Predictive maintenance in catalysis involves several key steps:
1. Data Collection: Sensors and other monitoring tools collect real-time data on various parameters like temperature, pressure, and chemical concentrations. 2. Data Analysis: Advanced algorithms and machine learning models analyze the collected data to identify patterns and anomalies that could indicate potential failures. 3. Prediction: Based on the analysis, the system predicts when a catalyst is likely to fail or require maintenance. 4. Action: Maintenance teams receive alerts and can take proactive measures to address the issue before it leads to a shutdown.
