Vol.11, No.1, February 2022.                                                                                                                                                                           ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Random Forest Regression to Predict Catalyst Deactivation in Industrial Catalytic Process


Wisnu Hafi Hanif, Fergyanto E Gunawan


© 2022 Fergyanto E Gunawan, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 11, Issue 1, Pages 104-110, ISSN 2217-8309, DOI: 10.18421/TEM111-12, February 2022.


Received: 01 October 2021.

Revised:   07 December 2021.
Accepted: 12 December 2021.
Published: 28 February 2022.




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.


Keywords – catalyst deactivation, heterogenous catalyst, petrochemical process, machine learning, random forest regression.



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