Random Forest Regression to Predict Catalyst Deactivation in Industrial Catalytic Process
Wisnu Hafi Hanif, Fergyanto E. Gunawan
Abstract
Catalyst deactivation has become a great concern in an industry with heterogenous catalystbased production. An accurate model to predict catalyst performance is needed to optimize the maintenance schedule, avoid an unplanned shutdown, and ensure reliable operation. This research work applies a machine learning model to predict catalyst deactivation based on actual data from relevant multitube-reactor sensors. The product conversion is a crucial indicator of the catalyst performance degradation over time. Random forest regression (RFR) algorithm is chosen to construct the model. Hyperparameter tuning is applied and shows improvement over the default model. The result showed that the RFR model could predict the conversion as a time series function. The feature importance analysis shows the most influencing factor and facilitates the model interpretation.