A novel hybrid experimental – ANN framework for thermo-exergetic evaluation of salinity gradient solar ponds

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Özcan Y., Deniz E., Gürdal M., EKMEKÇİ İ.

International Journal of Exergy, cilt.50, sa.1, ss.38-56, 2026 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 1
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1504/ijex.2026.153926
  • Dergi Adı: International Journal of Exergy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Sayfa Sayıları: ss.38-56
  • Anahtar Kelimeler: energy and exergy analyses, machine learning, salinity gradient solar pond
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • İstanbul Ticaret Üniversitesi Adresli: Evet

Özet

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.