A novel hybrid experimental – ANN framework for thermo-exergetic evaluation of salinity gradient solar ponds
International Journal of Exergy, vol.50, no.1, pp.38-56, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 50 Issue: 1
- Publication Date: 2026
- Doi Number: 10.1504/ijex.2026.153926
- Journal Name: International Journal of Exergy
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
- Page Numbers: pp.38-56
- Keywords: energy and exergy analyses, machine learning, salinity gradient solar pond
- Open Archive Collection: AVESIS Open Access Collection
- İstanbul Ticaret University Affiliated: Yes
Abstract
In the hybrid experimental study conducted, the thermal and exergy performance of a salinity gradient solar pond (SGSP) was tested under real outdoor conditions and later neural network (ANN) method was developed to predict temperatures at various depths of the pond. The SCG algorithm was used in the optimised ANN model. The highest thermal efficiency was found to be around 20.68, and the exergy efficiency was close to 0.81. The ANN model performed well, reaching an average R² value above 0.998. These outcomes show that the proposed model can successfully forecast pond temperatures with high reliability.