| Title: | Artificial intelligence - based exergy analysis of an absorption cooling system |
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| Authors: | ID Strušnik, Dušan (Author) |
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| Files: |  Strusnik_2025_Artificial_intelligence_based_exergy_analysis.pdf (1,13 MB)
MD5: D108143C617449811D73B637D7BE9762
 https://doi.org/10.18690/jet.18.1.21-34.2025
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FE - Faculty of Energy Technology
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| Abstract: | An artificial intelligence (AI)-based exergy analysis of an absorption cooling system (ACS), utilizing a lithium bromide–water refrigeration cycle, is presented in this paper. The ACS is characterised by the utilisation of the intermediate-pressure (IP) extraction steam from the steam turbine for its operation. The exergy analysis of the ACS is detailed, based on AI modelling through a machine learning algorithm, which predicts and optimises the ACS performance. The machine learning algorithm is validated using real process data obtained through ACS measurements via the supervisory control and data acquisition (SCADA) system. The AI results show that the ACS generates 126.71 kW of cooling for district cooling and 279.57 kW of heat, which is used for heating demineralised water. During the analysis period, the ACS consumed an average of 152.86 kW of IP steam, and operated with an average exergy efficiency of 17.3%. The study suggests that the average exergy efficiency of the ACS could be improved by using lower-quality steam, or even hot water, for operation. |
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| Keywords: | absorption, analysis, artificial intelligence, cooling, efficiency, exergy |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 02.04.2025 |
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| Article acceptance date: | 07.04.2025 |
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| Publication date: | 30.05.2025 |
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| Publisher: | Fakulteta za energetiko |
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| Year of publishing: | 2025 |
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| Number of pages: | Str. 21-34 |
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| Numbering: | Letn. 18, št. 1 |
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| PID: | 20.500.12556/DKUM-93248  |
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| UDC: | 620.9 |
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| ISSN on article: | 1855-5748 |
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| COBISS.SI-ID: | 239049731 |
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| DOI: | 10.18690/jet.18.1.21-34.2025 |
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| Publication date in DKUM: | 16.06.2025 |
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| Views: | 246 |
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| Downloads: | 12 |
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| Metadata: |    |
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| Categories: | Misc.
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