Research Article
Thermodynamic Performance Evaluation of a Two-Stage Cascade Refrigeration System Using Parametric Analysis and Support Vector Machine Algorithm
Abstract
In this study, a thermodynamic design of a two-stage cascade refrigeration system capable of reaching ultra-low temperatures was developed, and energy and exergy analyses were conducted. The performance of four refrigerant pairs—R744/R152a, R744/R32, R41/R152a, and R41/R32—was evaluated. Parametric analysis results, carried out across evaporator temperatures ranging from -70 °C to -50 °C and condenser temperatures between 25 °C and 45 °C, showed that R41/R152a was the most efficient pair. A maximum COP of 1.421 was achieved at -50 °C evaporator temperature, and the highest exergy efficiency of 0.4407 was recorded at -60 °C. In the second phase, an SVM approach was applied to predict COP and exergy efficiency, yielding very low MAE values of 0.0040 and 0.0009 in the test set, respectively. The outcomes of the energy-exergy analysis and SVM modeling are expected to provide valuable guidance for designing low-temperature cascade refrigeration systems.
Ethical Statement
Ethics committee approval was not required for the preparation of this article. The author declares no conflict of interest regarding this study.
References
- A. Mota-Babiloni et al., “Ultralow-temperature refrigeration systems: Configurations and refrigerants to reduce the environmental impact,” Int. J. Refrig., vol. 111, pp. 147–158, 2020.
- C. M. Udroiu, A. Mota-Babiloni, and J. Navarro-Esbri, “Advanced two-stage cascade configurations for energy-efficient -80°C refrigeration,” Energy Convers. Manag., vol. 267, 115907, Sep. 1, 2022.
- W. Ye, F. Liu, Y. Yan, and Y. Liu, “Application of response surface methodology and desirability approach to optimize the performance of an ultra-low temperature cascade refrigeration system,” Appl. Therm. Eng., vol. 239, 2024.
- S. Khalilzadeh, A. H. Nezhad, and F. Sarhaddi, “Reducing the power consumption of cascade refrigeration cycle by a new integrated system using solar energy,” Energy Convers. Manag., vol. 200, 112083, 2019.
- O. Pektezel, M. Das, and H. I. Acar, “Experimental analysis of different refrigerants’ thermal behavior and predicting their performance parameters,” J. Thermophys. Heat Transf., vol. 37, no. 2, pp. 309–319, 2023.
- A. Ustaoglu et al., “Performance optimization and parametric evaluation of the cascade vapor compression refrigeration cycle using Taguchi and ANOVA methods,” Appl. Therm. Eng., vol. 180, 2020.
- S. Asgari, A. Noorpoor, and F. A. Boyaghchi, “Parametric assessment and multi-objective optimization of an internal auto-cascade refrigeration cycle based on advanced exergy and exergoeconomic concepts,” Energy, vol. 125, pp. 576–590, 2017.
- R. Llopis et al., “Energy and environmental comparison of two-stage solutions for commercial refrigeration at low temperature: Fluids and systems,” Appl. Energy, vol. 138, pp. 133–142, 2015.
- A. Mota-Babiloni et al., “Optimisation of high-temperature heat pump cascades with internal heat exchangers using refrigerants with low global warming potential,” Energy, vol. 165, pp. 1248–1258, 2018.
- W. L. Luyben, “Estimating refrigeration costs at cryogenic temperatures,” Comput. Chem. Eng., vol. 103, pp. 144–150, 2017.
- M. W. Faruque et al., “A Comprehensive Thermodynamic Assessment of Cascade Refrigeration System Utilizing Low GWP Hydrocarbon Refrigerants,” Int. J. Thermofluids, vol. 15, 2022.
- Z. Sun et al., “Comparative analysis of thermodynamic performance of a cascade refrigeration system for refrigerant couples R41/R404A and R23/R404A,” Appl. Energy, vol. 184, pp. 19–25, 2016.
- W. Ye et al., “Parametric analysis and performance prediction of an ultra-low temperature cascade refrigeration freezer based on an artificial neural network,” Case Stud. Therm. Eng., vol. 55, 2024.
- M. Chen et al., “Performance comparison of ultra-low temperature cascade refrigeration cycles using R717/R170, R717/R41 and R717/R1150 to replace R404A/R23,” Therm. Sci. Eng. Prog., vol. 44, 2023.
- S. Ji et al., “Energy, exergy, environmental and exergoeconomic (4E) analysis of an ultra-low temperature cascade refrigeration system with environmental-friendly refrigerants,” Appl. Therm. Eng., vol. 248, 2024.
- A. K. Vuppaladadiyam et al., “Progress in the development and use of refrigerants and unintended environmental consequences,” Sci. Total Environ., vol. 823, 153670, 2022.
- K. Uddin and B. B. Saha, “An overview of environment-friendly refrigerants for domestic air conditioning applications,” Energies, vol. 15, no. 21, 8082, 2022.
- N. Abas et al., “Natural and synthetic refrigerants, global warming: A review,” Renew. Sustain. Energy Rev., vol. 90, pp. 557–569, 2018.
- A. Atmaca et al., “Thermodynamic performance of the transcritical refrigeration cycle with ejector expansion for R744, R170, and R41,” Isı Bilimi ve Tekniği Dergisi, vol. 38, no. 2, pp. 111–127, 2018.
- A. Usman et al., “Viability of the Proposed Alternative Refrigerants as Future Refrigerants,” Macromol. Symp., vol. 414, no. 1, 2025.
- Y. Heredia-Aricapa et al., “Overview of low GWP mixtures for the replacement of HFC refrigerants: R134a, R404A and R410A,” Int. J. Refrig., vol. 111, pp. 113–123, 2020.
- A. Mota-Babiloni et al., “Refrigerant R32 as lower GWP working fluid in residential air conditioning systems in Europe and the USA,” Renew. Sustain. Energy Rev., vol. 80, pp. 1031–1042, 2017.
- V. H. Panato et al., “Experimental evaluation of R32, R452B and R454B as alternative refrigerants for R410A in a refrigeration system,” Int. J. Refrig., vol. 135, pp. 221–230, 2022.
- A. Maiorino et al., “R-152a as an alternative refrigerant to R-134a in domestic refrigerators: An experimental analysis,” Int. J. Refrig., vol. 96, pp. 106–116, 2018.
- B. Bolaji, “Experimental study of R152a and R32 to replace R134a in a domestic refrigerator,” Energy, vol. 35, no. 9, pp. 3793–3798, 2010.
- S. A. Klein, “Engineering Equation Solver (EES),” Academic Professional Version, F-Chart Software: Middleton, 2013.
- R. Roy and B. K. Mandal, “Energetic and exergetic performance comparison of cascade refrigeration system using R170-R161 and R41-R404A as refrigerant pairs,” Heat Mass Transf., vol. 55, no. 3, pp. 723–731, 2018.
- Z. Sun et al., “Energy and exergy analysis of low GWP refrigerants in cascade refrigeration system,” Energy, vol. 170, pp. 1170–1180, 2019.
- O. E. Akay and M. Das, “Modeling the total heat transfer coefficient of a nuclear research reactor cooling system by different methods,” Case Stud. Therm. Eng., vol. 25, 100914, 2021.
- M. Türk, C. Haydaroglu, and H. Kılıç, “Machine Learning-Based Detection of Non-Technical Losses in Power Distribution Networks,” Fırat Univ. J. Exp. Comput. Eng., vol. 4, no. 1, pp. 192–205, 2025.
- A. B. Tatar, “Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods,” Fırat Univ. J. Exp. Comput. Eng., vol. 4, no. 1, pp. 206–225, 2025.
- O. Pektezel and H. I. Acar, “Experimental comparison of R290 and R600a and prediction of performance with machine learning algorithms,” Sci. Technol. Built Environ., vol. 29, no. 5, pp. 508–522, 2023.
- S. Salcedo‐Sanz et al., “Support vector machines in engineering: an overview,” Data Min. Knowl. Discov., vol. 4, no. 3, pp. 234–267, 2014.
- M. E. Cholette et al., “Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications,” Expert Syst. Appl., vol. 81, pp. 39–52, 2017.
- M. Bansal, A. Goyal, and A. Choudhary, “A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning,” Decis. Anal. J., vol. 3, 100071, 2022.
- N. Akgün, “Testing The Performance of Random Forest and Support Vector Machine Algorithms for Predicting Cyclist Casualty Severity,” Fırat Univ. J. Exp. Comput. Eng., vol. 2, no. 3, pp. 124–133, 2023.
- M. Das, E. Alic, and E. K. Akpinar, “Detailed analysis of mass transfer in solar food dryer with different methods,” Int. Commun. Heat Mass Transf., vol. 128, 105600, 2021.
- Y. E. Güzelel et al., “New multiple regression and machine learning models of rotary desiccant wheel for unbalanced flow conditions,” Int. Commun. Heat Mass Transf., vol. 134, 106006, 2022.
- O. Pektezel, M. Das, and H. I. Acar, “Experimental exergy analysis of low-GWP R290 refrigerant and derivation of exergetic performance equations with regression algorithms,” Int. J. Exergy, vol. 40, no. 4, pp. 467–482, 2023.
Parametrik Analiz ve Destek Vektör Makinesi Algoritması Kullanılarak İki Kademeli Kaskad Soğutma Sisteminin Termodinamik Performans Değerlendirmesi
Abstract
Bu çalışmada, ultra düşük sıcaklıklara ulaşabilen iki kademeli kaskad bir soğutma sisteminin termodinamik tasarımı yapılmış ve enerji ile ekserji analizleri gerçekleştirilmiştir. Dört soğutucu akışkan çiftinin—R744/R152a, R744/R32, R41/R152a ve R41/R32—performansı değerlendirilmiştir. -70 °C ile -50 °C arasındaki evaporatör sıcaklıkları ve 25 °C ile 45 °C arasındaki kondenser sıcaklıkları boyunca yürütülen parametrik analiz sonuçları, R41/R152a'nın en verimli çift olduğunu göstermiştir. -50 °C evaporatör sıcaklığında maksimum 1.421 COP değeri elde edilmiş ve en yüksek 0.4407 ekserji verimi -60 °C’de kaydedilmiştir. İkinci aşamada, COP ve ekserji verimini tahmin etmek için SVM yöntemi uygulanmış ve sırasıyla test setinde çok düşük MAE değerleri olan 0.0040 ve 0.0009 elde edilmiştir. Enerji-ekserji analizi ve SVM modellemesinin sonuçlarının, düşük sıcaklıklı kaskad soğutma sistemlerinin tasarımı için değerli bir rehberlik sağlaması beklenmektedir.
Ethical Statement
Bu makalenin hazırlanması için etik kurul onayı gerekmemektedir. Yazar bu çalışma ile ilgili herhangi bir çıkar çatışması bildirmemektedir.
References
- A. Mota-Babiloni et al., “Ultralow-temperature refrigeration systems: Configurations and refrigerants to reduce the environmental impact,” Int. J. Refrig., vol. 111, pp. 147–158, 2020.
- C. M. Udroiu, A. Mota-Babiloni, and J. Navarro-Esbri, “Advanced two-stage cascade configurations for energy-efficient -80°C refrigeration,” Energy Convers. Manag., vol. 267, 115907, Sep. 1, 2022.
- W. Ye, F. Liu, Y. Yan, and Y. Liu, “Application of response surface methodology and desirability approach to optimize the performance of an ultra-low temperature cascade refrigeration system,” Appl. Therm. Eng., vol. 239, 2024.
- S. Khalilzadeh, A. H. Nezhad, and F. Sarhaddi, “Reducing the power consumption of cascade refrigeration cycle by a new integrated system using solar energy,” Energy Convers. Manag., vol. 200, 112083, 2019.
- O. Pektezel, M. Das, and H. I. Acar, “Experimental analysis of different refrigerants’ thermal behavior and predicting their performance parameters,” J. Thermophys. Heat Transf., vol. 37, no. 2, pp. 309–319, 2023.
- A. Ustaoglu et al., “Performance optimization and parametric evaluation of the cascade vapor compression refrigeration cycle using Taguchi and ANOVA methods,” Appl. Therm. Eng., vol. 180, 2020.
- S. Asgari, A. Noorpoor, and F. A. Boyaghchi, “Parametric assessment and multi-objective optimization of an internal auto-cascade refrigeration cycle based on advanced exergy and exergoeconomic concepts,” Energy, vol. 125, pp. 576–590, 2017.
- R. Llopis et al., “Energy and environmental comparison of two-stage solutions for commercial refrigeration at low temperature: Fluids and systems,” Appl. Energy, vol. 138, pp. 133–142, 2015.
- A. Mota-Babiloni et al., “Optimisation of high-temperature heat pump cascades with internal heat exchangers using refrigerants with low global warming potential,” Energy, vol. 165, pp. 1248–1258, 2018.
- W. L. Luyben, “Estimating refrigeration costs at cryogenic temperatures,” Comput. Chem. Eng., vol. 103, pp. 144–150, 2017.
- M. W. Faruque et al., “A Comprehensive Thermodynamic Assessment of Cascade Refrigeration System Utilizing Low GWP Hydrocarbon Refrigerants,” Int. J. Thermofluids, vol. 15, 2022.
- Z. Sun et al., “Comparative analysis of thermodynamic performance of a cascade refrigeration system for refrigerant couples R41/R404A and R23/R404A,” Appl. Energy, vol. 184, pp. 19–25, 2016.
- W. Ye et al., “Parametric analysis and performance prediction of an ultra-low temperature cascade refrigeration freezer based on an artificial neural network,” Case Stud. Therm. Eng., vol. 55, 2024.
- M. Chen et al., “Performance comparison of ultra-low temperature cascade refrigeration cycles using R717/R170, R717/R41 and R717/R1150 to replace R404A/R23,” Therm. Sci. Eng. Prog., vol. 44, 2023.
- S. Ji et al., “Energy, exergy, environmental and exergoeconomic (4E) analysis of an ultra-low temperature cascade refrigeration system with environmental-friendly refrigerants,” Appl. Therm. Eng., vol. 248, 2024.
- A. K. Vuppaladadiyam et al., “Progress in the development and use of refrigerants and unintended environmental consequences,” Sci. Total Environ., vol. 823, 153670, 2022.
- K. Uddin and B. B. Saha, “An overview of environment-friendly refrigerants for domestic air conditioning applications,” Energies, vol. 15, no. 21, 8082, 2022.
- N. Abas et al., “Natural and synthetic refrigerants, global warming: A review,” Renew. Sustain. Energy Rev., vol. 90, pp. 557–569, 2018.
- A. Atmaca et al., “Thermodynamic performance of the transcritical refrigeration cycle with ejector expansion for R744, R170, and R41,” Isı Bilimi ve Tekniği Dergisi, vol. 38, no. 2, pp. 111–127, 2018.
- A. Usman et al., “Viability of the Proposed Alternative Refrigerants as Future Refrigerants,” Macromol. Symp., vol. 414, no. 1, 2025.
- Y. Heredia-Aricapa et al., “Overview of low GWP mixtures for the replacement of HFC refrigerants: R134a, R404A and R410A,” Int. J. Refrig., vol. 111, pp. 113–123, 2020.
- A. Mota-Babiloni et al., “Refrigerant R32 as lower GWP working fluid in residential air conditioning systems in Europe and the USA,” Renew. Sustain. Energy Rev., vol. 80, pp. 1031–1042, 2017.
- V. H. Panato et al., “Experimental evaluation of R32, R452B and R454B as alternative refrigerants for R410A in a refrigeration system,” Int. J. Refrig., vol. 135, pp. 221–230, 2022.
- A. Maiorino et al., “R-152a as an alternative refrigerant to R-134a in domestic refrigerators: An experimental analysis,” Int. J. Refrig., vol. 96, pp. 106–116, 2018.
- B. Bolaji, “Experimental study of R152a and R32 to replace R134a in a domestic refrigerator,” Energy, vol. 35, no. 9, pp. 3793–3798, 2010.
- S. A. Klein, “Engineering Equation Solver (EES),” Academic Professional Version, F-Chart Software: Middleton, 2013.
- R. Roy and B. K. Mandal, “Energetic and exergetic performance comparison of cascade refrigeration system using R170-R161 and R41-R404A as refrigerant pairs,” Heat Mass Transf., vol. 55, no. 3, pp. 723–731, 2018.
- Z. Sun et al., “Energy and exergy analysis of low GWP refrigerants in cascade refrigeration system,” Energy, vol. 170, pp. 1170–1180, 2019.
- O. E. Akay and M. Das, “Modeling the total heat transfer coefficient of a nuclear research reactor cooling system by different methods,” Case Stud. Therm. Eng., vol. 25, 100914, 2021.
- M. Türk, C. Haydaroglu, and H. Kılıç, “Machine Learning-Based Detection of Non-Technical Losses in Power Distribution Networks,” Fırat Univ. J. Exp. Comput. Eng., vol. 4, no. 1, pp. 192–205, 2025.
- A. B. Tatar, “Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods,” Fırat Univ. J. Exp. Comput. Eng., vol. 4, no. 1, pp. 206–225, 2025.
- O. Pektezel and H. I. Acar, “Experimental comparison of R290 and R600a and prediction of performance with machine learning algorithms,” Sci. Technol. Built Environ., vol. 29, no. 5, pp. 508–522, 2023.
- S. Salcedo‐Sanz et al., “Support vector machines in engineering: an overview,” Data Min. Knowl. Discov., vol. 4, no. 3, pp. 234–267, 2014.
- M. E. Cholette et al., “Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications,” Expert Syst. Appl., vol. 81, pp. 39–52, 2017.
- M. Bansal, A. Goyal, and A. Choudhary, “A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning,” Decis. Anal. J., vol. 3, 100071, 2022.
- N. Akgün, “Testing The Performance of Random Forest and Support Vector Machine Algorithms for Predicting Cyclist Casualty Severity,” Fırat Univ. J. Exp. Comput. Eng., vol. 2, no. 3, pp. 124–133, 2023.
- M. Das, E. Alic, and E. K. Akpinar, “Detailed analysis of mass transfer in solar food dryer with different methods,” Int. Commun. Heat Mass Transf., vol. 128, 105600, 2021.
- Y. E. Güzelel et al., “New multiple regression and machine learning models of rotary desiccant wheel for unbalanced flow conditions,” Int. Commun. Heat Mass Transf., vol. 134, 106006, 2022.
- O. Pektezel, M. Das, and H. I. Acar, “Experimental exergy analysis of low-GWP R290 refrigerant and derivation of exergetic performance equations with regression algorithms,” Int. J. Exergy, vol. 40, no. 4, pp. 467–482, 2023.
There are 39 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Mechanical Engineering (Other) |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | June 26, 2025 |
| Submission Date | April 24, 2025 |
| Acceptance Date | June 7, 2025 |
| Published in Issue | Year 2025 Volume: 4 Issue: 2 |
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