A comparative study of various machine learning methods for performance prediction of an evaporative condenser. (June 2021)

• Record Type: Journal Article
• Authors: Behnam, Pooria; Faegh, Meysam; Shafii, Mohammad Behshad; Khiadani, Mehdi
• Is Part Of: International journal of refrigeration. Volume 126(2021)
• Page Start: 280
• Page End: 290
• Publication Date: 2021-06
• Subjects: Evaporative condenser -- Machine learning -- Artificial neural networks -- Support vector regression -- Decision tree -- Random forest; Condenseur évaporatif -- Apprentissage automatique -- Réseaux neuronaux artificiels -- Régression à vecteurs de supports -- Arbre décisionnel -- Forêt d'arbres décisionnels; Refrigeration and refrigerating machinery -- Periodicals; 621.56
• DOI: 10.1016/j.ijrefrig.2021.02.009
• Languages: English
• ISSNs: 0140-7007

Abstract

Highlights: The performance of an evaporative condenser was studied via data-driven models. A large experimental dataset (339 tests) compared to previous studies was employed. The robustness of SVR, DT, and RF models compared to MLPANN was investigated. Feature importance analysis was performed by the random forest model. The effect of dataset size on the predictive performance of the models was studied. Abstract: Evaporative condensers are regarded as highly-efficient and eco-friendly heat exchangers in refrigeration systems. Data-driven methods can play a key role in performance prediction of evaporative condensers, conducted without the complexity of theoretical analysis. In this study, four machine learning models including multi-layer perceptron artificial neural network (ANNMLP), support vector regression (SVR), decision tree (DT), and random forest (RF) models have been employed to predict heat transfer rate and overall heat transfer coefficient of a small-scale evaporative condenser functioning under a wide range of working conditions. A set of experimental tests were conducted, where inlet air dry/wet-bulb temperatures, spraying water and condenser saturation temperatures, refrigerant, and air flow rates were considered as main influencing parameters. The results show that the ANNMLP followed by SVR, and RF models possess the best generalization capability. Further, the dataset size analysis indicates that SVR is the best model to predict heat transfer rate for small dataset sizes. Additionally, feature importance analysis by the RF model reveals that refrigerant flow rate is the most influencing parameter.