Emergency Shutdown Prediction: Anticipating Events 7 Days in Advance

Chemical
Predictive Maintenance

Challenge

Chemical processes are inherently complex, necessitating sophisticated setups. Sudden shutdowns, apart from incurring substantial operating cost losses, pose significant environmental and safety risks, particularly when abnormal reactions occur in the process. Historically, these incidents have led to emergency shutdowns (ESD) initiated by on-site engineers, resulting in considerable downtime losses. Consequently, there's a critical need to proactively detect equipment behavior anomalies in the process and prevent potential emergency shutdowns.

Approach

MakinaRocks addressed this challenge by developing deep learning-based anomaly detection models. These models were trained to understand the normal data distribution characteristics during the regular operation of the process. They were then capable of determining deviations from these normal characteristics. This approach was specifically applied to predict reactor anomalies in petrochemical PP processes, aiming to prevent sudden, unplanned shutdowns.

Value Delivered

The developed models successfully predicted reactor anomalies in the petrochemical PP process up to 7 days in advance. This early detection of potential emergency shutdowns significantly minimized costs and reduced environmental and safety risks associated with these events.

Emergency Shutdown Prediction: Anticipating Events 7 Days in Advance

Challenge

Approach

Value Delivered

Related Use Cases

Industrial Robot Anomaly Detection: Failures Predicted 5 Days Ahead

Solar Power Prediction: 3,000+ Models Deployed Across 787 Plants

Energy Storage System (ESS) Anomaly Detection: 12 Hours in Advance