Evaluation of machine learning approaches for estimating thermodynamic properties of new generation refrigerant R513A
Tài liệu tham khảo
Yang, 2019, Experimental study on R1234yf/R134a mixture (R513A) as R134a replacement in a domestic refrigerator, Appl Therm Eng, 146, 540, 10.1016/j.applthermaleng.2018.09.122
Sun, 2020, Energy and exergy analyses of R513a as a R134a drop-in replacement in a vapor compression refrigeration system, Int J Refrig, 112, 348, 10.1016/j.ijrefrig.2019.12.014
Navarro-Esbrí, 2013, Experimental analysis of R1234yf as a drop-in replacement for R134a in a vapor compression system, Int J Refrig, 36, 870, 10.1016/j.ijrefrig.2012.12.014
Fukuda, 2014, Low GWP refrigerants R1234ze(E) and R1234ze(Z) for high temperature heat pumps, Int J Refrig, 40, 161, 10.1016/j.ijrefrig.2013.10.014
Qi, 2013, Experimental study on evaporator performance in mobile air conditioning system using HFO-1234yf as working fluid, Appl Therm Eng, 53, 124, 10.1016/j.applthermaleng.2013.01.019
Spatz, 2012, Latest developments of low global warming refrigerants for chillers
Shapiro, 2012, Drop-in testing of next-generation R134a alternates in a commercial bottle cooler/freezer
Mota-Babiloni, 2018, Experimental exergy analysis of R513A to replace R134a in a small capacity refrigeration system, Energy, 162, 99, 10.1016/j.energy.2018.08.028
Yıldız, 2021, Investigation of using R134a, R1234yf and R513A as refrigerant in a heat pump, Int J Environ Sci Technol, 18, 1201, 10.1007/s13762-020-02857-z
Mota-Babiloni, 2017, Experimental assessment of R134a and its lower GWP alternative R513A, Int J Refrig, 74, 682, 10.1016/j.ijrefrig.2016.11.021
Al-Sayyab, 2022, Comprehensive experimental evaluation of R1234yf-based low GWP working fluids for refrigeration and heat pumps, Energy Convers Manag, 258, 10.1016/j.enconman.2022.115378
Devecioʇlu, 2015, Characteristics of some new generation refrigerants with low GWP, Energy Procedia, 75, 1452, 10.1016/j.egypro.2015.07.258
Mota-Babiloni, 2017, Experimental assessment of R134a and its lower GWP alternative R513A, Int J Refrig, 74, 680, 10.1016/j.ijrefrig.2016.11.021
Yıldız, 2021, A review of stability, thermophysical properties and impact of using nanofluids on the performance of refrigeration systems, Int J Refrig, 129, 342, 10.1016/j.ijrefrig.2021.05.016
Ağbulut, 2021, Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles–diesel blends using different machine learning algorithms, Energy, 215, 10.1016/j.energy.2020.119076
Mohanraj, 2012, Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems - A review, Renew Sustain Energy Rev, 16, 1340, 10.1016/j.rser.2011.10.015
Longo, 2020, Machine learning approach for predicting refrigerant two-phase pressure drop inside Brazed Plate Heat Exchangers (BPHE), Int J Heat Mass Transf, 163, 10.1016/j.ijheatmasstransfer.2020.120450
Ahmed, 2021, Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review, Sustain Energy Technol Assessments, 47, 101488, 10.1016/j.seta.2021.101488
Wan, 2021, Machine-learning-based compressor models: A case study for variable refrigerant flow systems, Int J Refrig, 123, 23, 10.1016/j.ijrefrig.2020.12.003
Honeywell. Solstice® 513A (R-513A) | European Refrigerants 2022. https://www.honeywell-refrigerants.com/europe/product/solstice-513a-r-513a/ (accessed March 16, 2022).
Friedman, 2001, Greedy function approximation: A gradient boosting machine, Ann Stat, 29, 1189, 10.1214/aos/1013203451
Freund, 1999, A brief introduction to boosting, J Japanese Soc Artif Intell, 14, 771
Chen, 2016, XGBoost: A scalable tree boosting system, 785
Altman, 1992, An introduction to kernel and nearest-neighbor nonparametric regression, Am Stat, 46, 175
Quinlan, 1986, Induction of decision trees, Mach Learn, 1, 81, 10.1007/BF00116251
Gelman, 2019, Why high-order polynomials should not be used in regression discontinuity designs, J Bus Econ Stat, 37, 447, 10.1080/07350015.2017.1366909
Bayona, 2019, An insight into RBF-FD approximations augmented with polynomials, Comput Math with Appl, 77, 2337, 10.1016/j.camwa.2018.12.029
Farsi, 2021, On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach, IEEE Access, 9, 31191, 10.1109/ACCESS.2021.3060290
Şencan, 2022, Determination with Gene Expression Programming of the relationship between socio-economic variables and greenhouse gas emissions in Turkey, KMU J Soc Econ Res, 24, 81
Şencan, 2022, Estimation of net energy consumption for Turkey based on economic factors, El-Cezerî J Sci Eng, 9, 1101
Pasini, 2015, Artificial neural networks for small dataset analysis, J Thorac Dis, 7, 953
Song QC, Tang C, Wee S. Making sense of model generalizability: A tutorial on cross-validation in R and shiny: 2021;4. doi:10.1177/2515245920947067.
Gurian, 2021, Repeated double cross-validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients, Anal Bioanal Chem, 413, 1303, 10.1007/s00216-020-03093-7
Lalwani, 2022, Customer churn prediction system: a machine learning approach, Computing, 104, 271, 10.1007/s00607-021-00908-y
Prakash, 2022, The temporal overfitting problem with applications in wind power curve modeling, Technometrics