Determining adsorbent performance degradation in pressure swing adsorption using a deep learning algorithm and one-dimensional simulator

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.