Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)

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...

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Bibliographic Details
Published in:Energies (Basel) 2020-12, Vol.13 (24), p.6601
Main Authors: Skrobek, Dorian, Krzywanski, Jaroslaw, Sosnowski, Marcin, Kulakowska, Anna, Zylka, Anna, Grabowska, Karolina, Ciesielska, Katarzyna, Nowak, Wojciech
Format: Article
Language:English
Subjects:
Adsorption
Algorithms
Artificial intelligence
Atoms & subatomic particles
Chambers
Coal-fired power plants
Datasets
Deep learning
Evaporators
Fluidized beds
Heat exchangers
Heat transfer
Heat transfer coefficients
Literature reviews
Long short-term memory
Long Short-Term Memory (LSTM)
Neural networks
Sensors
Sorbents
Sorption
sorption processes
Teaching methods
Time series
Valves
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13246601