Share

Recommended by the IIR / IIR document

A comparative study of various machine learning methods for performance prediction of an evaporative condenser.

Author(s) : BEHNAM P., FAEGH M., SHAFII M. B., KHIADANI M.

Type of article: IJR article

Summary

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.

Available documents

Format PDF

Pages: 280-290

Available

  • Public price

    20 €

  • Member price*

    Free

* Best rate depending on membership category (see the detailed benefits of individual and corporate memberships).

Details

  • Original title: A comparative study of various machine learning methods for performance prediction of an evaporative condenser.
  • Record ID : 30028305
  • Languages: English
  • Subject: Technology
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 126
  • Publication date: 2021/06
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2021.02.009

Links


See other articles in this issue (25)
See the source

Indexing