1.1 Hydrogen DRI: Techno-Economic Study for Low-Carbon Steel This research proposes to evaluate hydrogen-based direct reduced iron as an alternative to conventional coal- and gas-based ironmaking. It examines technology readiness, hydrogen supply, electricity demand, plant configuration, production cost, infrastructure requirements and carbon reduction potential.

1.2 Zero-Carbon Ironmaking: Comparing BF-BOF, DRI-EAF and New Technologies This research proposes to compare the emissions, energy requirements, raw material needs, costs and technology readiness of blast furnace-basic oxygen furnace, direct reduced iron-electric arc furnace and emerging ironmaking technologies.

1.3 Energy and CO2 Reduction in EAF Using Scrap-HBI Blends This research proposes to examine how different combinations of steel scrap and hot-briquetted iron affect electric arc furnace energy consumption, productivity, metallic yield, slag formation and carbon emissions.

1.4 CCUS in Blast Furnace: Performance and Feasibility This research proposes to evaluate the application of carbon capture, utilisation and storage in integrated blast furnace operations. It will review capture technologies, achievable emission reductions, energy penalties, infrastructure requirements, storage availability and project economics. The study will also determine the operational integration, regulatory support and conditions under which CCUS can serve as a transitional decarbonisation solution for existing steel plants.

1.5 Green Steel Life Cycle Carbon Footprint AssessmentThis research proposes to assess greenhouse gas emissions across the full life cycle of steel, covering raw material extraction, production, processing, transportation, use, recycling and end-of-life management. It will compare conventional and low-carbon production routes and develop evidence to support credible green steel claims, environmental product declarations, procurement standards, carbon reporting and informed investment decisions.

1.6 Technology Options for Scope 3 Steel DecarbonisationThis research proposes to explore technologies and collaborative approaches for reducing indirect emissions across the steel value chain. It will examine low-carbon raw materials, renewable electricity, green transportation, supplier engagement, product redesign, recycling, digital traceability and customer use-phase efficiency. The study will identify practical interventions for managing Scope 3 emissions that fall outside a steel producer's direct operational control.

1.7 Material Efficiency and Circular Steel for Lower Scope 1-3 EmissionsThis research proposes to examine how improved product design, higher material yield, scrap recovery, reuse, remanufacturing and closed-loop recycling can reduce emissions throughout the steel value chain. It will evaluate opportunities to minimise production losses, extend product life and reduce demand for primary steel, thereby lowering direct, energy-related and supply-chain greenhouse gas emissions.

1.8 Decarbonising Steel: Policy Drivers for a Low-Carbon Future This research proposes to review the policy instruments required to accelerate steel-sector decarbonisation, including carbon pricing, green steel standards, public procurement, renewable energy access, research incentives, green financing and trade-related carbon measures. It will assess how coherent policies can stimulate investment, protect industrial competitiveness and create sustained demand for lower-carbon steel products.

2.1 Green Transformation Initiative for Steel Processing. This research proposes to develop an integrated programme for reducing emissions from steel processing activities such as rolling, heat treatment, coating, fabrication and finishing. It will combine energy efficiency, fuel switching, renewable electricity, waste heat recovery, digital monitoring and process optimisation to establish a structured pathway for companies to set baselines, implement interventions and measure improvements.

2.2 Carbon Footprint Modelling for Complex Supply Chains. This research proposes to develop practical methods for calculating carbon emissions across multi-tier steel supply chains involving numerous suppliers, processes, transport routes and data sources. It will address data gaps, allocation rules, emission factors and supplier verification. The resulting model will support product carbon footprints, Scope 3 reporting, environmental declarations and more informed sourcing decisions.

2.3 Uncertainty Analysis in Carbon Emissions Measurement. This research proposes to examine how data quality, measurement errors, emission factors, system boundaries and modelling assumptions affect the reliability of reported carbon emissions. Statistical and sensitivity analysis methods will be applied to quantify uncertainty and identify the most influential variables, supporting more transparent, defensible and credible greenhouse gas inventories and product carbon assessments.

2.4 Roadmap to Sustainable Steelmaking: From BF-BOF to Net Zero. This research proposes to develop a phased transition roadmap from conventional blast furnace-basic oxygen furnace production towards net-zero steelmaking. It will examine energy efficiency, alternative fuels, increased scrap use, DRI-EAF conversion, hydrogen, renewable power and CCUS, while linking technology deployment with investment cycles, infrastructure readiness, workforce development, policy support and market demand.

2.5 Energy and Emissions Optimisation in Steel Mills Using Digital Twins. This research proposes to explore the use of digital twins to simulate, monitor and optimise energy and material flows across steel mill operations. By combining real-time plant data with process models, the research will identify inefficiencies, test operating scenarios and reduce fuel, electricity and carbon emissions without disrupting production. Applications will include furnaces, rolling mills, utilities and material handling systems.

3.1 Digital Twins for Real-Time Steelmaking Optimisation. This research proposes to develop virtual replicas of steelmaking equipment and processes that continuously update using live plant data. The digital twins will be used to predict process behaviour, test alternative settings and optimise productivity, energy use, yield and quality. The research will also assess applications in predictive maintenance, operator training and troubleshooting across steelmaking operations.

3.2 AI Quality Prediction in Continuous Casting. This research proposes to apply artificial intelligence to predict defects and quality variations during continuous casting. Machine-learning models will analyse temperature, casting speed, mould behaviour, cooling conditions and chemical composition to identify risks before defects occur. The study aims to enable earlier corrective action, reduce downgrades and improve slab, billet and bloom consistency.

3.3 Smart Steel Plants with IoT and Sensors. This research proposes to investigate how connected sensors, industrial internet platforms and automated data collection can improve visibility across steel plant operations. Real-time monitoring of equipment condition, temperature, pressure, vibration, energy and material flows will support better maintenance, safety, quality and production decisions, enabling more responsive, efficient and integrated plant management.

3.4 Data-Driven EAF Optimisation for Lower Energy and CO2. This research proposes to use operational data, advanced analytics and machine learning to optimise electric arc furnace performance. It will evaluate charge composition, power input, oxygen injection, carbon addition, slag conditions and tap temperature. The objective is to reduce electricity consumption, electrode use, process time and carbon emissions while maintaining productivity and steel quality.

3.5 Industry 4.0 for End-to-End Steel Production Traceability.This research proposes to develop a digital traceability framework connecting raw materials, production parameters, quality results, logistics and final products. Technologies such as IoT, cloud platforms, blockchain, digital identification and integrated databases will be assessed to enable materials to be tracked throughout the steel value chain, improving quality assurance, carbon reporting, regulatory compliance and customer confidence.

2.1 Green Transformation Initiative for Steel Processing. This research proposes to develop an integrated programme for reducing emissions from steel processing activities such as rolling, heat treatment, coating, fabrication and finishing. It will combine energy efficiency, fuel switching, renewable electricity, waste heat recovery, digital monitoring and process optimisation to establish a structured pathway for companies to set baselines, implement interventions and measure improvements.

4.1 Real-Time Foamy Slag Optimisation in EAF Using Off-Gas Data. This research proposes to use real-time off-gas measurements to optimise foamy slag formation in electric arc furnaces. Analysis of carbon monoxide, carbon dioxide, oxygen and temperature will be used to regulate oxygen and carbon injection. The research aims to improve arc stability, heat transfer and refractory protection while reducing electricity use, electrode consumption and processing time.

4.2 Advanced Temperature and Roll Gap Control for Better Hot Strip Mill Efficiency. This research proposes to develop advanced sensors, control models and automation for maintaining optimum temperature and roll gap throughout hot strip rolling. Improved control is expected to enhance strip thickness, flatness, mechanical properties and surface quality while reducing cobbles, rework, energy losses and production variability, resulting in higher throughput and more consistent product performance.

4.3 Optimised Descaling in Hot Rolling for Better Scale Removal and Lower Water Use. This research proposes to investigate improved descaling strategies for removing oxide scale from steel surfaces during hot rolling. It will examine nozzle design, water pressure, spray timing, strip temperature and scale characteristics. The objective is to improve surface quality and equipment reliability while reducing water consumption, pumping energy, wastewater generation and unnecessary material loss.

4.4 Efficiency Improvement in Galvanising Lines with Automated Chemistry and Coating Control. This research proposes to develop automated monitoring and control of bath chemistry, strip temperature, line speed and coating thickness in continuous galvanising operations. Real-time adjustments will be assessed for their ability to improve coating uniformity, adhesion and corrosion resistance while reducing zinc consumption, rejects, maintenance requirements and production interruptions.

5.1 Improving DRI Quality with Better Pellet Chemistry and Low-Carbon Methods. This research proposes to investigate how pellet composition, gangue content, porosity, strength and reducibility affect direct reduced iron quality and process performance. It will also examine low-carbon pellet production and reduction methods. The objective is to produce cleaner, stronger and more reactive DRI suitable for efficient EAF operation and low-emission steelmaking.

5.2 Real-Time Ore Blending for More Consistent Iron Ore Quality. This research proposes to develop sensors, online analysers and automated blending systems for managing variations in iron ore chemistry, moisture and particle size. Real-time adjustments will be studied to deliver a more consistent furnace feed, improve process stability, fuel efficiency, productivity and product quality, and enable more effective use of diverse ore sources.

5.3 AI and Sensor-Based Scrap Sorting for Higher Steel Recycling. This research proposes to examine the use of artificial intelligence, machine vision, spectroscopy and automated sensors to identify and separate steel scrap by grade, composition and contamination level. Improved sorting is expected to increase the availability of high-quality scrap, reduce residual elements and support greater recycled content without compromising steel performance or production efficiency.

5.4 Using Low-Grade Ore and Waste Products through New Process Innovations. This research proposes to evaluate innovative beneficiation, agglomeration, reduction and smelting technologies for converting low-grade ores, fines, residues and iron-bearing waste into usable steelmaking inputs. It will assess opportunities to improve resource efficiency, reduce disposal requirements and diversify raw material supply while managing impurities, energy consumption, emissions and product quality.

5.5 Slag Engineering to Reduce Phosphorus and Sulfur in Steelmaking. This research proposes to examine the design and control of slag chemistry to improve phosphorus and sulfur removal during steelmaking. It will investigate basicity, oxidation potential, temperature, viscosity and flux selection. The study aims to enhance refining efficiency, steel cleanliness and metallic yield while reducing flux consumption, processing time and refractory wear.

6.1 Improving Steel Processing for Ultra-High Strength Steel. This research proposes to investigate advanced alloy design, casting, rolling, cooling and heat-treatment processes required to produce ultra-high strength steel. It will focus on achieving the required balance of strength, ductility, toughness and formability while controlling microstructure and defects for automotive, construction, energy and high-performance engineering applications.

6.2 Adaptive MPC for Better Gauge Control and Thickness Uniformity. This research proposes to develop adaptive model predictive control for regulating strip thickness during rolling. The system will continuously update its process model using real-time measurements and adjust roll force, speed, tension and gap settings. The expected outcomes are reduced thickness variation, off-specification material, rework and yield losses, together with improved product consistency.

6.3 Real-Time Inclusion Detection Using LIBS. This research proposes to assess laser-induced breakdown spectroscopy for detecting and characterising non-metallic inclusions in steel in real time. Rapid analysis of elemental composition will be evaluated as a means of identifying cleanliness problems earlier and improving process control, thereby reducing laboratory delays, product defects and failures associated with harmful inclusions.

6.4 Quality Control in Hot Products Operations. This research proposes to develop an integrated approach to quality control across hot rolling and related operations. It will examine temperature, dimensions, surface condition, mechanical properties, equipment settings and process stability. Combining real-time inspection, statistical control and operator response systems is expected to reduce defects, improve yield and ensure consistent compliance with customer specifications.

6.5 Reducing Sheet Defects with Automated UT and Digital Twin Casting Control. This research proposes to combine automated ultrasonic testing with digital twin models of casting operations to detect and prevent internal sheet defects. Inspection results will be linked to casting conditions, cooling patterns and process parameters to identify root causes. The approach aims to enable earlier intervention, improve traceability and reduce rejection or downgrading of finished products.

6.6 Enhancing Coating Adhesion and Corrosion Resistance in Steel Production. This research proposes to examine surface preparation, bath chemistry, coating composition, temperature control and post-treatment methods for improving coated steel performance. It will address poor adhesion, uneven coverage and premature corrosion, with the aim of supporting longer product life, lower rejection rates and more reliable performance in demanding environments.