Economic Sustainability

Project D: Obtaining Value from Steel Plant By-Products

Realizing economic value from steel plant by-products and maintaining a strong social and environmental perspective is an essential requirement of present day steelmaking. The focus of this research is on the sustainable recovery and utilisation of iron and flux units. This will be achieved through an early drive to characterize selected steel plant waste to develop, inform and assess recovery strategies and specific project plans to realize value from the by-products.

Team: Brian Monaghan, David Pinson, Sheng Chew, Guanqing Zhang, Ray Longbttom, John Heslin

Project D1.1: Effect of Retained BOS Slag on Refractory Wear

This research will provide a fundamental understanding of the effects of retained slag practice on Basic Oxygen Furnace (BOF) refractory wear. BOF steelmaking remains the predominant process worldwide. While there has been much work carried out on the effects of BOS slag composition on carbon-bonded magnesia refractories there has been little that has dealt with the specifics of retained slag practice on refractory wear. This slag practice focuses on increasing the steel yield in the vessel; in addition, its use favours early slag formation in the BOF that results in a different slag evolution. As such, the changes in slag formation can have profound effects on the physicochemical properties of the slag (viscosity, species chemical activity, interfacial tensions), how it flows in the BOF vessel and therefore, how it reacts with refractories.

Team: Brian Monaghan, Sheng Chew, Michael Chapman, Geoff Evans, Guangqing Zhang, Thi Diem Ai Nguyen, Andrew Jacobs, Ian Kennedy, Ray Longbottom, Raju Chowdhury

Project D1.2: A Fundamental Study into the Use of Different Ferrous Ores on Melt Characteristics of Sinter

This research aims to develop a fundamental understanding and capability to assess the effects of using different Australian iron ores on the melt characteristics of raw materials in the sinter blend, particularly in relation to temperature, basicity and gas composition. Fundamental work will focus on the interaction between different components of a sinter blend. Expected outcomes include the establishing of cost-effective, bench-scale experimental capabilities to investigate these component interactions prior to either pilot-scale or plant trials.

Team: Guangqing Zhang, David Pinson, Brian Monaghan, Sheng Chew,Huibin Li, Paul Zulli

Project D1.4: Development of a Fundamental Particle Scale Approach to Modelling Blast Furnace Charging Phenomena

Ironmaking blast furnaces are typically charged with alternating layers of granular coke and ferrous materials using a rotating chute to distribute materials circumferentially around the furnace throat. Although rotating chute designs offer a significant degree of control, there are many phenomena where an operator has only limited ability to control the fundamental granular behaviours that impact the final distribution. Simple empirical rotating chute models have been developed by considering simple stream trajectory calculation and volume filling/slope stability formulations. However, they require significant effort in measuring the parameters that describe these phenomena.
Discrete particle simulation is well situated to provide a deeper understanding of burden distribution in a blast furnace. Development of the next generation of blast furnace distribution model can provide the step change in control needed to take advantage of the modern multicomponent burden composition. The project will deliver a model for offline use as a burden distribution planning and evaluation tool to enhance the decision making capability of blast furnace engineers.

Team: Zongyan Zhou, David Pinson, Sheng Chew, Yinxuan Qiu

Project D1.6: The effect of Ti on the Kinetics of Phosphorous Removal during BOF Steelmaking

Utilisation of iron ores (ironsands) with higher Ti content has prompted this BOF-based investigation into the effects of Ti on the removal of P from steel. A specific focus will be to separate the effects of Ti on the physical properties of the slag and the chemistry of phosphorous transfer from steel to slag. From a plant perspective, the thermodynamic and kinetic data obtained from high-temperature experiments will be incorporated into a) an operational model that accurately predicts the steel’s final phosphorous composition enabling a reduction in overall heat-to-heat time and increased productivity of the existing plant, and b) a flux usage model to optimize flux usage, plant practices and incoming phosphorous loads reducing the overall cost of converting iron ores into steel.

Team: Brian Monaghan, Sheng Chew, Michael Chapman, Guangqing Zhang, Andrew Jacobs, Phillip Drain, Ray Longbottom

Project D1.7 Data Mining Applications and Digital Signal Processing in Primary Operations

Utilisation of machine learning techniques such as data mining provide an objective means of identifying valid and useful structure or patterns in large, complex, multi-factorial and highly coupled data sets, as found in integrated steel manufacturing plants. This project will assess the applicability and value of data mining techniques to the iron and steelmaking operations. This will be achieved through a series of targeted case studies, based on a diverse range of data sources drawn from existing archives as well as targeted plant measurements. Emphasis will be placed on the application of signal processing and spatiotemporal data mining techniques.

Team: David Stirling, Sheng Chew, Paul Zulli, David Pinson, Christian Ritz,Shahab Pasha

D1.8 A Fundamental Understanding of Processing Limits in Blast Furnace Ironmaking Leading to Optimisation of Productivity Through Innovative Management of Raw Material Quality

The project addresses one of the most strategic issues faced by BlueScope and ArcelorMittal, and in general, the global steel industry and its raw materials suppliers – being, the selection of a suite of raw materials, the properties of which optimize the performance of each specific BF, particularly in terms of productivity and overall operating cost. Linking raw material properties with BF input and operational parameters, such as the pulverized coal rate (PCR), slag rate and coke strength, should make it possible to derive the most relevant, inherent relationships for “value-in-use” determinations made by steel companies.

Team: Xuefeng Dong, Apsara Jayasekara, Dominique Sert, Pascal Gardan, Rodolfo Paulo Santos Ferreira, Sophie Clairay, David PinsonBrian MonaghanSheng Chew, Paul Zulli

D1.9 Productivity and Campaign Life Improvements Through Development of Numerical Models of the Ironmaking Blast Furnace

This is a project that aims to support higher production levels for Liberty Primary Steel through utilising process modelling tools to assist in the planning and designing of operations at the Whyalla blast furnace. This includes optimising the raw materials mix charged.  Specific areas where improved understanding and knowledge is required include: a) burden and gas distribution, for hot metal quality and gas efficiency, b) hearth erosion and management, for campaign life and liquids control, and c) overall process assessment and diagnosis, particularly concerning the control of the position and shape of the cohesive zone. It is expected that these modelling tools will facilitate the interpretation of both online and historical data, and provide realistic assessments of the physical, chemical and thermal conditions within the furnace. 

Team: Xuefeng Dong, John Tsalapatis, Matt Middleton Des Bartholomew, Paul Zulli

Project D2.1: Wall Stress Analysis under Blast Furnace Conditions

Many blast furnaces that have installed copper staves have experienced premature failure usually attributed to abrasive wear by the granular burden materials. A number of conceptual frameworks have been developed to explain the severity of the wear (compared to previous cast iron stave experience) but a key issue that is not easily resolved is the role of local stress transfer from the granular burden to the wall. The second issue is the degree of local burden cooling possible due to the high heat extraction capability of copper stave and the extent to which this may contribute to the observed wear rates.

Discrete Particle Simulation (DPS) offers a well-established method to model complex particle flow systems and their mechanical interaction with process equipment. This project will leverage DPS techniques to study how overall furnace profile influences the stress generation in the wall region of a blast furnace and consider how to account for operational phenomena including ferrous material softening and melting.

Team: Zongyan Zhou, David Pinson, Sheng Chew, Joel Samsu

Project D2.2: Mixing and Segregation in Burden Delivery to the Blast Furnace

Under certain circumstances, blast furnace productivity can be constrained due to the capacity of the stockhouse to deliver materials. The stockhouse receives the bulk solid materials from various sources (stockpiles, sinter plant, coke oven battery, etc), storing each material in individual bins to provide several hours of feed material for normal furnace operations.

This project aims to address the issues of overcharge capacity, mixing and segregation, and stock transitions through the analysis and characterization of solids flow behavior in burden delivery unit operations including: bin and weigh hopper filling and discharge, screening, transfer points and conveying.

Team: Geoffrey Evans, Deside Chibwe, David Pinson, Sheng Chew, Mark Biasutti, Brian Monaghan