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- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received July 18, 2022
Revised November 2, 2022
Accepted December 16, 2022
- Acknowledgements
- This work was partly supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE). (P0008475, Development Program for Smart Digital Engineering Specialist) and Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20207200000070, Development of performance standardization and operation risk estimation for renewable energylinked alkaline water electrolysis hydrogen production systems using digital t
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Multiple linear regression and GRU model for the online prediction of catalyst activity and lifetime in counter-current continuous catalytic reforming
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Abstract
In the catalytic reforming process, aromatic yield is a standard for evaluating the production quality of the
process, and studies are underway to improve productivity by optimizing the cost and energy. In particular, the activity
and lifetime prediction of reforming catalysts can contribute to overall process efficiency improvement, such as product quality, productivity, and predictive maintenance. However, it is difficult to predict real-time catalyst activity and
lifetime according to changes in process operation with the existing method that requires experimental data. In this
study, a multiple linear regression (MLR) model and GRU model with the real process operating data are proposed for
long-term plant operation and optimization in the counter-current continuous catalytic reforming. The MLR-GRU
model predicts catalyst performance degradation and lifetime according to operating conditions by defining a new
variable, reforming catalyst activity. The proposed model can predict the future reformate yield with an error of less
than 1%. As a result of predicting the catalyst lifetime according to various operating temperatures, feed flow patterns,
and feed quality, the feed flow rate had the greatest influence on the catalyst lifetime profile. In terms of the amount of
produced reformate oil, the case with maximum feed rate is the worst (25.6%); on the other hand, the case with minimum feed rate is the best (+11.4%). Thus, it is important to establish an appropriate production plan of the produced
reformate oil. The model proposed in this study can predict the reformate yield and lifetime, reflecting the degradation
of catalyst performance according to the operating profile in real-time, which is expected to improve productivity by
production scheduling, optimization, and predictive maintenance.
Keywords
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