Szczegóły publikacji
Opis bibliograficzny
Prediction of sorption processes using the deep learning methods (long short-term memory) / Dorian Skrobek, Jaroslaw Krzywanski, Marcin Sosnowski, Anna Kulakowska, Anna Zylka, Karolina Grabowska, Katarzyna Ciesielska, Wojciech NOWAK // W: Adsorption desalination and cooling systems: advances in design, modeling and performance / eds. Jaroslaw Krzywanski, Norbert Skoczylas, Marcin Sosnowski. — Basel : MDPI, cop. 2022. — N. Skoczylas - afiliacja: The Strata Mechanics Research Institute of the Polish Academy of Sciences. — ISBN: 978-3-0365-5913-1; e-ISBN: 978-3-0365-5914-8. — S. 21–36. — Bibliogr. s. 34–36, Abstr. — Reprinted from: Energies 2020, 13, 6601, doi:10.3390/en13246601
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 | 143935 |
|---|---|
| Data dodania do BaDAP | 2022-12-21 |
| Tekst źródłowy | URL |
| Rok publikacji | 2022 |
| Typ publikacji | fragment książki |
| Otwarty dostęp |  |
| Creative Commons |
Abstract
The paper introduces the artificial intelligence (AI) approach for modeling fluidized adsorption beds. The idea of fluidized bed application allows a significantly increased heat transfer coefficient between adsorption bed and the surface of a heat exchanger, improving the performance of adsorption cooling and desalination systems. The Long Short-Term Memory (LSTM) network algorithm was used, classified as a deep learning method, to predict the vapor mass quantity in the adsorption bed. The research used an LSTM network with two hidden layers. The network used in the study is composed of seven inputs (absolute pressures in the adsorption chamber and evaporator, the temperatures in adsorption chamber and evaporator, relative pressure, the temperatures in the center of adsorption bed and 25 mm from the bed center, the kind of the solids mixture, the percentage value of the addition) and one output (mass of the sorption bed). The paper presents numerical research concerning mass prediction with the algorithm mentioned above for three sorbents in fixed ad fluidized beds. The results obtained by the developed algorithm of the LSTM network and the experimental tests are in good agreement of the matching the results above 0.95.
