TY - JOUR
T1 - Towards enhanced heat and mass exchange in adsorption systems
T2 - The role of AutoML and fluidized bed innovations
AU - Krzywanski, Jaroslaw
AU - Skrobek, Dorian
AU - Sosnowski, Marcin
AU - Ashraf, Waqar Muhammad
AU - Grabowska, Karolina
AU - Zylka, Anna
AU - Kulakowska, Anna
AU - Nowak, Wojciech
AU - Sztekler, Karol
AU - Shahzad, Muhammad Wakil
N1 - Funding information: This work was performed within project No. 2018/29/B/ST8/00442, “Research on sorption process intensification methods in modified construction of adsorbent beds”, supported by the National Science Center, Poland. The support is gratefully acknowledged. Jaroslaw Krzywanski thanks William Disch (DataRobot Inc.) and Kinga Partyka (Partyka Consulting), and Sandra Mistal (student) for help in the project. Dr. Shahzad also would like to thank RAEng / Leverhulme Trust Research Fellowships Tranche 19 for FAM Project (LTRF2223-19-103) and Northumbria University UK.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - 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.
AB - 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.
KW - Big data
KW - Desalination
KW - Energy efficiency
KW - Net-zero emissions
KW - Sustainability
KW - Waste heat recovery
UR - http://www.scopus.com/inward/record.url?scp=85183459392&partnerID=8YFLogxK
U2 - 10.1016/j.icheatmasstransfer.2024.107262
DO - 10.1016/j.icheatmasstransfer.2024.107262
M3 - Article
AN - SCOPUS:85183459392
SN - 0735-1933
VL - 152
JO - International Communications in Heat and Mass Transfer
JF - International Communications in Heat and Mass Transfer
M1 - 107262
ER -