Implementation of deep learning methods in prediction of adsorption processes. (November 2022)

• Record Type: Journal Article
• Authors: Skrobek, Dorian; Krzywanski, Jaroslaw; Sosnowski, Marcin; Kulakowska, Anna; Zylka, Anna; Grabowska, Karolina; Ciesielska, Katarzyna; Nowak, Wojciech
• Is Part Of: Advances in engineering software. Volume 173(2022)
• Publication Date: 2022-11
• Subjects: Deep learning -- Neural networks -- Long short-term memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Gated recurrent unit (GRU) -- Sorption processes; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103190
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: The artificial intelligence approach for modeling adsorption processes in adsorption cooling and desalination systems with fluidized adsorption beds is presented. The prediction performance of four predictive methods is compared. The proposed model shows forecast skill score in a range of about 97%–99%. GRU method has high accuracy and shortest computation time. Abstract: 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.