Implementation of deep learning methods in prediction of adsorption processes

The article presents deep learning methods applied to predict the mass of an adsorption bed in the fixed and fluidized bed. The purpose of the application of this kind of bed is to improve the efficiency of the adsorption cooling systems by increased heat and mass transfer by using fluidization. The paper employs three deep learning methods: Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU). In each of the selected neural networks, the epoch values, the number of neurons on the first layer and the number of neurons on the second layer were changed. These networks had two hidden layers. The paper presents numerical research on mass prediction using the algorithm mentioned above for silica gel as sorbent with copper, aluminum, carbon nanotubes additives. The results obtained by the developed algorithms of the LSTM, BiLSTM, GRU network and experimental tests are in good agreement with R-2 above 0.97. The GRU network guarantees predicting the mass of both the fluidized and fixed beds with the best agreement with the measurement results.

Automated summary

This article presents the application of deep learning methods to predict the mass of an adsorption bed. The research shows that the GRU network achieves the best agreement with the measurement results in predicting the mass of both fixed and fluidized beds.