Szczegóły publikacji
Opis bibliograficzny
Implementation of deep learning methods in prediction of adsorption processes / Dorian Skrobek, Jaroslaw Krzywanski, Marcin Sosnowski, Anna Kulakowska, Anna Zylka, Karolina Grabowska, Katarzyna Ciesielska, Wojciech NOWAK // Advances in Engineering Software ; ISSN 0965-9978 . — 2022 — vol. 173 art. no. 103190, s. 1–13. — Bibliogr. s. 12–13, Abstr. — Publikacja dostępna online od: 2022-08-13
Autorzy (8)
- Skrobek Dorian
- Krzywański Jarosław
- Sosnowski Marcin
- Kulakowska Anna
- Zylka Anna
- Grabowska Karolina
- Ciesielska Katarzyna
- AGHNowak Wojciech Janusz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 141936 |
|---|---|
| Data dodania do BaDAP | 2022-09-09 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.advengsoft.2022.103190 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Advances in Engineering Software |
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 R2 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.
