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

Abstract

A detailed analysis of energy and exergy is conducted on a single-effect solar ammonia–water (NH3–H2O) absorption refrigeration cycle (ARC) using TRNSYS and EES software. Considering the physical and chemical exergies, the exergy destruction rate (ĖD) in each component of the system is calculated, highlighting its contribution to the overall ĖD. The study explores the effects of varying refrigerant mass flow rate (ṁᵣ) and ammonia concentration in strong and weak solutions (Xs and Xw) on key performance parameters, including coefficient of performance (COP), exergy efficiency (ĖD), and overall ĖD across a range of generator temperatures (Tg). 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 ηηEwhile minimizing Tg and ĖD. The GBRT models are developed using available experimental and simulation data, revealing relationships between variables (ṁᵣ, Xs, Xw, and Tg) and outcomes (COP, ĖD, and overall ĖD). The results revealed that the generator exhibits considerable ĖD regardless of operating conditions, underscoring its pivotal role in the ARC. It emerges as the primary ĖD contributor (50 %), followed by the evaporator (17 %) and the absorber (15 %). However, ĖD associated with the recooler, pump, and expansion valves is negligible in comparison. Optimization results reveal that, when minimizing Tg and ĖD, the highest COP and ĖD at Tg of 373.15 K reach 0.8081 and 0.46, respectively.