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A GEP-Based Model Approach for Estimating Thermodynamic Properties of R513A Refrigerant

Yıl 2021, , 376 - 388, 31.01.2021
https://doi.org/10.31202/ecjse.814527

Öz

The changes in the politic and environmentally are highly effective in refrigerant choices. Nowadays it is critical using environmentally friendly and energy-efficient refrigerants for a sustainable future. The R513A refrigerant provides both GWP reduction and increases energy efficiency. Accurate estimates of thermodynamic properties of refrigerant are critical when considering the changing of temperature in a small interval. Many data of thermodynamic properties of refrigerants are hard-to-reach data from literature and tables. In the present study, thermodynamic properties such as entropy, enthalpy and density are modelled using gene expression programming (GEP). In this context, for R513A new refrigerant determined mathematical equations through gene expression programming. Developed mathematical models for saturation and superheated R513A refrigerant under different temperature and pressure condition compared experimental data obtained from the literature. The results have shown that the GEP model could be considered as an efficient modelling technique for the estimate data of predicting thermodynamic properties of refrigerants.

Kaynakça

  • Calm, J.M.; Hourahan, G., Refrigerant data update, Hpac Engineering, 2007, 79(1): 50-64.
  • Calm, J.M., The next generation of refrigerants – Historical review, considerations, and outlook, International Journal of Refrigeration, 2008, 31(7): 1123-1133. doi:10.1016/j.ijrefrig. 2008.01.013
  • Mohanraj, M.; Jayaraj, S.; Muraleedharan, C., Environment friendly alternatives to halogenated refrigerants—A review, International Journal of Greenhouse Gas Control, 2009, 3(1): 108-119. doi:https://doi.org/10.1016/j.ijggc.2008.07.003
  • Sun, J.; Li, W.; Cui, B., Energy and exergy analyses of R513a as a R134a drop-in replacement in a vapor compression refrigeration system, International Journal of Refrigeration, 2020, 112: 348-356. doi:https://doi.org/10.1016/j.ijrefrig.2019.12.014
  • Devecioğlu, A.G.; Oruç, V., Characteristics of Some New Generation Refrigerants with Low GWP, Energy Procedia, 2015, 75: 1452-1457. doi:10.1016/j.egypro.2015.07.258
  • Dey, P.; Sarkar, A.; Das, A.K., Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Computing and Applications, 2015, 27(8): 2537-2549. doi:10.1007/s00521-015-2023-8
  • Mota-Babiloni, A.; Makhnatch, P.; Khodabandeh, R.; Navarro-Esbrí, J., Experimental assessment of R134a and its lower GWP alternative R513A, International Journal of Refrigeration, 2017, 74: 682-688. doi:10.1016/j.ijrefrig.2016.11.021
  • Yıldırım, R.; Yıldız, A., Evaluation of Performance of HFC-R134a/HFO-1234yf Binary Mixtures Used as Refrigerant in a Heat Pump System, El-Cezeri Journal of Science and Engineering (ECJSE), 2020, 7 (3): 1440-1449. doi: 10.31202/ecjse.734445
  • Su, W.; Zhou, S.; Zhao, L.; Zhou, N., Vapor–Liquid Equilibrium Prediction of Refrigerant Mixtures with Peng–Robinson Equation of State and Binary Interaction Parameters Calculated Through Group Contribution Model, International Journal of Thermophysics, 2020, 41(2): 1-24.
  • Di̇kmen, E.; Şencan Şahi̇n, A.; Deveci̇, Ö.; Akdağ, E., GWP Değeri Düşük Soğutucu Akışkanların Kullanıldığı Kaskad Soğutma Sisteminin Karşılaştırmalı Performans Analizi, El-Cezeri Journal of Science and Engineering (ECJSE), 2020, 7 (1): 338-345. doi: 10.31202/ ecjse.630262
  • Hamza, A.; Khan, T.A., Comparative Performance of Low-GWP Refrigerants as Substitutes for R134a in a Vapor Compression Refrigeration System, Arabian Journal for Science and Engineering, 2020, 45: 5697-5712. doi:10.1007/s13369-020-04525-3
  • Özen, D.; Yağcıoğlu, K., Thermodynamic and Exergy Analysis of an Absorption Cooling System for Different Refrigerants, El-Cezeri Journal of Science and Engineering (ECJSE), 2020, 7(1): 93-103. doi: 10.31202/ecjse.594641
  • Chemours Company: https://www.chemours.com/Refrigerants. 2017.
  • Harasami, F.; Akhgar, S.; Javan, M.; Shiri, J., Investigating the effect of previous time on modeling stage–discharge curve at hydrometric stations using GEP and NN models, ISH Journal of Hydraulic Engineering, 2017, 23(3): 293-300. doi:10.1080/09715010.2017.1308278
  • Kaboli, S.H.A.; Fallahpour, A.; Selvaraj, J.; Rahim, N.A., Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming, Energy, 2017, 126: 144-164. doi:10.1016/j.energy.2017.03.009
  • Sharifi, S.S.; Rezaverdinejad, V.; Nourani, V., Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches, Journal of Atmospheric and Solar-Terrestrial Physics, 2016, 149: 131-145. doi:10.1016/j.jastp.2016.10.008
  • Najafi-Marghmaleki, A.; Tatar, A.; Barati-Harooni, A.; Mohammadi, A.H., A GEP based model for prediction of densities of ionic liquids, Journal of Molecular Liquids, 2017, 227: 373-385. doi:10.1016/j.molliq.2016.11.072
  • Dikmen, E.; Ayaz, M.; Gül, D.; Şencan Şahin, A., Gene expression programming approach for the estimation of moisture ratio in herbal plants drying with vacuum heat pump dryer, Heat Mass Transfer, 2016, 53: 2419–2424. doi:10.1007/s00231-017-1998-3
  • Kaya, H., Paralel Bağlı Vorteks Tüplerinin Performansı için Yapay Sinir Ağları Analizi, El-Cezeri Journal of Science and Engineering (ECJSE), 2020, 7(3): 1509-1517. doi.org/10.31202/ecjse.774448
  • Sattar, A.M.A.; Gharabaghi, B., Gene expression models for prediction of longitudinal dispersion coefficient in streams, Journal of Hydrology, 2015, 524: 587-596. doi:10.1016/j.jhydrol.2015.03.016
  • Ferreira, C., Gene expression programming: A new adaptive algorithm for solving problems, Complex Systems, 2001, 13(2): 87-129.
  • Hoseinian, F.S.; Faradonbeh, R.S.; Abdollahzadeh, A.; Rezai, B.; Soltani-Mohammadi, S., Semi-autogenous mill power model development using gene expression programming, Powder Technology, 2017, 308: 61-69. doi:10.1016/j.powtec.2016.11.045
  • Mesbah, M.; Soroush, E.; Rostampour Kakroudi, M., Predicting physical properties (viscosity, density, and refractive index) of ternary systems containing 1-octyl-3-methyl-imidazolium bis (trifluoromethylsulfonyl) imide, esters and alcohols at 298.15 K and atmospheric pressure, using rigorous classification techniques, Journal of Molecular Liquids, 2017, 225: 778-787. doi:10.1016/j.molliq.2016.11.004

R513A Soğutucu Akışkanın Termodinamik Özelliklerini Tahmin Etmek İçin GEP Tabanlı Model Yaklaşımı

Yıl 2021, , 376 - 388, 31.01.2021
https://doi.org/10.31202/ecjse.814527

Öz

Soğutucu akışkan seçimlerinde politik ve çevresel faktörler oldukça etkilidir. Günümüzde sürdürülebilir bir gelecek için çevre dostu ve enerji tasarruflu soğutucu akışkanların kullanılması kritik önem taşımaktadır. Soğutma sistemlerinde R513A soğutucu akışkanının kullanımı, hem GWP azaltımı sağlar hem de enerji verimliliğini artırır. Soğutucu akışkanların termodinamik özelliklerinin doğru olarak belirlenmesi oldukça önemlidir. Soğutucu akışkanların termodinamik özelliklerine ilişkin birçok veri, literatür ve tablolardan güçlükle elde edilebilmektedir. Bu çalışmada, entropi, entalpi ve yoğunluk gibi termodinamik özellikler gen ifade programlaması (GEP) kullanılarak modellenmiştir. Bu bağlamda, yeni bir soğutucu akışkan olan R513A için GEP modeli yardımıyla matematiksel denklemler türetilmiştir. R513A soğutucu akışkan için farklı sıcaklık ve basınç şartlarında, literatürden elde edilen deneysel verilerle GEP modelinden elde edilen değerler karşılaştırılmıştır. Elde edilen sonuçlar, GEP modelinin soğutkanların termodinamik özelliklerini tahmin etmede verimli bir modelleme tekniği olarak kullanılabileceğini göstermiştir.

Kaynakça

  • Calm, J.M.; Hourahan, G., Refrigerant data update, Hpac Engineering, 2007, 79(1): 50-64.
  • Calm, J.M., The next generation of refrigerants – Historical review, considerations, and outlook, International Journal of Refrigeration, 2008, 31(7): 1123-1133. doi:10.1016/j.ijrefrig. 2008.01.013
  • Mohanraj, M.; Jayaraj, S.; Muraleedharan, C., Environment friendly alternatives to halogenated refrigerants—A review, International Journal of Greenhouse Gas Control, 2009, 3(1): 108-119. doi:https://doi.org/10.1016/j.ijggc.2008.07.003
  • Sun, J.; Li, W.; Cui, B., Energy and exergy analyses of R513a as a R134a drop-in replacement in a vapor compression refrigeration system, International Journal of Refrigeration, 2020, 112: 348-356. doi:https://doi.org/10.1016/j.ijrefrig.2019.12.014
  • Devecioğlu, A.G.; Oruç, V., Characteristics of Some New Generation Refrigerants with Low GWP, Energy Procedia, 2015, 75: 1452-1457. doi:10.1016/j.egypro.2015.07.258
  • Dey, P.; Sarkar, A.; Das, A.K., Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Computing and Applications, 2015, 27(8): 2537-2549. doi:10.1007/s00521-015-2023-8
  • Mota-Babiloni, A.; Makhnatch, P.; Khodabandeh, R.; Navarro-Esbrí, J., Experimental assessment of R134a and its lower GWP alternative R513A, International Journal of Refrigeration, 2017, 74: 682-688. doi:10.1016/j.ijrefrig.2016.11.021
  • Yıldırım, R.; Yıldız, A., Evaluation of Performance of HFC-R134a/HFO-1234yf Binary Mixtures Used as Refrigerant in a Heat Pump System, El-Cezeri Journal of Science and Engineering (ECJSE), 2020, 7 (3): 1440-1449. doi: 10.31202/ecjse.734445
  • Su, W.; Zhou, S.; Zhao, L.; Zhou, N., Vapor–Liquid Equilibrium Prediction of Refrigerant Mixtures with Peng–Robinson Equation of State and Binary Interaction Parameters Calculated Through Group Contribution Model, International Journal of Thermophysics, 2020, 41(2): 1-24.
  • Di̇kmen, E.; Şencan Şahi̇n, A.; Deveci̇, Ö.; Akdağ, E., GWP Değeri Düşük Soğutucu Akışkanların Kullanıldığı Kaskad Soğutma Sisteminin Karşılaştırmalı Performans Analizi, El-Cezeri Journal of Science and Engineering (ECJSE), 2020, 7 (1): 338-345. doi: 10.31202/ ecjse.630262
  • Hamza, A.; Khan, T.A., Comparative Performance of Low-GWP Refrigerants as Substitutes for R134a in a Vapor Compression Refrigeration System, Arabian Journal for Science and Engineering, 2020, 45: 5697-5712. doi:10.1007/s13369-020-04525-3
  • Özen, D.; Yağcıoğlu, K., Thermodynamic and Exergy Analysis of an Absorption Cooling System for Different Refrigerants, El-Cezeri Journal of Science and Engineering (ECJSE), 2020, 7(1): 93-103. doi: 10.31202/ecjse.594641
  • Chemours Company: https://www.chemours.com/Refrigerants. 2017.
  • Harasami, F.; Akhgar, S.; Javan, M.; Shiri, J., Investigating the effect of previous time on modeling stage–discharge curve at hydrometric stations using GEP and NN models, ISH Journal of Hydraulic Engineering, 2017, 23(3): 293-300. doi:10.1080/09715010.2017.1308278
  • Kaboli, S.H.A.; Fallahpour, A.; Selvaraj, J.; Rahim, N.A., Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming, Energy, 2017, 126: 144-164. doi:10.1016/j.energy.2017.03.009
  • Sharifi, S.S.; Rezaverdinejad, V.; Nourani, V., Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches, Journal of Atmospheric and Solar-Terrestrial Physics, 2016, 149: 131-145. doi:10.1016/j.jastp.2016.10.008
  • Najafi-Marghmaleki, A.; Tatar, A.; Barati-Harooni, A.; Mohammadi, A.H., A GEP based model for prediction of densities of ionic liquids, Journal of Molecular Liquids, 2017, 227: 373-385. doi:10.1016/j.molliq.2016.11.072
  • Dikmen, E.; Ayaz, M.; Gül, D.; Şencan Şahin, A., Gene expression programming approach for the estimation of moisture ratio in herbal plants drying with vacuum heat pump dryer, Heat Mass Transfer, 2016, 53: 2419–2424. doi:10.1007/s00231-017-1998-3
  • Kaya, H., Paralel Bağlı Vorteks Tüplerinin Performansı için Yapay Sinir Ağları Analizi, El-Cezeri Journal of Science and Engineering (ECJSE), 2020, 7(3): 1509-1517. doi.org/10.31202/ecjse.774448
  • Sattar, A.M.A.; Gharabaghi, B., Gene expression models for prediction of longitudinal dispersion coefficient in streams, Journal of Hydrology, 2015, 524: 587-596. doi:10.1016/j.jhydrol.2015.03.016
  • Ferreira, C., Gene expression programming: A new adaptive algorithm for solving problems, Complex Systems, 2001, 13(2): 87-129.
  • Hoseinian, F.S.; Faradonbeh, R.S.; Abdollahzadeh, A.; Rezai, B.; Soltani-Mohammadi, S., Semi-autogenous mill power model development using gene expression programming, Powder Technology, 2017, 308: 61-69. doi:10.1016/j.powtec.2016.11.045
  • Mesbah, M.; Soroush, E.; Rostampour Kakroudi, M., Predicting physical properties (viscosity, density, and refractive index) of ternary systems containing 1-octyl-3-methyl-imidazolium bis (trifluoromethylsulfonyl) imide, esters and alcohols at 298.15 K and atmospheric pressure, using rigorous classification techniques, Journal of Molecular Liquids, 2017, 225: 778-787. doi:10.1016/j.molliq.2016.11.004
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Arzu Şencan Şahin 0000-0001-8519-4788

Tuğba Kovacı 0000-0002-0974-1660

Erkan Dikmen 0000-0002-6804-8612

Yayımlanma Tarihi 31 Ocak 2021
Gönderilme Tarihi 22 Ekim 2020
Kabul Tarihi 5 Aralık 2020
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

IEEE A. Şencan Şahin, T. Kovacı, ve E. Dikmen, “A GEP-Based Model Approach for Estimating Thermodynamic Properties of R513A Refrigerant”, ECJSE, c. 8, sy. 1, ss. 376–388, 2021, doi: 10.31202/ecjse.814527.