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The selection of optimal design and the most efficient operational parameters for energy devices constitute a priority task for sustainable development and increasing energy efficiency within the net-zero emissions strategy. This is particularly important in adsorption cooling and desalination systems with poor performance due to unfavourable heat transfer conditions in conventional packed beds of adsorption chillers (ACs). Therefore, looking for additional ways of performance improvement is still challenging, especially covering different design variants and operational strategies. The existing complex, time-consuming and costly analytical, numerical and experimental methods, usually focused on a specific design and operating parameters of conventional packed adsorption beds, cannot tackle these comprehensive problems. Since artificial intelligence (AI) based models are considered tools that sometimes may overcome the shortcomings of the programmed computing approach and the experimental procedures, the paper introduces automated machine learning (AutoML) as a general approach for the design and optimization study of adsorption cooling and desalination systems. The double-effect, i.e. specific cooling capacity (SCP) and specific daily water production (SDWP) of various adsorption chillers (ACs) operating in large-, pilot- and small-scale adsorption cooling and desalination systems, is considered in the study. The paper also presents a novel big data optimization procedure for selecting the best operating and design strategy in adsorption cooling and desalination technology. Finally, a new concept of fluidized bed-type application in adsorption chillers is proposed, which allows for enhancing the performance of ACs. The presented approach can be referred to as a complementary design technique in adsorption cooling and desalination systems, besides the existing complex analytical and time-consuming numerical methods and expensive experiments. • Double-effect desalination and cooling production in adsorption chillers (AC) are evaluated • The new approach allows considering various cooling and desalination adsorption systems. • The novel concept of fluidized bed application in adsorption systems is proposed. • The developed models constitute powerful tools for optimizing ACs systems' performance. [ABSTRACT FROM AUTHOR]

• The artificial intelligence approach for modeling adsorption processes in adsorption cooling and desalination systems with fluidized adsorption beds is presented. • The prediction performance of four predictive methods is compared. • The proposed model shows forecast skill score in a range of about 97%–99%. • GRU method has high accuracy and shortest computation time. The article presents deep learning methods applied to predict the mass of an adsorption bed in the fixed and fluidized bed. The purpose of the application of this kind of bed is to improve the efficiency of the adsorption cooling systems by increased heat and mass transfer by using fluidization. The paper employs three deep learning methods: Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU). In each of the selected neural networks, the epoch values, the number of neurons on the first layer and the number of neurons on the second layer were changed. These networks had two hidden layers. The paper presents numerical research on mass prediction using the algorithm mentioned above for silica gel as sorbent with copper, aluminum, carbon nanotubes additives. The results obtained by the developed algorithms of the LSTM, BiLSTM, GRU network and experimental tests are in good agreement with R 2 above 0.97. The GRU network guarantees predicting the mass of both the fluidized and fixed beds with the best agreement with the measurement results. [ABSTRACT FROM AUTHOR]

Abstract: Burning lean mixture in spark ignition (SI) engine leads to decrease in temperature of combustion process and is one of the methods of limiting nitric oxide emission and increasing the engine efficiency. The two-stage combustion system of stratified mixture (engine with prechamber) can be an effective method of lean mixture combustion. The paper presents the results of three-dimensional modeling of fuel mixture preparation and combustion in SI engine with sectional combustion chamber powered by liquefied fuel. Three dimensional modeling was performed in KIVA-3V code. The modeling results were compared with results obtained from the analysis of experimental measurements of two-stage combustion test engine operating at the Institute of Internal Combustion Engines and Control Engineering (Czestochowa University of Technology). The performed simulations of the combustion process provided data concerning the spatial and temporal distributions of turbulent kinetic energy, pressure, temperature and nitric oxides concentration in the combustion chambers of the engine. The engine model with two-stage combustion system properly represents the real processes which occur in the combustion chambers of the test engine. Pressure and temperature courses in function of CA obtained from the experiment and modeling were in good qualitative and quantitative consistence. Comparison of modeled and measured nitric oxide emissions revealed relatively significant discrepancies. In case of λ =1.4, the measured values of NO x concentration were 1.75 times higher than the modeled values. In case of λ =2.0, the modeled and measured values were close to each other and were within the range of measurement error. [Copyright &y& Elsevier]

• Numerical simulations of CuO-based oxygen carrier in the CLC process were performed. • The experimental research was carried out on the FB-CLC-SF unit. • The 4th Generation of the CeSFaMB simulator was used to generate 1.5D CLC model. The paper deals with numerical simulations of CuO-based oxygen carrier in the Chemical Looping Combustion (CLC) process by the use of CeSFaMB software. The oxygen carrier used in the study provides high both reaction rates and oxygen transfer capacity. However, despite these good properties the material tends to sinter at higher temperatures. Therefore, the support materials such as: ilmenite (20%) and fly ash (20%) was applied in order to improve features of the oxygen carrier. In this work, the 4th Generation of the CeSFaMB simulator was used to generate 1.5D CLC model for the CuO-based oxygen carrier. The developed comprehensive model includes hydrodynamics of fluidized bed and reaction kinetics of the CLC process. Successful validation of modeling results are performed on experimental results, obtained from the Fluidized-Bed Chemical-Looping-Combustion of Solid-Fuels (FB-CLC-SF) unit, which is located at the Institute of Advanced Energy Technologies, Czestochowa University of Technology, Poland. The validation results showed good accuracy of the developed 1.5D CLC model. The maximum relative error between simulations and experiment results is approximately 8%. [ABSTRACT FROM AUTHOR]