caused by using the refractory cottons is less than using the refractory bricks. Besides that, the different fuel will only affect the quantity of fuel consumption, not increasing temperature of slab and soaking degree.

No description available.

<p>Steel heating and melting processes consume the majority of the energy used in advanced short-process steelmaking practices. Economic and environmental pressures from energy consumption drive the research to improve the furnace operation efficiency and energy efficiency. The goal of this research is to utilize computational fluid dynamics (CFD) modeling to provide useful tools and recommendations on the steel heating and melting practices in the steelmaking process. The steel slab reheating process, the steel scrap preheating process and the steel scrap melting process are studied.</p> <p> </p> <p>A transient three-dimensional (3-D) CFD model was developed to simulate the flow characteristics, combustion process and multi-scale, multi-mode heat transfer inside the reheating furnace. The actual geometry of an operating industrial furnace was used and typical operating conditions were simulated. Specific walking speeds of slabs in production were modeled using a dynamic mesh model which is controlled by a user-defined function (UDF) solved using ANSYS Fluent. Fuel variations at different zones with respect to time were also considered. The model was validated with instrumented slab trials conducted at the SSAB Mobile (Alabama) mill. The temperature field in the furnace and the temperature evolution of a slab predicted by the CFD model are in good agreement with those obtained from the instrumented slab trials. Based on the simulation results, the slab reheating process and the temperature uniformity of a slab at discharge were able to be properly evaluated. In addition, a comprehensive two-dimensional (2-D) numerical heat transfer model for slab reheating in a walking beam furnace was developed using the finite difference method. An in-house code was developed. The model is capable of predicting slab temperature evolution during a reheating process based on real time furnace conditions and steel physical properties. The model was validated by using mill instrumented slab trials and production data. The results show that the temperature evolution predicted by the model is in good agreement with that measured by the thermocouples embedded in the instrumented slab. Compared with 3-D CFD simulation of a reheating process, this 2-D heat transfer model used for predicting slab temperature evolution requires less computing power and can provide results in a few seconds. A graphical user interface was also developed to facilitate the input and output process. This is a very convenient and user-friendly tool which can be used easily by mill metallurgists in troubleshooting and process optimization.</p> <p> </p> <p>CFD models for steel scrap preheating and melting processes by the combined effects of the heat source from both oxy-fuel combustion and electric arc were also developed. The oxy-fuel burners firing natural gas (NG) are widely used in EAF operation during the scrap preheating and melting stages. In order to understand the role of oxy-fuel combustion and potentially increase the energy input from NG while decreasing the electricity consumption, numerical simulation of scrap preheating by oxy-fuel combustion in an EAF was firstly conducted. A 3-D CFD model was developed with detailed consideration of gas flow, oxy-fuel combustion, heat transfer between gas and solid scrap and scrap oxidation. The model was validated by a small-scale experimental study and applied onto a real-scale EAF.</p> <p> </p> <p>Scrap melting in bath is comprehensively studied with a CFD model developed to simulate the melting in bath process under given operating conditions. Two sub-models were developed for model integration: steel melting model and coherent jet model. The multiphase volume of fluid (VOF) model and the enthalpy-porosity technique are applied to describe the steel melting process. The coherent jet model calculates the gas jet momentum and is integrated into the flow model to calculate its effect on the fluid flow in the bath. The electric arc was treated as a heat flux to represent the heat transfer from the electric arc during the melting process. Model validations were conducted for each sub-model to ensure their accuracy. Parametric studies were also carried out to obtain useful information for real practice. </p><p>Overall, the CFD models developed in this research work have demonstrated value in improving energy efficiency in the energy-intensive steelmaking processes. The developed CFD models also provide insights for better understanding of the multi-physics processes.<br></p> <p> </p>

In this thesis, a proof of concept of a digital twin of a type of reheating furnace, the walking beam furnace, is presented. It is created by using a machine learning concept called a neural network. The digital twin is trained using real data from a walking beam furnace located in Swerim AB, Luleå, and is taught to predict the temperature in the furnace using air, fuel and pressure as inputs. The machine learning technique used is an artifical neural network in the form of a multilayer perceptron model. The resulting model consists of 3 layers, input, hidden and output layer. The hyperparameters is decided by using grid search cross validation. The hyperparameters chosen to use in this thesis was amount of epochs, optimizer, learning rate, batch size, activation function, regularizer and amount of neurons in the hidden layer. The final settings for these can be found in table. The digital twin is then evaluated comparing predicted temperatures and actual temperatures from the measured data. The end result shows that the twin performs reasonably well. The predictions differs from measured temperature with a percentage around 0.5% to 1.5%.

<p>Current industrial practice in the steel Industry involves the use of natural gas with high methane content as a primary energy source. Natural combustion produces greenhouse gases, and with the continued focus on managing and reducing harmful emissions from industrial processes, there is a need for research into alternative sources of energy. Among several alternatives that have been studied is hydrogen: a non-carbon-based fuel. This work uses a coupled computational fluid dynamics (CFD)-finite element analysis (FEA) combustion model to investigate hydrogen utilization as a fuel in a reheat furnace and how it impacts the quality of the steel produced by understanding the three dimensional (3D) flow behavior, furnace temperature profile, thermal stress distribution, heat flux, formation of iron oxides, emission gases and mode of heat transfer onto the steel slabs. The modeling process integrates the five different zones of a pusher type reheating furnace (top and bottom) and modeled using Ansys Fluent 2020R1 and Ansys Workbench 2022R1. Changes in these parameters are determined by comparison to a baseline case that uses methane as fuel and maintaining the same heat input in terms of chemical energy into the furnace. Global mechanism was used for hydrogen and two step mechanism was used for methane combustion. Results revealed a 2.6% increase in average temperature to 1478K across the furnace for hydrogen which resulted in 6.45% increase in maximum heat flux into the slabs. Similar flue gas flow patterns were seen for both cases and heat transfer mode from the combustion gases to the slabs was primarily by radiation (~97%) for both methane and hydrogen. 11.5% increase in iron oxide formation on the slab was recorded for the hydrogen case, however, the bulk of the iron oxide formed was more of wüstites which are the easiest form of iron oxide to descale. However, elevated nitrogen oxide (NOx) levels were recorded for hydrogen combustion which led to further study into NOx mitigation techniques. Application of the staged combustion method using hydrogen fuel showed potentials for NOx reduction. The use of regenerative burners further conserved exergy losses in hydrogen fuel application. Insignificant deviation from base case thermal stress distribution and zero carbon emission from the hydrogen case indicates the usability of hydrogen as an alternative fuel in reheating furnace operations. </p>

Dans l'industrie sidérurgique, les fours de réchauffage sont les plus grands consommateurs d'énergie après les hauts fourneaux. Réduire leur consommation énergétique est donc la préoccupation majeure de la commande des fours. Dans un four de réchauffage, des brames d'acier sont chauffées en traversant successivement plusieurs zones, de la température ambiante à un profil de température homogène de 1250 °C en sortie du four, avant d’être laminées dans les laminoirs à chaud. La température de brames est contrôlée par une structure de commande hiérarchisée à deux niveaux (niveau 1 et 2).L'objectif de ces travaux est d'améliorer la performance du chauffage et donc de réduire la consommation énergétique du four via une stratégie de commande prédictive distribuée hiérarchisée sur les deux niveaux de commande. Une approche de commande prédictive distribuée est tout d’abord développée pour le niveau 1 afin de suivre les consignes de température de zone, prenant en compte les couplages entre les zones et induisant une moindre complexité d’implantation par rapport à une approche centralisée. L’implantation industrielle a permis une amélioration significative de la précision du suivi de température et une réduction de la consommation d'énergie de 3%. Une deuxième étape propose l’élaboration de la commande prédictive hiérarchisée du niveau 2 afin, à partir de la consigne de température de brame, de déterminer les consignes de température optimales des zones en se fondant sur un modèle de transfert thermique du four. Les résultats de simulation, comparés aux données industrielles, montrent une réduction de la consommation énergétique de 5% et une meilleure qualité de chauffage des brames. L’approche précédente est enfin étendue pour prendre en compte et optimiser le cadencement des brames afin d’augmenter la productivité du four. La simulation montre une augmentation potentielle de productivité du four de 15 tonnes par heure tout en améliorant la qualité de chauffage des brames. / In steel industry, reheating furnaces are the biggest energy consumers after blast furnaces. As a result, reduction of energy consumption is the major concern of furnace control. In a walking-beam slab reheating furnace, steel slabs are heated by moving through successive zones from ambient temperature to a homogenous temperature profile of 1250°C at the furnace exit, to be rolled subsequently in the hot rolling mills. Temperature of slabs is controlled mainly by a two-level hierarchical structure, so called level 1 and level 2.The aim of this thesis is to improve the heating performance and consequently to reduce the energy consumption of the furnace by using hierarchical distributed model predictive control (MPC) strategy for both levels. In a first step, distributed model predictive controllers are developed for the level 1 in order to track zone temperature set-points. The distributed feature of the control law enables to consider coupling effects between zones while reducing the computation complexity compared to a complete centralized approach. The industrial results showed significant improvement on temperature tracking accuracy and an energy consumption reduction of 3%. In a second step, the hierarchical MPC is constructed for the level 2 in order to determine the optimal zones temperature setpoint from the slab temperature setpoint, based on a numerical heat transfer model of the furnace. The simulation results obtained with this strategy compared against industrial data show an energy consumption reduction of 5% and a better heating quality. The previous structure is finally extended to take into account and optimize the scheduling of the slabs within the MPC level 2 in order to increase productivity of the considered furnace. The simulation shows a potential increase of productivity of the furnace of 15 tons per hour while improving the slab heating quality.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-11-08T13:01:52Z No. of bitstreams: 1 João Eduardo Sampaio_.pdf: 5481805 bytes, checksum: ff4fd651d904071e9a30223e3222c4dc (MD5) / Made available in DSpace on 2018-11-08T13:01:52Z (GMT). No. of bitstreams: 1 João Eduardo Sampaio_.pdf: 5481805 bytes, checksum: ff4fd651d904071e9a30223e3222c4dc (MD5) Previous issue date: 2018-08-23 / Nenhuma / O Brasil precisa de um setor siderúrgico eficiente e competitivo para enfrentar a concorrência externa. A siderurgia é um ramo da metalurgia responsável pela fabricação do aço, e dentre os processos produtivos do setor, destaca-se o processo de laminação do aço, que utiliza fornos de reaquecimento. Esta pesquisa emprega a modelagem com as técnicas do Pensamento Sistêmico e da Dinâmica de Sistemas na formulação de um modelo computacional no contexto dos fornos de reaquecimento. A partir da validação do modelo, são utilizadas a Análise Envoltória de dados, para avaliar a eficiência técnica, e a regressão Tobit, para identificar variáveis estatisticamente significantes. Essas variáveis são usadas para definição dos cenários simulados. Posteriormente, as eficiências dos cenários são avaliadas por meio de estatística descritiva. Também são avaliados alvos e folgas e é testada a hipótese de igualdade da média com o teste de Welch e Post-Hoc Kruskal-Wallis. Finalmente, é realizada a análise explicativa e, com a aplicação da técnica computacional da Rede Neural Artificial, são identificadas as variáveis prevalentes da eficiência técnica do forno de reaquecimento. Tal estudo possibilita e estimula o planejamento, a gestão e a tomada de decisão a partir da análise das melhores opções. Permite, ainda, a tomada de ações com base no conhecimento prévio, contribuindo para iniciativas pontuais e focadas na competitividade. / Brazil needs an efficient and competitive steel sector to face external competition. The siderurgy is a branch of metallurgy responsible for steelmaking, and among the productive processes in the industry the steelmaking process that uses the reheating furnaces can be highlighted. This research employs the modeling with the techniques of Systemic Thinking and Systems Dynamics in the formulation of a computational model in the context of reheating furnaces. Then, using the validated model, Data Envelopment Analysis was used, evaluating the technical efficiency and the use of the Tobit regression of statistically significant variables. These variables are used to define the simulated scenarios. Subsequently, the scenarios efficiencies were evaluated by means of descriptive statistics, evaluated targets and backlash and tested the hypothesis of equality of the average with the test of Welch and Post-Hoc Kruskal-Wallis. Finally, the explanatory analysis and identified with the application of the computational technique of the Artificial Neural Network are the prevalent variables of the technical efficiency of the reheating furnace. This study enables and stimulates planning, management and decision making based on the analysis of the best options and allows the taking of actions based on previous knowledge, and thus contributes to specific initiatives focused on competitiveness.

Dans l'industrie sidérurgique, les fours de réchauffage sont les plus grands consommateurs d'énergie après les hauts fourneaux. Réduire leur consommation énergétique est donc la préoccupation majeure de la commande des fours. Dans un four de réchauffage, des brames d'acier sont chauffées en traversant successivement plusieurs zones, de la température ambiante à un profil de température homogène de 1250 °C en sortie du four, avant d’être laminées dans les laminoirs à chaud. La température de brames est contrôlée par une structure de commande hiérarchisée à deux niveaux (niveau 1 et 2).L'objectif de ces travaux est d'améliorer la performance du chauffage et donc de réduire la consommation énergétique du four via une stratégie de commande prédictive distribuée hiérarchisée sur les deux niveaux de commande. Une approche de commande prédictive distribuée est tout d’abord développée pour le niveau 1 afin de suivre les consignes de température de zone, prenant en compte les couplages entre les zones et induisant une moindre complexité d’implantation par rapport à une approche centralisée. L’implantation industrielle a permis une amélioration significative de la précision du suivi de température et une réduction de la consommation d'énergie de 3%. Une deuxième étape propose l’élaboration de la commande prédictive hiérarchisée du niveau 2 afin, à partir de la consigne de température de brame, de déterminer les consignes de température optimales des zones en se fondant sur un modèle de transfert thermique du four. Les résultats de simulation, comparés aux données industrielles, montrent une réduction de la consommation énergétique de 5% et une meilleure qualité de chauffage des brames. L’approche précédente est enfin étendue pour prendre en compte et optimiser le cadencement des brames afin d’augmenter la productivité du four. La simulation montre une augmentation potentielle de productivité du four de 15 tonnes par heure tout en améliorant la qualité de chauffage des brames. / In steel industry, reheating furnaces are the biggest energy consumers after blast furnaces. As a result, reduction of energy consumption is the major concern of furnace control. In a walking-beam slab reheating furnace, steel slabs are heated by moving through successive zones from ambient temperature to a homogenous temperature profile of 1250°C at the furnace exit, to be rolled subsequently in the hot rolling mills. Temperature of slabs is controlled mainly by a two-level hierarchical structure, so called level 1 and level 2.The aim of this thesis is to improve the heating performance and consequently to reduce the energy consumption of the furnace by using hierarchical distributed model predictive control (MPC) strategy for both levels. In a first step, distributed model predictive controllers are developed for the level 1 in order to track zone temperature set-points. The distributed feature of the control law enables to consider coupling effects between zones while reducing the computation complexity compared to a complete centralized approach. The industrial results showed significant improvement on temperature tracking accuracy and an energy consumption reduction of 3%. In a second step, the hierarchical MPC is constructed for the level 2 in order to determine the optimal zones temperature setpoint from the slab temperature setpoint, based on a numerical heat transfer model of the furnace. The simulation results obtained with this strategy compared against industrial data show an energy consumption reduction of 5% and a better heating quality. The previous structure is finally extended to take into account and optimize the scheduling of the slabs within the MPC level 2 in order to increase productivity of the considered furnace. The simulation shows a potential increase of productivity of the furnace of 15 tons per hour while improving the slab heating quality.