Case Study

Case Study

Industry.AI uses advanced AI and deep learning tools to provide energy savings, cost saving and reduce emissions.

Industry.AI uses advanced AI and deep learning tools to provide energy savings, cost saving and reduce emissions.

In heavy manufacturing industries like steel and aluminum, frequent failures and unplanned downtime pose significant challenges to production efficiency and quality. The continuous casting process, a crucial step in steel production, often faces issues such as component failure, quality defects, billet breakout, and casting delays. In this blog, we will explore how Industry.AI can address these challenges and reduce unplanned downtime in the continuous billet casting process. By leveraging the capabilities of Industry.AI, steel manufacturers can enhance productivity, improve product quality, and gain a competitive edge in the market.

In heavy manufacturing industries like steel and aluminum, frequent failures and unplanned downtime pose significant challenges to production efficiency and quality. The continuous casting process, a crucial step in steel production, often faces issues such as component failure, quality defects, billet breakout, and casting delays. In this blog, we will explore how Industry.AI can address these challenges and reduce unplanned downtime in the continuous billet casting process. By leveraging the capabilities of Industry.AI, steel manufacturers can enhance productivity, improve product quality, and gain a competitive edge in the market.

Challenges
Challenges

The continuous billet casting process in steel production faced several challenges:

The continuous billet casting process in steel production faced several challenges:

Component Failure: Critical components in the casting process, such as molds, rollers, and cooling systems, can experience frequent failures. These failures led to production disruptions, costly repairs, and unplanned downtime.

Component Failure: Critical components in the casting process, such as molds, rollers, and cooling systems, can experience frequent failures. These failures led to production disruptions, costly repairs, and unplanned downtime.

Quality Defects: Process variability can result in quality defects in the cast billets, affecting the integrity and performance of the final steel products. This posed a significant concern in the face of growing competition and customer demands for high-quality materials.
Quality Defects: Process variability can result in quality defects in the cast billets, affecting the integrity and performance of the final steel products. This posed a significant concern in the face of growing competition and customer demands for high-quality materials.
Billet Breakout: Improper solidification and insufficient control of casting parameters can cause billet breakout, where the billet breaks during the casting process. This led to production losses and delays, impacting overall productivity.
Billet Breakout: Improper solidification and insufficient control of casting parameters can cause billet breakout, where the billet breaks during the casting process. This led to production losses and delays, impacting overall productivity.
Casting Delay: Inefficient scheduling and lack of real-time insights into the casting process can result in delays, leading to production bottlenecks and increased operational costs.
Casting Delay: Inefficient scheduling and lack of real-time insights into the casting process can result in delays, leading to production bottlenecks and increased operational costs.
Industry.AI Approach
Industry.AI Approach

To address the challenges and reduce unplanned downtime in continuous billet casting, steel manufacturers adopted the following approach using Industry.AI:

To address the challenges and reduce unplanned downtime in continuous billet casting, steel manufacturers adopted the following approach using Industry.AI:

Data Integration and Analysis

Integrated data from sensors, process parameters, and production systems into the Industry.AI platform.

Analyzed real-time and historical data to identify patterns, correlations, and root causes of component failures, quality defects, billet breakout, and casting delays.

Data Integration and Analysis

Integrated data from sensors, process parameters, and production systems into the Industry.AI platform.

Analyzed real-time and historical data to identify patterns, correlations, and root causes of component failures, quality defects, billet breakout, and casting delays.

Predictive Maintenance

Utilized Industry.AI's predictive analytics capabilities to monitor the health of critical components and predict failures before they occur. This allowed proactive maintenance and reduced unplanned downtime.

Predictive Maintenance

Utilized Industry.AI's predictive analytics capabilities to monitor the health of critical components and predict failures before they occur. This allowed proactive maintenance and reduced unplanned downtime.

Process Optimization

Analyzed casting parameters, such as temperature, speed, and cooling rates, to identify optimal settings that minimized quality defects and reduce the risk of billet breakout.

Leverageed Industry.AI's machine learning algorithms to optimize process parameters and achieved consistent casting performance.

Process Optimization

Analyzed casting parameters, such as temperature, speed, and cooling rates, to identify optimal settings that minimized quality defects and reduce the risk of billet breakout.

Leverageed Industry.AI's machine learning algorithms to optimize process parameters and achieved consistent casting performance.

Real-time Monitoring and Alerting

Implemented real-time monitoring of the continuous casting process using Industry.AI's monitoring features. This enabled operators to detect anomalies, deviations, and potential issues, promptly allowing for timely intervention and corrective actions.

Real-time Monitoring and Alerting

Implemented real-time monitoring of the continuous casting process using Industry.AI's monitoring features. This enabled operators to detect anomalies, deviations, and potential issues, promptly allowing for timely intervention and corrective actions.

Digital Twin

Created a digital twin of the RUBM line at the steel plant.

Digital Twin

Created a digital twin of the RUBM line at the steel plant.

Benefits
Benefits

Implementing Industry.AI in the continuous billet casting process yields several benefits:

Implementing Industry.AI in the continuous billet casting process yields several benefits:

Early Detection of Component Failures: By leveraging machine learning-based anomaly detection, the energy producer identified component failures well in advance, enabling proactive maintenance and reducing unplanned downtime.
Improved Asset Management: Accurate measures of asset health and performance allowed for timely maintenance interventions, extending the lifespan of critical components and optimizing maintenance practices.
Enhanced Operational Efficiency: Through predictive maintenance and optimized resource allocation, the energy producer can improve operational efficiency, maximize energy generation, and minimize downtime.
Cost Savings: Proactive maintenance and optimized inventory and resource management led to cost savings by reducing unplanned repairs, minimizing spare part inventory costs, and optimizing manpower contracts.
Reduced Unplanned Downtime: By employing predictive maintenance, component failures were anticipated and addressed proactively which reduced planned downtime and production disruptions.
Improved Product Quality: Optimizing casting parameters and process settings based on data analysis and machine learning algorithms enhanced product quality and reduced the occurrence of defects. This led to higher customer satisfaction and better market competitiveness.
Increased Productivity: Through process optimization and real-time monitoring, steel manufacturers minimized casting delays and production bottlenecks. This resulted in improved productivity, optimized resource utilization, and higher throughput.
Cost Savings: By reducing unplanned downtime, minimizing quality defects, and improving process efficiency, steel manufacturers achieved significant cost savings. Operational costs associated with repairs, rework, and production losses were reduced, positively impacting the bottom line.
Results
Results
Improved Prediction Failure:
>60 % of the failures were predicted in advance and corrective actions were taken.
Increased Production:
>40 tons increase in steel production (per annum) after a full scale roll out across all the 8 strands of the billet caster.
Reduced Defects:
>40% reduction in quality defects.
Improved Accuracy: Predictions of component failure up to 2 months in advance.
Increased Production: Increased generation for a 2.5 MW wind turbine.
Reduced Downtime:
5 days of turbine shutdown time reduced through equipment procurement ahead of failure.
Conclusion: The continuous billet casting process in the steel industry can benefit greatly from the implementation of Industry.AI. By leveraging predictive maintenance, process optimization, and real-time monitoring, steel manufacturers can reduce unplanned downtime, improve product quality, enhance productivity, and achieve cost savings. The ability to address component failures, quality defects, billet breakout, and casting delays led to a streamlined and efficient production process, ultimately positioning steel manufacturers for success in a competitive market.
Conclusion: The continuous billet casting process in the steel industry can benefit greatly from the implementation of Industry.AI. By leveraging predictive maintenance, process optimization, and real-time monitoring, steel manufacturers can reduce unplanned downtime, improve product quality, enhance productivity, and achieve cost savings. The ability to address component failures, quality defects, billet breakout, and casting delays led to a streamlined and efficient production process, ultimately positioning steel manufacturers for success in a competitive market.

Get In Touch

Get In Touch

Scroll to Top