Arzu Şencan Şahin’s research while affiliated with Isparta University of Applied Sciences and other places

This study investigates the energetic and exergetic performance of subcooled and superheated vapor compression refrigeration system utilizing data mining techniques. New-generation refrigerants R457A and R459B, considered alternatives to R404A, were utilized in the system. The analysis focuses on the impact of temperatures in the subcooling, superheating, condenser and evaporator. Data mining methods including multilayer perceptron (MLP), linear regression, M5 rules, M5P model tree (M5P), random committee and decision table models were used to estimate both energy and exergy efficiencies. The MLP model proved to be the most effective approach for predicting the energy (COP) and exergy efficiencies of R457A and R459B. When the predicted and actual COP values were compared, R-squared (R2) values of 0.9997 and 0.9994 were obtained for R457A and R459B, respectively. Similarly, the R2 values for exergy efficiency were 0.9984 and 0.9989 for the same refrigerants. These results demonstrate the successful application of data mining, in particular the MLP model, in evaluating the complex processes involved in refrigeration system performance analysis. This approach provides engineers with a fast, accurate and user-friendly method for predicting the behavior of refrigeration systems.

Organic Rankine Cycles (ORCs) are identified as one of the most promising technologies for generating electricity from low-grade heat sources. Unlike conventional Rankine cycles, ORCs operate at lower temperatures and pressures. This allows them to utilize organic fluids or refrigerants as the working fluid instead of water, which is better suited for high-pressure and high-temperature applications. The performance and design of an ORC system are heavily dependent on the chosen working fluid. Therefore, selecting the right working fluid is crucial for a specific application, such as solar thermal, geothermal, or waste heat recovery. This study analyzed the performance of ORCs using four different working fluids: R-134a, R-245fa, R417A, and R422D. The researchers investigated how variations in condensation and evaporation temperatures affect thermal efficiency, mass flow rate, pump power, and turbine pressure ratio. The Engineering Equation Solver (EES) program was used for analyses. The results demonstrated that condensation and evaporation temperatures significantly influence system performance. The study found that ORC systems using R417A and R422D exhibited higher efficiencies compared to the other working fluids analyzed. Additionally, these fluids required lower mass flow rates per unit of power generation compared to the other fluids.

Isıtma ve soğutma endüstrisinde çalışma akışkanı olarak soğutucu akışkanlar kullanılmaktadır. Çevresel sorunların artmasıyla birlikte dünyada giderek artan sera gazı emisyonu, küresel ısınma gibi çevresel problemlerin önüne geçmek için çevreye zarar veren akışkanların kullanımı azaltılmaya çalışılmaktadır. Bu kapsamda birçok soğutucu akışkan geliştirilmiş ve geliştirilmeye devam edilmektedir. Bu çalışmada, küresel ısınma potansiyeli (GWP) yüksek olan R404A yerine alternatif soğutucu akışkanlarının kullanıldığı buhar sıkıştırmalı soğutma sisteminin teorik analizi, mühendislik denklem çözücü (EES) programı kullanılarak yapılmıştır. Buhar sıkıştırmalı soğutma sisteminde GWP değeri düşük olan R454A, R454C, R455A, R457A ve R459B soğutucu akışkanları kullanılmıştır. Soğutucu akışkanların termodinamik özellikleri REFROP yazılımından alınmıştır. Yapılan analizler sonucunda en yüksek soğutma performans katsayısı (COP) değeri, kondenser sıcaklığı 30oC ve evaporatör sıcaklığı 5 oC iken R457A soğutucu akışkanı ile çalışan sistemde 8.03 olarak bulunmuştur. Aynı şartlarda, R404A soğutucu akışkanıyla çalışan sistemde, COP değeri 7.94 olarak bulunmuştur. En yüksek ekserji verimi ise kondenser sıcaklığı 30oC ve evaporatör sıcaklığı -5oC iken R457A soğutucu akışkanı ile çalışan sistemde 0.59 olarak bulunmuştur. Aynı şartlarda, R404A soğutucu akışkanıyla çalışan sistemde ekserji verimi 0.58 olarak bulunmuştur. En yüksek ekserji yıkımının ise soğutma sisteminin evaporatöründe olduğu görülmüştür. Sonuç olarak, R457A'nın hem GWP hem de COP ve ekserji verimliliği açısından R404A yerine kullanılabilecek en uygun alternatif soğutucu akışkanlardan biri olduğu belirlenmiştir.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R2) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

Hydrofluorocarbon (HFC) refrigerants (such as R134a and R404A), which are widely used in commercial refrigeration systems, contribute greatly to global warming. Because the global warming potential (GWP) values of HFC refrigerants are high. With increasing concerns about the environment (global warming, climate change, etc.) various legal regulations have been made to limit or phase out the use of refrigerants with high GWP values. To use the R471A refrigerant developed by Honeywell in 2022 as an environmentally friendly new-generation refrigerant in the commercial refrigeration system, the performance of the R471A refrigerant should be comprehensively evaluated. In this study, energy, exergy, and environmental analyses of using R471A refrigerant instead of R404A in low and medium-temperature commercial refrigeration systems were carried out and presented. Evaporator temperatures of − 30 °C, − 25 °C, − 20 °C, − 15 °C and − 10 °C and condenser temperatures of 30 °C, 40 °C, and 50 °C were selected to represent low and medium cooling temperatures and hot and cold country seasons. It was seen that R471A presents a lower mass flow rate (between 67 and 71%) and cooling capacity (between 61 and 68%) than R404A however has higher COP (between 6% and 14%) and R404A has exergy efficiency value of 6–14% lower than R471A. Also, from an environmental point of view, R471A has better environmental results than R404A.

Sürdürülebilir enerji türü olarak jeotermal kaynaklardan elektrik üretimi ülkemizde gittikçe yaygınlaşmaktadır. İkili (binary) jeotermal enerji santrali, jeotermal akışkan ısısından Organik Rankine Çevrimi (ORC) yardımı ile elektrik üreten sistemlerdir. Çevrimin ve çevrimi oluşturan her bir sistem elemanının enerji ve ekserji analizleri ayrıntılı bir şekilde yapılmıştır. Bu analizler için Engineering Equation Solver (EES) yazılımı kullanılmıştır. ORC sisteminde iş akışkanı olarak n-pentan kullanılmıştır. Hesaplamalar sonucunda tüm sistemin enerji verimi %6, ekserji verimi ise %45 olarak bulunmuştur. Sistemin farklı çalışma parametrelerine göre verimlerdeki değişimler grafikler aracılığıyla ortaya konmuştur. Santralde en yüksek ekserji kaybının 6.12 MW (tüm ekserji kaybının %26’sı) ile hava soğutmalı kondenser 2’de olduğu tespit edilmiştir. Çalışmada son olarak ekserji kayıplarının azaltılması ve sistem verimliliğin iyileştirilmesi için çeşitli öneriler ve tavsiyelerde bulunulmuştur.

In this study, an adaptive network-based fuzzy inference system is used to facilitate the thermodynamic performance analysis of a cooling system. Adaptive network-based fuzzy inference system can extract nonlinear relationships between variables using training data and has an advantage its speed and simplicity in modeling a multivariate problem. Since the evaporator, condenser, superheating, and subcooling temperatures affect the performance of the cooling system, thermodynamic efficiencies are estimated depending on these temperatures. New generation R516A, R515A, and R515B refrigerants were used in the refrigeration system. When the estimated and actual coefficient of performance values is compared, the mean absolute percentage error values were 7.3%, 5.7%, and 6.2% for these refrigerants respectively. The mean absolute percentage error values for exergy efficiency were 4.7%, 5.9%, and 5.5% for the same refrigerants respectively. The results show that the adaptive-network-based fuzzy inference system can be successfully used in complex processes such as estimating the thermodynamic performance of the refrigeration system. Thus, this model will help engineers to predict refrigeration system performance very accurately, quickly, and easily.

The use of refrigerants used in most refrigeration systems in use today has been or will be banned soon. Therefore, it is extremely important to search for alternative refrigerants. In this study, the thermodynamic properties of R471A, a very new refrigerant with low global warming potential, were estimated using a data mining approach. The results obtained from the data mining were compared with the actual data obtained from the REFROP program. The correlation coefficients (R2) for the saturation liquid enthalpy (hf), saturation vapor enthalpy (hg), saturation fluid entropy (sf), saturation vapor entropy (sg), superheated vapor enthalpy (hsh), superheated vapor entropy (ssh) have been found 0.9999, 0.9997, 1, 0.9884, 0.9992, and 0.9601, respectively. In addition, compared the hf, hg, sf, sg, hsh, and ssh values with the values calculated using the data mining and the actual values, and the percent error values. It was seen that the values calculated from the obtained formulations and the actual values are in good agreement. This study shows that the data mining approach can be successfully applied to determine enthalpy and entropy values for any temperature and pressure of R471A refrigerant. Thus, the determination of the thermodynamic properties of refrigerants and the simulation of vapor compression refrigeration systems become quite easy.

In the analysis of vapor compression refrigeration systems, simple and effective mathematical formulas are required to determine the thermodynamic properties of refrigerants. This study aims to determine the thermodynamic properties such as enthalpy, entropy, and specific volume of environmentally friendly new-generation refrigerants (R515B and R450A) with low global warming potential using gene expression programming (GEP). The thermodynamic properties calculated using the formulations obtained from the GEP model and the actual thermodynamic properties obtained from the REFROP software were compared. Performance evaluation criteria such as R², root mean square error, and mean absolute percent error are in the range of 0.86 to 0.999, 0.0000285–6.489, and 0.0009–0.35, respectively, and these values are acceptable. This study offers simple and efficient formulations to calculate the thermodynamic properties of new-generation refrigerants without the need for any software. So, the simulation of cooling and heat pump systems will be greatly facilitated.