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.

Global CO₂ concentration level in the air is unprecedently high and should be rapidly and significantly reduced to avoid a global climate catastrophe. The work indicates the possibility of quickly lowering the impact of changes that have already happened and those we know will happen, especially in terms of the CO₂ emitted and stored in the atmosphere, by implanting a virgin ivy plant on the available area of walls and roofs of the houses. The proposed concept of reducing CO₂ from the atmosphere is one of the technologies with significant potential for implementation entirely and successfully. For the first time, we showed that the proposed concept allows over 3.5 billion tons of CO₂ to be captured annually directly from the atmosphere, which makes even up 6.9% of global greenhouse gas emissions. The value constitutes enough high CO₂ reduction to consider the concept as one of the applicable technologies allowing to decelerate global warming. Additional advantages of the presented concept are its global nature, it allows for the reduction of CO₂ from all emission sources, regardless of its type and location on earth, and the fact that it will simultaneously lower the air temperature, contribute to oxygen production, and reduce dust in the environment.

Chemical looping combustion (CLC) is one of the most advanced technologies allowing for the reduction in CO₂ emissions during the combustion of solid fuels. The modified method combines chemical looping with oxygen uncoupling (CLOU) and in situ gasification chemical looping combustion (iG-CLC). As a result, an innovative hybrid chemical looping combustion came into existence, making the above two technologies complementary. Since the complexity of the CLC is still not sufficiently recognized, the study of this process is of a practical significance. The paper describes the experiences in the modelling of complex geometry CLC equipment. The experimental facility consists of two reactors: an air reactor and a fuel reactor. The paper introduces the fuzzy logic (FL) method as an artificial intelligence (AI) approach for the prediction of SO₂ and NO_x (i.e., NO + NO₂) emissions from coal and biomass combustion carried out in air-firing; oxyfuel; iG-CLC; and CLOU conditions. The developed model has been successfully validated on a 5 kW_th research unit called the dual fluidized bed chemical looping combustion of solid fuels (DFB-CLC-SF).

The first experience in the operation of intensified heat transfer adsorption bed reactor designed for low-pressure adsorption processes is presented in this paper. This work aims to assess the possibility of fluidizing the porous media bed induced by the pressure difference between the evaporator and the adsorption reactor. The conducted experimental research allowed indicating the type of silica gel recommended to use in fluidized beds of adsorption chiller. The fixed bed of silica gel was observed for the lower pressure differences, while fluidization appeared in the case of the pressure difference between the evaporator and the adsorption chamber higher than 1000 Pa. The most significant differences in the adsorption process between the fixed bed and the fluidized bed are revealed in the changes of sorbent temperatures. The silica gel bed was fluidized with water vapor generated in the evaporator.

In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science. Man needs intelligent machines to create and discover new relationships in the world, so AI is beginning to reach various areas of science, such as medicine, economics, management, and the power industry. Artificial intelligence is one of the most exciting directions in the development of computer science, which absorbs a considerable amount of human enthusiasm and the latest achievements in computer technology. This article was dedicated to the practical use of artificial neural networks. The article discusses the development of neural networks in the years 1940–2022, presenting the most important publications from these years and discussing the latest achievements in the use of artificial intelligence. One of the chapters focuses on the use of artificial intelligence in energy processes and systems. The article also discusses the possible directions for the future development of neural networks.