Anomaly detection in catalysis typically involves several steps: 1. Data Collection: Gathering data from the catalytic process, including reaction conditions, product concentrations, and catalyst properties. 2. Data Preprocessing: Cleaning and normalizing the data to remove noise and irrelevant information. 3. Modeling Normal Behavior: Using statistical or machine learning models to establish a baseline of normal catalytic behavior. 4. Detection Algorithms: Applying algorithms to compare real-time data against the established baseline to identify anomalies.
