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ORC technology is one of the most promising technologies for the use of residual energy in the generation of electrical energy, offering simple and environmentally friendly alternatives. In this field, the selection of working fluids plays an important role in the operation of the cycle, whether in terms of the energy efficiency or the minimization of environmental impacts. Therefore, in this paper, a comprehensive review is presented on the use of R1234yf refrigerant and its mixtures as working fluids in ORC systems. These fluids are used in low- and medium-temperature applications for the use of residual energy generated from solar energy, geothermal energy, and internal combustion engines. It was concluded that R1234yf and its mixtures are competitive as compared with conventional refrigerants used in ORC.
Juan García-Pabón; Dario Méndez-Méndez; Juan Belman-Flores; Juan Barroso-Maldonado; Ali Khosravi. A Review of Recent Research on the Use of R1234yf as an Environmentally Friendly Fluid in the Organic Rankine Cycle. Sustainability 2021, 13, 5864 .
AMA StyleJuan García-Pabón, Dario Méndez-Méndez, Juan Belman-Flores, Juan Barroso-Maldonado, Ali Khosravi. A Review of Recent Research on the Use of R1234yf as an Environmentally Friendly Fluid in the Organic Rankine Cycle. Sustainability. 2021; 13 (11):5864.
Chicago/Turabian StyleJuan García-Pabón; Dario Méndez-Méndez; Juan Belman-Flores; Juan Barroso-Maldonado; Ali Khosravi. 2021. "A Review of Recent Research on the Use of R1234yf as an Environmentally Friendly Fluid in the Organic Rankine Cycle." Sustainability 13, no. 11: 5864.
A crucial aspect of Joule-Thomson cryocooler analysis and optimization is the accurate estimation of frictional pressure drop. This paper presents a pressure drop model for boiling of non-azeotropic mixtures of nitrogen with hydrocarbons (e.g., methane, ethane, and propane) in microchannels. These refrigerant mixtures are important for their applicability in natural gas liquefaction plants. The pressure drop model is based on computational intelligence techniques, and its performance is evaluated with the mean relative error (mre), and compared with three correlations previously selected as most accurate: Awad and Muzychka; Sun and Mishima; and Cicchitti et al. Comparison between the proposed artificial neural network (ANN) model and the three correlations shows the advantages of the ANN to predict pressure drop for non-azeotropic mixtures. Existing correlations predict experimental data within mre=23.9-25.3%, while the ANN has mre = 8.3%. Additional features of the ANN model include: (1) applicability to laminar, transitional and turbulent flow, and (2) demonstrated applicability to experiments not used in the training process. Therefore, the ANN model is recommended for predicting pressure drop due to accuracy and ease of applicability.
Juan Manuel Barroso-Maldonado; Jhon Montanez Barrera; Juan Belman; S.M. Aceves. ANN-based correlation for frictional pressure drop of non-azeotropic mixtures during cryogenic forced boiling. Applied Thermal Engineering 2018, 149, 492 -501.
AMA StyleJuan Manuel Barroso-Maldonado, Jhon Montanez Barrera, Juan Belman, S.M. Aceves. ANN-based correlation for frictional pressure drop of non-azeotropic mixtures during cryogenic forced boiling. Applied Thermal Engineering. 2018; 149 ():492-501.
Chicago/Turabian StyleJuan Manuel Barroso-Maldonado; Jhon Montanez Barrera; Juan Belman; S.M. Aceves. 2018. "ANN-based correlation for frictional pressure drop of non-azeotropic mixtures during cryogenic forced boiling." Applied Thermal Engineering 149, no. : 492-501.
The replacement of HFCs using lower GWP refrigerants in the coming years is a priority to reduce the predicted climate change. The exergy analysis of vapor compression systems can help to identify the feasibility of alternative fluids in existing installations and the potential to improve them. In this sense, this paper presents an exergy analysis of an experimental setup which operates with R134a and the alternative HFO/HFC mixture R513A. The evaporating temperature is ranges between −15 °C and 5 °C, while the condensing temperature is set at 30 °C and 35 °C. In this analysis, the highest amount of exergy destruction rate is obtained at the compressor, followed by the evaporator. The maximum exergy efficiencies are observed at the condenser and the thermostatic expansion device. Finally, the average global exergy efficiency of R513A when replaced R134a in this refrigeration experimental setup is 0.4% higher (absolute difference), and with respect to the components, there is only slight reduction in efficiency in the condenser using R513A. Therefore, the R513A replacement is acceptable according to the second law of thermodynamics.
Adrián Mota-Babiloni; J.M. Belman-Flores; Pavel Makhnatch; Joaquín Navarro-Esbrí; J.M. Barroso-Maldonado. Experimental exergy analysis of R513A to replace R134a in a small capacity refrigeration system. Energy 2018, 162, 99 -110.
AMA StyleAdrián Mota-Babiloni, J.M. Belman-Flores, Pavel Makhnatch, Joaquín Navarro-Esbrí, J.M. Barroso-Maldonado. Experimental exergy analysis of R513A to replace R134a in a small capacity refrigeration system. Energy. 2018; 162 ():99-110.
Chicago/Turabian StyleAdrián Mota-Babiloni; J.M. Belman-Flores; Pavel Makhnatch; Joaquín Navarro-Esbrí; J.M. Barroso-Maldonado. 2018. "Experimental exergy analysis of R513A to replace R134a in a small capacity refrigeration system." Energy 162, no. : 99-110.
A key problem faced in the design of heat exchangers, especially for cryogenic applications, is the determination of convective heat transfer coefficients in two-phase flow such as condensation and boiling of non-azeotropic refrigerant mixtures. This paper proposes and evaluates three models for estimating the convective coefficient during boiling. These models are developed using computational intelligence techniques. The performance of the proposed models is evaluated using the mean relative error (mre), and compared to two existing models: the modified Granryd’s correlation and the Silver-Bell-Ghaly method. The three proposed models are distinguished by their architecture. The first is based on directly measured parameters (DMP-ANN), the second is based on equivalent Reynolds and Prandtl numbers (eq-ANN), and the third on effective Reynolds and Prandtl numbers (eff-ANN). The results demonstrate that the proposed artificial neural network (ANN)-based approaches greatly outperform available methodologies. While Granryd's correlation predicts experimental data within a mean relative error mre = 44% and the S-B-G method produces mre = 42%, DMP-ANN has mre = 7.4% and eff-ANN has mre = 3.9%. Considering that eff-ANN has the lowest mean relative error (one tenth of previously available methodologies) and the broadest range of applicability, it is recommended for future calculations. Implementation is straightforward within a variety of platforms and the matrices with the ANN weights are given in the appendix for efficient programming.
J.M. Barroso-Maldonado; Juan Belman; Sergio Ledesma; S.M. Aceves. Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks. Cryogenics 2018, 92, 60 -70.
AMA StyleJ.M. Barroso-Maldonado, Juan Belman, Sergio Ledesma, S.M. Aceves. Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks. Cryogenics. 2018; 92 ():60-70.
Chicago/Turabian StyleJ.M. Barroso-Maldonado; Juan Belman; Sergio Ledesma; S.M. Aceves. 2018. "Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks." Cryogenics 92, no. : 60-70.
J.M. Belman-Flores; J.M. Barroso-Maldonado; Sergio Ledesma; V. Pérez-García; A. Gallegos-Muñoz; Jorge Arturo Alfaro-Ayala. Exergy assessment of a refrigeration plant using computational intelligence based on hybrid learning methods. International Journal of Refrigeration 2018, 88, 35 -44.
AMA StyleJ.M. Belman-Flores, J.M. Barroso-Maldonado, Sergio Ledesma, V. Pérez-García, A. Gallegos-Muñoz, Jorge Arturo Alfaro-Ayala. Exergy assessment of a refrigeration plant using computational intelligence based on hybrid learning methods. International Journal of Refrigeration. 2018; 88 ():35-44.
Chicago/Turabian StyleJ.M. Belman-Flores; J.M. Barroso-Maldonado; Sergio Ledesma; V. Pérez-García; A. Gallegos-Muñoz; Jorge Arturo Alfaro-Ayala. 2018. "Exergy assessment of a refrigeration plant using computational intelligence based on hybrid learning methods." International Journal of Refrigeration 88, no. : 35-44.
This paper describes an educational simulator developed in the software Engineering Equation Solver to simulate the behavior of a vapor compression system. The application is focused on educational purposes, specifically for handling skills in refrigeration facilities by students in engineering careers. Using this simulator, the students are able to analyze easily the influence of the measured parameters (such as the compressor rotation speed, volumetric flow rates and temperature of the secondary fluids) on the energy performance of the facility and its components. The virtual test bench consists of a primary screen showing a general scheme of the vapor compression facility with input and output parameters. From this primary screen, the performance of the main components can be analyzed. Additionally, this virtual test bench was tested by students, concluding that the simulator is an interesting tool as improvement and support for learning in different subjects.
Juan Manuel Belman Flores; Universidad De Guanajuato; Juan Manuel Barroso-Maldonado; Santos Mendez Díaz; Simón Martínez Martínez; Universidad Autónoma De Nuevo León. Virtual test bench as a complement to study thermal area application in vapor compression systems. Revista Facultad de Ingeniería Universidad de Antioquia 2015, 1 .
AMA StyleJuan Manuel Belman Flores, Universidad De Guanajuato, Juan Manuel Barroso-Maldonado, Santos Mendez Díaz, Simón Martínez Martínez, Universidad Autónoma De Nuevo León. Virtual test bench as a complement to study thermal area application in vapor compression systems. Revista Facultad de Ingeniería Universidad de Antioquia. 2015; (77):1.
Chicago/Turabian StyleJuan Manuel Belman Flores; Universidad De Guanajuato; Juan Manuel Barroso-Maldonado; Santos Mendez Díaz; Simón Martínez Martínez; Universidad Autónoma De Nuevo León. 2015. "Virtual test bench as a complement to study thermal area application in vapor compression systems." Revista Facultad de Ingeniería Universidad de Antioquia , no. 77: 1.
Transitioning from R134a refrigerant to a low global warming potential (GWP) refrigerant is a current issue of global importance. Although any refrigerant still has set; there are a few options to replace it such as the R1234yf. In this paper is presented a semi-empirical model to assess the energy performance of mixtures with R134a and its possible substitute R1234yf. The inputs variables to the computational model are: suction conditions (pressure and temperature), discharge pressure and rotation speed. With these variables the model must compute the following parameters: mass flow rate, discharge temperature and energy consumption. The model is validated with data obtained from an experimental facility; calculations are obtained within a relative error band of ±10% for mass flow rate and energy consumption, and an error of ±1 K for discharge temperature. Finally, the model is carried out to an energy simulation in order to predict the behavior of different mass fractions of R1234yf. Energy savings are found when R1234yf mass fraction is reduced from 1 to 0.9. Knowing that the mixture with y=0.9 may be used as its GWP is 150.
Juan Manuel Barroso-Maldonado; J. M. Belman-Flores; Carlos Rubio-Maya. A Brief Model to Evaluate the Behaviour of R134a/R1234yf Mixtures on a Reciprocating Compressor. Volume 3: Biomedical and Biotechnology Engineering 2015, 1 .
AMA StyleJuan Manuel Barroso-Maldonado, J. M. Belman-Flores, Carlos Rubio-Maya. A Brief Model to Evaluate the Behaviour of R134a/R1234yf Mixtures on a Reciprocating Compressor. Volume 3: Biomedical and Biotechnology Engineering. 2015; ():1.
Chicago/Turabian StyleJuan Manuel Barroso-Maldonado; J. M. Belman-Flores; Carlos Rubio-Maya. 2015. "A Brief Model to Evaluate the Behaviour of R134a/R1234yf Mixtures on a Reciprocating Compressor." Volume 3: Biomedical and Biotechnology Engineering , no. : 1.
Sergio Ledesma; J.M. Belman-Flores; J.M. Barroso-Maldonado. Analysis and modeling of a variable speed reciprocating compressor using ANN. International Journal of Refrigeration 2015, 59, 190 -197.
AMA StyleSergio Ledesma, J.M. Belman-Flores, J.M. Barroso-Maldonado. Analysis and modeling of a variable speed reciprocating compressor using ANN. International Journal of Refrigeration. 2015; 59 ():190-197.
Chicago/Turabian StyleSergio Ledesma; J.M. Belman-Flores; J.M. Barroso-Maldonado. 2015. "Analysis and modeling of a variable speed reciprocating compressor using ANN." International Journal of Refrigeration 59, no. : 190-197.
Juan Belman; Juan Manuel Barroso-Maldonado; Andrea Del Pilar Rodriguez Muñoz; G. Camacho-Vázquez. Enhancements in domestic refrigeration, approaching a sustainable refrigerator – A review. Renewable and Sustainable Energy Reviews 2015, 51, 955 -968.
AMA StyleJuan Belman, Juan Manuel Barroso-Maldonado, Andrea Del Pilar Rodriguez Muñoz, G. Camacho-Vázquez. Enhancements in domestic refrigeration, approaching a sustainable refrigerator – A review. Renewable and Sustainable Energy Reviews. 2015; 51 ():955-968.
Chicago/Turabian StyleJuan Belman; Juan Manuel Barroso-Maldonado; Andrea Del Pilar Rodriguez Muñoz; G. Camacho-Vázquez. 2015. "Enhancements in domestic refrigeration, approaching a sustainable refrigerator – A review." Renewable and Sustainable Energy Reviews 51, no. : 955-968.
J.M. Belman-Flores; S. Ledesma; J.M. Barroso-Maldonado; J. Navarro-Esbrí. A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model. International Journal of Refrigeration 2015, 59, 144 -156.
AMA StyleJ.M. Belman-Flores, S. Ledesma, J.M. Barroso-Maldonado, J. Navarro-Esbrí. A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model. International Journal of Refrigeration. 2015; 59 ():144-156.
Chicago/Turabian StyleJ.M. Belman-Flores; S. Ledesma; J.M. Barroso-Maldonado; J. Navarro-Esbrí. 2015. "A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model." International Journal of Refrigeration 59, no. : 144-156.
J.M. Belman-Flores; J.M. Barroso-Maldonado; S. Ledesma. Modeling of the Compression Process for Refrigerants R134a and R1234yf of a Variable Speed Reciprocating Compressor. Journal of Advanced Thermal Science Research 2015, 2, 33 -43.
AMA StyleJ.M. Belman-Flores, J.M. Barroso-Maldonado, S. Ledesma. Modeling of the Compression Process for Refrigerants R134a and R1234yf of a Variable Speed Reciprocating Compressor. Journal of Advanced Thermal Science Research. 2015; 2 (1):33-43.
Chicago/Turabian StyleJ.M. Belman-Flores; J.M. Barroso-Maldonado; S. Ledesma. 2015. "Modeling of the Compression Process for Refrigerants R134a and R1234yf of a Variable Speed Reciprocating Compressor." Journal of Advanced Thermal Science Research 2, no. 1: 33-43.