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An adaptive neuro-fuzzy inference system (ANFIS) modelling of oil retention in a carbon dioxide air-conditioning system.

Author(s) : MEHRABI M., PESTEEI S. M.

Summary

In a closed loop vapour compression cycle, a small portion of the oil circulates with the refrigerant flow through the cycle components while most of the oil stays inside the compressor. The worst scenario of oil circulation in the refrigeration cycle is when large amounts of oil become logged in the system. Each cycle component has different amounts of oil retention. Because oil retention in refrigeration systems can affect performance and compressor reliability, it receives continuous attention from manufactures and operators. In this paper, an adaptive neuro fuzzy inference system (ANFIS) is used for modelling the effect of important parameters on oil retention in a carbon dioxide air-conditioning system is trained and tested with the experimental data taken from the experimental work by Lee (2003). In this way, the authors considered oil retention in a carbon dioxide air-conditioning system when oil injection occurs at evaporator as target parameter, refrigerant mass flow rate, oil mass flow rate, oil circulation ratio, gas cooler inlet pressure, evaporator inlet pressure, gas cooler inlet temperature gas cooler outlet temperature and evaporator outlet temperature as input parameters. Then, they randomly divided empirical data into train and test sections in order to accomplish modelling. The authors instructed ANFIS network by 75% of empirical data. 25% of primary data which had been considered for testing the approbativity of the modelling were entered into ANFIS model. Results were compared by two statistical criterions (R2, RMSE) with empirical ones. Considering the results, it is obvious that the proposed modelling by ANFIS is efficient and valid and it can also be promoted to more general states.

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Details

  • Original title: An adaptive neuro-fuzzy inference system (ANFIS) modelling of oil retention in a carbon dioxide air-conditioning system.
  • Record ID : 2010-1988
  • Languages: English
  • Publication date: 2010/07/12
  • Source: Source: Proc. 2010 int. Refrig. Air Cond. Conf., Purdue Univ.
    n. 2130; 7 p.; fig.; tabl.; 9 ref.

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