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Predictive Maintenance for Rotating Equipment (Compressor – MAB)

Problem

The main air blower (MAB) in the Fluid Catalytic Cracking (FCC) unit of a refinery is a critical centrifugal compressor that provides air necessary for combustion in the regenerator. The smooth functioning of the MAB is essential for the overall performance of the FCC unit. Unplanned shutdowns of the MAB can result in significant losses, including direct production losses, additional maintenance costs, and indirect efficiency losses due to unwanted downtime. The challenges faced by the refinery include:

  • High Downtime Impact: Unplanned shutdown of the MAB leads to the shutdown of the entire FCC unit, resulting in substantial losses in terms of production, maintenance, and efficiency. 
  • Frequency of Unplanned Shutdowns: The MAB has experienced multiple unplanned shutdowns, leading to financial losses and decreased production efficiency. 
  • Monitoring Complexity: The MAB requires monitoring of over 200+ tags, and the lack of a single source of truth for 20 assets causes incomplete visibility into the system. 

Approach

To minimize unplanned shutdowns and gain visibility over MAB operations, the refinery adopted Algo8’s Predictive Maintenance application. The approach includes the following steps:

  • Process and Machine Data Integration: The Predictive Maintenance application leverages both process and machine data to detect issues early. It uses historical data from the equipment and the process to train and fine-tune anomaly and predictive models. 
  • Anomaly Detection Engine: Algo8’s Anomaly Detection Engine uses supervised and unsupervised machine learning algorithms to identify anomalous behavior in the MAB. It also determines the contributing factors responsible for the anomalous behavior, which could be related to process or operational issues. 
  • Root Cause Analysis with Generative AI: The application uses Generative AI enabled Failure Mode and Effects Analysis (FMEA) to identify the failure mode responsible for the primary cause of the problem, leading to a better understanding of the underlying issues. 
  • Predictive Model: The Predictive Maintenance application includes a predictive model that forecasts the remaining time to failure of the equipment. This early prediction allows operators to take action and prevent unplanned shutdowns. 
  • Recommendations with Generative AI: The application provides recommendations through Generative AI enabled what-if scenarios. These recommendations help manage equipment failures proactively and extend the operating window of the MAB. 

Benefit

The implementation of Algo8’s Predictive Maintenance application resulted in several benefits for the refinery:

  • Minimized Production Losses: By avoiding unplanned shutdowns, the refinery experienced minimized production losses, ensuring continuous operation and optimized productivity. 
  • Reduced O&M Spend: The reduced frequency of unscheduled maintenance downtime led to cost savings in operations and maintenance expenses. 
  • Decreased Safety Incidents: Predictive maintenance reduced the likelihood of safety incidents caused by unexpected shutdowns and equipment failures. 

Impact

The successful implementation of Algo8’s Predictive Maintenance application had a significant impact on the refinery: 

The refinery managed to save $7.5 million per shutdown, ensuring 100% asset availability by proactively addressing issues and preventing unplanned shutdowns. This not only improved the financial performance of the refinery but also increased overall operational efficiency and safety.