Maximizing efficiency in solar ammonia–water absorption refrigeration cycles: Exergy analysis, concentration impact, and advanced optimization with GBRT machine learning and FHO optimizer.

• Detailed energy and exergy analyses are conducted on the proposed ARC. • Explore the effects of varying refrigerant mass flow rate and ammonia concentration in strong and weak solutions on key performance parameters. • Employing the GBRT machine learning method and FHO approach in the ARC is highly recommended. • The GBRT model proves to be a reliable predictive tool with strong accuracy, offering valuable insights into system performance. • The generator is the main source of exergy destruction rate (50 %). A detailed analysis of energy and exergy is conducted on a single-effect solar ammonia–water (NH 3 –H 2 O) absorption refrigeration cycle (ARC) using TRNSYS and EES software. Considering the physical and chemical exergies, the exergy destruction rate (Ė D) in each component of the system is calculated, highlighting its contribution to the overall Ė D. The study explores the effects of varying refrigerant mass flow rate (ṁ ᵣ) and ammonia concentration in strong and weak solutions (X s and X w) on key performance parameters, including coefficient of performance (COP), exergy efficiency (Ė D), and overall Ė D across a range of generator temperatures (T g). In this study, a gradient boosting regression tree (GBRT) is employed as a supervised machine-learning technique for classification and regression problems, utilizing boosting to enhance conventional decision tree predictions. The Fire Hawk Optimizer (FHO) approach is also utilized to optimize performance parameters, maximizing COP and η η E while minimizing T g and Ė D. The GBRT models are developed using available experimental and simulation data, revealing relationships between variables (ṁ ᵣ, X s , X w , and T g) and outcomes (COP, Ė D , and overall Ė D). The results revealed that the generator exhibits considerable Ė D regardless of operating conditions, underscoring its pivotal role in the ARC. It emerges as the primary Ė D contributor (50 %), followed by the evaporator (17 %) and the absorber (15 %). However, Ė D associated with the recooler, pump, and expansion valves is negligible in comparison. Optimization results reveal that, when minimizing T g and Ė D , the highest COP and Ė D at T g of 373.15 K reach 0.8081 and 0.46, respectively. [ABSTRACT FROM AUTHOR]