Boost Efficiency in Blast Furnace Steelmaking

Transform Blast Furnace Operations with Computer Vision Systems

Enhance blast furnace stability, efficiency, and productivity with AI-driven insights and recommendations. Tackle the inherent complexity of blast furnace operations to optimize performance, reduce downtime, and improve overall output.

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Blast Furnace Operations

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Issues Leading to blast furnace instability

Major Issues Causing Inefficiencies in Blast Furnace Operations

Blast furnace steelmaking faces challenges like raw material variability, inconsistent fuel rates, and uneven burden distribution, disrupting efficiency. Temperature fluctuations, slag formation issues, and gas flow instability further compromise performance. Refractory wear and downtime increase maintenance costs. Computer vision solutions optimize furnace stability, reduce costs, and improve productivity.

Variability in Raw Material

Inconsistencies in steel raw materials, such as oversize particles, fines, and moisture content, disrupts steel manufacturing process,

Inconsistencies in raw materials, like iron ore quality, coke strength, and moisture content, disrupt the steel manufacturing process, leading to unpredictable furnace conditions. This increases fuel consumption and impacts the final product's quality.

Improper burden distribution

Uneven burden distribution disrupts gas flow and heat distribution in the steel production process

Inefficient burden distribution in the steel production process causes poor performance, higher energy use, and increased costs, with operators relying on heuristics for adjustments.

Excessive Fuel Consumption

Inconsistent operational conditions, heat losses, and suboptimal furnace operation further contribute to increased fuel usage in blast furnace

Excessive fuel consumption in the steel production process results from poor combustion efficiency, fluctuations in raw material quality, and uneven burden distribution. These inefficiencies drive up operational costs and lower furnace performance, impacting productivity and energy consumption.

Unplanned Equipment Failures

Equipment failures lead to unexpected downtime, production delays, and increased maintenance costs, disrupting overall furnace instability.

Unexpected equipment failures and extended maintenance periods in the steel manufacturing process can severely impact blast furnace performance. Frequent repairs and downtime disrupt the production schedule, resulting in lower output and higher operational costs.

Refractory Wear and Damage

Continuous wear and tear on furnace lining increase downtime and maintenance needs

Over time, continuous exposure to high temperatures, corrosive slag, and mechanical stress degrades blast furnace refractories, causing damage that leads to furnace downtime, unplanned maintenance, and reduced efficiency.

Uncontrolled slag viscosity

Improper slag control leads to higher energy use and furnace damage

Poorly controlled slag formation can obstruct heat transfer, resulting in higher energy usage and lower furnace productivity. Improper slag properties can also cause excessive wear on the furnace lining and affect metal quality.

Temperature Fluctuations

Inconsistent furnace temperatures affect metal quality and reduce efficiency.

Inconsistent temperature management inside the blast furnace can lead to inefficiencies in the reduction process. Overheating can damage the refractory lining, while insufficient heat can slow down the reaction, leading to reduced metal quality and productivity.

High Carbon Emissions

Undetected oversized rocks disrupt kiln material flow, causing inefficient combustion and higher fuel consumption.

Inefficient fuel consumption, poor combustion efficiency, and excessive energy usage lead to higher CO2 emissions, resulting in high carbon emissions in the blast furnace steelmaking process.

Enhance steel manufacturing process

Vision AI Technology to
Optimal Blast Furnace Steel Making Process

Real-time raw material sizing
Blast Furnace Steel Making Process
  • Accurate Particle Size Analysis: Computer vision systems enable over 90% accuracy in measuring raw material size across coke (BF and Nut coke), sinter, pellet, and lump ore, ensuring optimal burden distribution in blast furnace operations.
  • Real-Time Alerts: Get real-time data with automated alerts for higher fines percentage and visual anomalies such as high moisture content, enabling swift corrective actions.
Burden Mix Optimizer & Data Warehouse
Blast Furnace Steel Making Process
  • Burden Mix Optimization: The Burden Mix Tool optimizes blast furnace operations by analyzing chemistry and costs to determine the best sinter, pellet, ore, and flux mix. Integrated with LIMS and genetic algorithms, it provides real-time adjustments & recommendations.
  • Data warehouse integration: By analysing historical and real-time data, the data warehouse identifies optimal operating parameters, improving efficiency, and furnace stability.
Burden Distribution optimization
Blast Furnace Steel Making Process
  • AI Stability Monitoring: Vision AI models continuously monitors thermal, pressure profiles, and raw materials to provide early alerts and AI-driven recommendations, optimizing burden distribution and furnace performance.
  • Optimized Furnace Operations: In-depth analysis of furnace production and permeability, along with effective SOPs, improved productivity by 3% through optimized gas flow and efficiency.
RCA module for etaCO
Blast Furnace Steel Making Process
  • Root Cause Analysis: Ripik’s AI model delivers real-time root cause analysis (RCA) and corrective recommendations to maintain high etaCO and reduce coke rate in the furnace. The tool identifies variables that positively and negatively impact etaCO, providing actionable insights for optimization.
  • Operational Recommendations: Vision AI tool provides recommendations for setting operational variables to optimize performance and efficiency in the furnace.
Hot Metal Silicon Prediction​
Blast Furnace Steel Making Process
  • AI-ML Based Hot Metal Silicon Prediction: Ripik’s AI model predicts the hot metal silicon (Si) in the upcoming cast based on raw material and process parameters. It offers recommendations for adjusting PCI/RAFT to stabilize silicon levels. The model achieves a 40% reduction in standard deviation, improving prediction accuracy.
  • Instant Alerts :The tool performs continuous RCA on Si values, with WhatsApp alerts for timely notifications when key variables change.
Tuyere Monitoring
Blast Furnace Steel Making Process
  • Real-time Monitoring: Vision AI agents enables real-time tuyere monitoring, detecting issues like tuyere sticking, lance off-centering, and choking with high accuracy.

  • Historical Video Feed : Real-time alerts notify operators of anomalies, to remove manual inspections, enhances safety, and improves furnace performance. Timely intervention minimizes unplanned shutdowns and extending tuyere lifespan.
Furnace Refractory Monitoring
blast Furnace Refractory Monitoring
    • 360-Degree Monitoring with IR Cameras: Multiple IR cameras offer full furnace shell coverage, detecting temperature variations and identifying hotspots in real-time, ensuring comprehensive monitoring to eliminate steel downtime.
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    • Real-Time Alerts: Receive instant alerts for hotspot regions and track recurring issues, enabling timely intervention to reduce refractory failure risks and extend refractory life.
    Bunker level Monitoring
    cement crusher predictive maintenance
    • Early Detection of Belt & Chute Damage: Bunker level monitoring provides real-time data to detect material flow abnormalities that may cause belt and chute damage in blast furnace operations. By tracking bunker levels, it identifies fluctuations or blockages, enabling early intervention to prevent equipment wear and reduce downtime in steel plants. This proactive approach prevents failures, and extends the lifespan of critical components, ensuring stable furnace performance.

    Improve blast furnace operations in steel industry

    Benefits of AI in blast furnace operations

    Vision AI in the blast furnace steel making process enable real-time monitoring, early detection of equipment issues, improved efficiency, reduced downtime, and extended component lifespan for stable, cost-effective furnace performance.

    Increased Productivity

    Real-time blast furnace monitoring and predictive maintenance ensure smoother operations, leading to higher throughput, better quality, and more consistent output in the steelmaking process.

    Blast Furnace Operations
    Blast Furnace Operations

    Cost Reduction

    Advanced monitoring and predictive maintenance reduce costly repairs, while optimizing fuel usage and process efficiency lowers material waste and energy consumption, leading to significant savings in steelmaking.

    Extended Equipment Life

    Blast furnace operations extend equipment life by optimizing performance and enabling early issue detection in refractory linings, reducing wear and preventing costly breakdowns.

    Reduce Carbon Emission

    Blast furnace operations reduce carbon emissions by optimizing fuel usage and enhancing combustion efficiency. Precise monitoring minimizes fuel consumption improving steelmaking process.