ANN Modeling of an ORC-Binary Geothermal Power Plant: Simav Case Study

Arslan O., Yetik Ö.

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, cilt.36, sa.4, ss.418-428, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 4
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1080/15567036.2010.542437
  • Dergi Adı: ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.418-428
  • Anahtar Kelimeler: artificial neural network, life cycle cost, geothermal energy, Levenberg-Marquardt, Organic Rankine Cycle-Binary, Pola-Ribiere conjugate gradient, scaled conjugate gradient, ARTIFICIAL NEURAL-NETWORKS, ELECTRICITY-GENERATION, ENERGY, PREDICTION
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Due to recently developed technologies, there is now more availability to generate electricity. One of these technologies is the binary cycle system, a kind of Organic Rankine Cycle. Since the design of these technologies requires more proficiency and longer times within complex calculations, artificial neural network is a new tool used to make rapid decisions and modeling of the processes within the expertise. The most suitable algorithm was found to be Levenberg-Marguardt with 20 neurons in a single hidden layer for o2 and o3 type cycles, whereas it was found to be Levenberg-Marguardt with 22 neurons for the b3 type cycle.