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 learning algorithm. 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 trained using a deep learning algorithm based on the developed simulator. The model combines the 1D convolutional neural network and long-short-term memory (LSTM) network. The prediction of the degradation degree of the internal adsorbent was then presented using a pretrained neural network. The developed methodology demonstrates a mean squared error lower than 10−6 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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Abbreviations
- Aw :
-
wall cross-sectional area [m2]
- C:
-
diffusion time constant [sec−1]
- Cpg, Cps, Cpw :
-
heat capacity of gas, pellet and wall [J/kg K]
- DL :
-
axial dispersion coefficient [m2/s]
- hi :
-
internal heat transfer coefficient [J/m2 K s]
- ho :
-
external heat transfer coefficient [J/m2 K s]
- ΔH:
-
heat of adsorption [J/mol]
- KL :
-
axial thermal conductivity [J/m K s]
- kd :
-
adsorbent degradation factor [-]
- L:
-
bed length [m]
- P:
-
total pressure [Pa]
- q, q*, \(\overline {\rm{q}} \) :
-
adsorption loading amount, adsorption isotherm, average concentration [mmol/g]
- R:
-
gas constant [8.31447 J/mol K]
- RP :
-
pellet radius [m]
- RBi :
-
bed inner radius [m]
- RBo :
-
bed outer radius [m]
- t:
-
time [sec]
- Tamb :
-
ambient temperature [K]
- T:
-
bed temperature [K]
- Tw :
-
wall temperature [K]
- u:
-
interstitial velocity [m/s]
- yi :
-
mole fraction of species i in gas phase [-]
- z:
-
axial distance in bed from the feed gas inlet [m]
- ADS:
-
adsorption step
- BD:
-
blowdown step
- DEQ:
-
depressurizing pressure equalization step
- PEQ:
-
pressurizing pressure equalization step
- PR:
-
pressurization step
- PG:
-
purge step
- LSTM:
-
long-short term memory
- α :
-
particle porosity [-]
- ε :
-
voidage of adsorbent bed [-]
- ε t :
-
total void fraction [-]
- μ :
-
viscosity [Pa sec]
- ρ g, ρp, ρB, ρw :
-
density of gas, pellet, bulk and bed wall [kg/m3]
- ω :
-
linear driving force (LDF) coefficient [sec−1]
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Acknowledgement
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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Son, S. Determining adsorbent performance degradation in pressure swing adsorption using a deep learning algorithm and one-dimensional simulator. Korean J. Chem. Eng. 40, 2602–2611 (2023). https://doi.org/10.1007/s11814-023-1524-x
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DOI: https://doi.org/10.1007/s11814-023-1524-x