Share

IIR document

Predictions of oil retention in horizontal and vertical refrigerant vapor lines of unitary split systems using a physics-based machine learning-aided model.

Number: 0405

Author(s) : BAHMAN A. M., ERFANIMATIN M., NOURANI P., LIU H., SHAH V. M., BRAUN J. E., GROLL E. A.

Summary

Due to deployment of variable-speed unitary air conditioning (AC) systems as well as the future implementation of newer HFO refrigerants, there is a need to upgrade line sizing guidelines to properly account for the effects of oil retention (OR). These new guidelines can be facilitated through the development and application of a model that can predict the OR in gas lines for commonly used refrigerant-lubricant combinations in the HVAC&R industry. This work aims to evaluate the prediction performance of machine learning (ML) models that are trained using OR data obtained for horizontal and vertical lines of different diameters (11, 17, 20 mm), for different refrigerants (R-134a, R-410A, R-32, and R-1234ze(E)) mixed with POE-32 lubricant at different flow conditions. The results of the predictions from the ML models are compared to an analytical model. The ML models were trained using experimentally collected data based on more than 240 tests. The inputs to each model are refrigerant conditions (type, temperature, pressure, and mass flow rate), pipeline dimension and orientation, injected oil mass flow rate, and viscosity, whereas the model output is OR. Several model types were investigated to predict the OR that included a physics-based model, two standalone ML models, and two physics-based machine learning-aided (PBMLA) algorithms. The results showed that the standalone ML algorithms performed poorly for OR prediction compared to the analytical model. The results also showed that the PBMLA models could predict OR slightly better than the analytical model.

Available documents

Format PDF

Pages: 10

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: Predictions of oil retention in horizontal and vertical refrigerant vapor lines of unitary split systems using a physics-based machine learning-aided model.
  • Record ID : 30031513
  • Languages: English
  • Subject: Technology
  • Source: Proceedings of the 26th IIR International Congress of Refrigeration: Paris , France, August 21-25, 2023.
  • Publication date: 2023/08/21
  • DOI: http://dx.doi.org/10.18462/iir.icr.2023.0405

Links


See other articles from the proceedings (491)
See the conference proceedings

Indexing