Refining and Casting of Steel

Steel has become the most requested material all over the world during the rapid technological evolution of recent centuries. As our civilization grows and its technological development becomes connected with more demanding processes, it is more and more challenging to fit the required physical and mechanical properties for steel in its huge portfolio of grades for each steel producer. It is necessary to improve the refining and casting processes continuously to meet customer requirements and to lower the production costs to remain competitive. New challenges related to both the precise design of steel properties and reduction in production costs are combined with paying special attention to environmental protection. These contradictory demands are the theme of this book

New Methods for ferrous raw materials characterization in electric steelmaking

425 p.In the siderurgical sector, the steel scrap is the most important raw material in electric steelmaking,contributing between 70% of the total production costs. It is well-known how the degree of which thescrap mix can be optimized, and also the degree of which the melting operation can be controlled andautomated, is limited by the knowledge of the properties of the scrap and other raw-materials in thecharge mix.Therefore, it is of strategic importance having accurate information about the scrap composition of thedifferent steel scrap types. In other words, knowing scrap characteristics is a key point in order to managethe steel-shop resources, optimize the scrap charge mix/composition at the electric arc furnace (EAF),increase the plant productivity, minimize the environmental footprint of steelmaking activities and tohave the lowest total cost of ownership of the plant.As a main objective of present doctoral thesis, the doctorate will provide new tools and methods of scrapcharacterization to increase the current recycling ration, through better knowledge of the quality of thescrap, and thus go in the direction of a 100% recycling ratio. In order to achieve it, two main workinglines were developed in present research. Firstly, it was analysed not only the different existingmethodologies for scrap characterization and EAF process optimization, but also to develop new methodsor combination of existing, Secondly, it was defined a general recommendations guide for implementingthese methods based on the specifics of each plant

Challenges and Prospects of Steelmaking Towards the Year 2050

The world steel industry is strongly based on coal/coke in ironmaking, resulting in huge carbon dioxide emissions corresponding to approximately 7% of the total anthropogenic CO2 emissions. As the world is experiencing a period of imminent threat owing to climate change, the steel industry is also facing a tremendous challenge in next decades. This themed issue makes a survey on the current situation of steel production, energy consumption, and CO2 emissions, as well as cross-sections of the potential methods to decrease CO2 emissions in current processes via improved energy and materials efficiency, increasing recycling, utilizing alternative energy sources, and adopting CO2 capture and storage. The current state, problems and plans in the two biggest steel producing countries, China and India are introduced. Generally contemplating, incremental improvements in current processes play a key role in rapid mitigation of specific emissions, but finally they are insufficient when striving for carbon neutral production in the long run. Then hydrogen and electrification are the apparent solutions also to iron and steel production. The book gives a holistic overview of the current situation and challenges, and an inclusive compilation of the potential technologies and solutions for the global CO2 emissions problem

Data mining for fault diagnosis in steel making process under industry 4.0

The concept of Industry 4.0 (I4.0) refers to the intelligent networking of machines and processes in the industry, which is enabled by cyber-physical systems (CPS) - a technology that utilises embedded networked systems to achieve intelligent control. CPS enable full traceability of production processes as well as comprehensive data assignments in real-time. Through real-time communication and coordination between "manufacturing things", production systems, in the form of Cyber-Physical Production Systems (CPPS), can make intelligent decisions. Meanwhile, with the advent of I4.0, it is possible to collect heterogeneous manufacturing data across various facets for fault diagnosis by using the industrial internet of things (IIoT) techniques. Under this data-rich environment, the ability to diagnose and predict production failures provides manufacturing companies with a strategic advantage by reducing the number of unplanned production outages. This advantage is particularly desired for steel-making industries. As a consecutive and compact manufacturing process, process downtime is a major concern for steel-making companies since most of the operations should be conducted within a certain temperature range. In addition, steel-making consists of complex processes that involve physical, chemical, and mechanical elements, emphasising the necessity for data-driven approaches to handle high-dimensionality problems. For a modern steel-making plant, various measurement devices are deployed throughout this manufacturing process with the advancement of I4.0 technologies, which facilitate data acquisition and storage. However, even though data-driven approaches are showing merits and being widely applied in the manufacturing context, how to build a deep learning model for fault prediction in the steel-making process considering multiple contributing facets and its temporal characteristic has not been investigated. Additionally, apart from the multitudinous data, it is also worthwhile to study how to represent and utilise the vast and scattered distributed domain knowledge along the steel-making process for fault modelling. Moreover, state-of-the-art does not iv Abstract address how such accumulated domain knowledge and its semantics can be harnessed to facilitate the fusion of multi-sourced data in steel manufacturing. In this case, the purpose of this thesis is to pave the way for fault diagnosis in steel-making processes using data mining under I4.0. This research is structured according to four themes. Firstly, different from the conventional data-driven research that only focuses on modelling based on numerical production data, a framework for data mining for fault diagnosis in steel-making based on multi-sourced data and knowledge is proposed. There are five layers designed in this framework, which are multi-sourced data and knowledge acquisition, data and knowledge processing, KG construction and graphical data transformation, KG-aided modelling for fault diagnosis and decision support for steel manufacturing. Secondly, another of the purposes of this thesis is to propose a predictive, data-driven approach to model severe faults in the steel-making process, where the faults are usually with multi-faceted causes. Specifically, strip breakage in cold rolling is selected as the modelling target since it is a typical production failure with serious consequences and multitudinous factors contributing to it. In actual steel-making practice, if such a failure can be modelled on a micro-level with an adequately predicted window, a planned stop action can be taken in advance instead of a passive fast stop which will often result in severe damage to equipment. In this case, a multifaceted modelling approach with a sliding window strategy is proposed. First, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using a Recurrent Neural Network (RNN). Experimental results have demonstrated the merits of the proposed approach. Thirdly, among the heterogeneous data surrounding multi-faceted concepts in steelmaking, a significant amount of data consists of rich semantic information, such as technical documents and production logs generated through the process. Also, there Abstract v exists vast domain knowledge regarding the production failures in steel-making, which has a long history. In this context, proper semantic technologies are desired for the utilisation of semantic data and domain knowledge in steel-making. In recent studies, a Knowledge Graph (KG) displays a powerful expressive ability and a high degree of modelling flexibility, making it a promising semantic network. However, building a reliable KG is usually time-consuming and labour-intensive, and it is common that KG needs to be refined or completed before using in industrial scenarios. In this case, a fault-centric KG construction approach is proposed based on a hierarchy structure refinement and relation completion. Firstly, ontology design based on hierarchy structure refinement is conducted to improve reliability. Then, the missing relations between each couple of entities were inferred based on existing knowledge in KG, with the aim of increasing the number of edges that complete and refine KG. Lastly, KG is constructed by importing data into the ontology. An illustrative case study on strip breakage is conducted for validation. Finally, multi-faceted modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is typically applied to cope with the high dimensionality and data heterogeneity. Besides the ability for knowledge management and sharing, KG can aggregate the relationships of features from multiple aspects by semantic associations, which can be exploited to facilitate the information fusion for multi-faceted modelling with the consideration of intra-facets relationships. In this case, process data is transformed into a stack of temporal graphs under the faultcentric KG backbone. Then, a Graph Convolutional Networks (GCN) model is applied to extract temporal and attribute correlation features from the graphs, with a Temporal Convolution Network (TCN) to conduct conceptual modelling using these features. Experimental results derived using the proposed approach, and GCN-TCN reveal the impacts of the proposed KG-aided fusion approach. This thesis aims to research data mining in steel-making processes based on multisourced data and scattered distributed domain knowledge, which provides a feasibility study for achieving Industry 4.0 in steel-making, specifically in support of improving quality and reducing costs due to production failures

Copper contamination in end-of-life steel recycling, developing a new strategy from million-tonnes to milligrams

Increasing the share of scrap-based steel production is necessary to achieve CO2 emissions targets. However, the quality of recycled steel is compromised by contaminating elements, of which copper is the most pervasive. Copper from wiring and motors entangles with steel fragments during shredding and is not completely removed by magnetic separation. Beyond hand-picking, no commercial process exists for extraction, but copper in solution with steel segregates during hot rolling, causing surface cracking and defects that are unacceptable for high-quality flat products. This thesis characterizes copper in the global steel system, evaluates the energy requirements of possible extraction processes and presents experimental results to aid in the development of an efficient extraction technique. Copper contamination is currently managed by globally trading contaminated scrap to tolerant applications and by dilution with primary steel. An evaluation of copper in the global steel system is needed to develop long-term strategies, and this is presented in the first part of this thesis. The copper concentration of flows along the 2008 steel supply chain are estimated from a range of literature sources and compared with the maximum concentration that can be tolerated in all steel products. Quantities of final steel demand and scrap supply by sector from a global stock-saturation model are used to estimate the amount of copper in the future scrap supply, and the total amount tolerable. Assuming current scrap preparation continues, more copper will enter the steel cycle than can be tolerated by demanded products by 2050. This global constraint will set in sooner if primary production is cut to meet climate mitigation targets. Given the upcoming constraints, improved copper control is necessary. Various techniques for copper separation have been explored in laboratory trials, but as yet no attempt has been made to provide an integrated assessment of these options. The second part of this thesis presents a framework to define the full range of separation routes and evaluate their potential to remove copper, while estimating their energy and material input requirements. The thermodynamic, kinetic and technological constraints of the various techniques are analyzed to show that copper could be removed to below 0.1wt% (enabling the production of high-value flat products) with 5-20% of the melting energy in the electric arc furnace route. The above analysis reveals a promising and under-explored process route: preferential melting of copper from solid steel scrap, which could be integrated into conventional scrap re-melting with little additional energy. Previous investigations show removal of liquid copper is limited by its adherence to solid scrap. In the third part of this thesis, the individual and combined effects of several parameters (steel carbon content, initial surface oxidation and applied coatings) on the wetting behavior of liquid copper are observed with a heating microscope to understand if a process window to enable separation exists. The most significant factor was carbon content. On medium carbon steel substrates, copper spread rapidly, likely due to reduction of the oxide layer by carbon. Non-wetting copper droplets were observed on low carbon substrates in an inert atmosphere. This indicates a possible process window, but further investigation considering diverse, fragmented end-of-life scrap is needed. The scrap supply of all metals is expanding. The multi-scale, interdisciplinary method developed in this thesis could be applied to other metal systems to understand the constraints caused by contamination and identify key areas to develop efficient extraction processes, necessary to conserve resources and reduce CO2 emissions.Cambridge Trust International Scholarshi