The current development of chemical looping combustion (CLC) technology is presented in this paper. This technique of energy conversion enables burning of hydrocarbon fuels with dramatically reduced CO₂ emission into the atmosphere, since the inherent separation of carbon dioxide takes place directly in a combustion unit. In the beginning, the general idea of the CLC process is described, which takes advantage of solids (so-called oxygen carriers) being able to transport oxygen between combustion air and burning fuel. The main groups of oxygen carriers (OC) are characterized and compared, which are Fe-, Mn-, Cu-, Ni-, and Co-based materials. Moreover, different constructions of reactors tailored to perform the CLC process are described, including fluidized-bed reactors, swing reactors, and rotary reactors. The whole systems are based on the chemical looping concept, such as syngas CLC (SG-CLC), in situ Gasification CLC (iG-CLC), chemical looping with oxygen uncoupling (CLOU), and chemical looping reforming (CLR), are discussed as well. Finally, a comparison with other pro-CCS (carbon capture and storage) technologies is provided.

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.