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- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
-
Received March 30, 2023
Revised May 28, 2023
Accepted June 29, 2023
- Acknowledgements
- This study was conducted with the support of the Kyungpook National University and Research Insititute of Industrial Science and Technology (RIST) individual project
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Determining adsorbent performance degradation in pressure swing adsorption using a deep learning algorithm and one-dimensional simulator
1Department of Smart Plant Engineering, Kyungpook National University, Sangju 37224, South Korea 2Department of Convergence & Fusion System Engineering, Kyungpook National University, Sangju 37224, South Korea
seongminson@knu.ac.kr
Korean Journal of Chemical Engineering, November 2023, 40(11), 2602-2611(10), 10.1007/s11814-023-1524-x
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Abstract
This study proposes a methodology for diagnosing the degree of performance degradation of the adsorbent in pressure swing adsorption (PSA) plants using a one-dimensional simulator and a time-series deep learningalgorithm. First, a 1D PSA simulator was developed using mathematical models and validated with previously published experimental data. The behavior change of the PSA plant according to the performance degradation was trainedusing a deep learning algorithm based on the developed simulator. The model combines the 1D convolutional neuralnetwork and long-short-term memory (LSTM) network. The prediction of the degradation degree of the internaladsorbent was then presented using a pretrained neural network. The developed methodology demonstrates a meansquared error lower than 106 when predicting the degree of adsorbent degradation from the adsorption-bed-temperature time-series profiles with an example. The methodology can be used to predictive maintenance strategy by identifying PSA performance degradation in real time without stopping operation
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