Towards enhanced heat and mass exchange in adsorption systems: The role of AutoML and fluidized bed innovations

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.