An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower

HOŞÖZ M., ERTUNÇ H. M., BULGURCU H.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.38, sa.11, ss.14148-14155, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 11
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.eswa.2011.04.225
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.14148-14155
  • Anahtar Kelimeler: Refrigeration, Cooling tower, Adaptive neuro-fuzzy inference system (ANFIS), Prediction, HEAT-PUMP SYSTEM, NETWORK ANALYSIS, EVAPORATIVE CONDENSER, WATER
  • Kocaeli Üniversitesi Adresli: Evet

Özet

This paper investigates the applicability of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance of an R134a vapor-compression refrigeration system using a cooling tower for heat rejection. For this aim, an experimental system was developed and tested at steady state conditions while varying the evaporator load, dry bulb temperature and relative humidity of the air entering the tower, and the flow rates of air and water streams. Then, utilizing some of the experimental data for training, an ANFIS model for the system was developed. This model was used for predicting various performance parameters of the system including the evaporating temperature, compressor power and coefficient of performance. It was found that the predictions usually agreed well with the experimental data with correlation coefficients in the range of 0.807-0.999 and mean relative errors in the range of 0.83-6.24%. The results suggest that the ANFIS approach can be used successfully for predicting the performance of refrigeration systems with cooling towers. (C) 2011 Elsevier Ltd. All rights reserved.