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.
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.
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.
Implementing Industry.AI in the continuous billet casting process yields several benefits:
Implementing Industry.AI in the continuous billet casting process yields several benefits:
>60 % of the failures were predicted in advance and corrective actions were taken.
>40 tons increase in steel production (per annum) after a full scale roll out across all the 8 strands of the billet caster.
>40% reduction in quality defects.
5 days of turbine shutdown time reduced through equipment procurement ahead of failure.