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 and Wojciech Nowak
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Dorian Skrobek: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Jaroslaw Krzywanski: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Marcin Sosnowski: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Anna Kulakowska: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Anna Zylka: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Karolina Grabowska: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Katarzyna Ciesielska: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Wojciech Nowak: Faculty of Energy and Fuel, AGH University of Science and Technology, A. Mickiewicza 30, 30-059 Cracow, Poland

Energies, 2020, vol. 13, issue 24, 1-16

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.

Keywords: sorption processes; deep learning; neural networks; Long Short-Term Memory (LSTM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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