Prediction of performance parameters of a hermetic reciprocating compressor applying an artificial neural network

Abstract

The efficiency of the compressor's thermodynamic, mechanical, and electric motors can be increased by minimizing their losses. Based on the design characteristics of the compressor, the current study proposes an artificial neural network (ANN) model to forecast the mass flow rate, cooling capacity, compression power, and discharge line loss of the hermetic reciprocating compressor as outputs. This study uses experimental and numerical results obtained by the fluid–structure interaction (FSI) method, and the whole findings are used as inputs for the machine learning method. Using 36 numerical and 3 experimental data sets, a multilayer network is created with input parameters defining mass flow rate, cooling capacity, compression power, and discharge line energy loss. The input parameters for the network model are the compressor's design parameters, which include the compressor speed, discharge valve thickness, and discharge valve length. When comparing the experimental and FSI methods for compressor speeds of 1300, 2100, and 3000 rpm, the FSI study's mass flow rate and cooling capacity parameters converged at 11.9%, 9.1%, and 9.3%, respectively, to their actual values. The convergence rate to experimental compression power data was −0.3%, 2.6%, and 11%. The numerical-experimental deviation for discharge line energy losses is 14.9%, −3.6%, and 7.8%, respectively, for 1300, 2100, and 3000 rpm. With the Levenberg–Marquardt (LM) ANN model, which is used in this study, the average squared error value is calculated as 1.75E-02 and the R2 value is calculated as 0.99976. ANN model can predict outputs with average deviation rates of less than 0.88%. It is seen that results obtained with the ANN method provide high convergence on the experimental and FSI results, while the results obtained with the regression model deviate from the compressor exhaust line results by more than ±20%, and the ANN method is more successful than regression.

Keywords

Reciprocating compressor efficiency thermodynamic loss artificial neural network (ANN)fluid-structure interaction (FSI)

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References

Liu

Duan

Yuan

, et al. An intelligent fault diagnosis method for reciprocating compressors based on LMD and SDAE. Sensors 2019; 19: 1041.

Ding

Horton

, et al. Development of an automated compressor performance mapping using artificial neural network and multiple compressor technologies. Int J Refrig 2020; 120: 66–80.

Sun

Jin

, et al. Proposal and experimental study on a diagnosis method for hermetic refrigeration compressor using dual time-frequency image fusion. Appl Sci 2022; 12: 3033.

Soltani

Sørensen

Leth

, et al. Fault detection and diagnosis in refrigeration systems using machine learning algorithms. Int J Refrig 2022; 144: 34–45.

Ledesma

Belman-Flores

Barroso-Maldonado

. Analysis and modeling of a variable speed reciprocating compressor using ANN. Int J Refrig 2015; 59: 190–197.

Belman-Flores

Ledesma

Barroso-Maldonado

, et al. A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model. Int J Refrig 2015; 59: 144–156.

Gabel KS and Bradshaw CR. Evaluation and quantification of compressor model predictive capabilities under modulation and extrapolation scenarios. Int J Refrig 2023; 149: 1–10.

Popovic

Shapiro

. A semi-empirical method for modeling a reciprocating compressor in refrigeration systems (No. CONF-950624-). Atlanta, GA, United States: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc, 1995.

Kocyigit

. Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network. Int J Refrig 2015; 50: 69–79.

10.

Cortés

Urquiza

Hernández

. Optimization of operating conditions for compressor performance by means of neural network inverse. Appl Energy 2009; 86: 2487–2493.

11.

Zendehboudi

Wang

. Utilization of ANN and ANFIS models to predict variable speed scroll compressor with vapor injection. Int J Refrig 2017; 74: 475–487.

12.

Tian

Yang

, et al. Hybrid ANN–PLS approach to scroll compressor thermodynamic performance prediction. Appl Therm Eng 2015; 77: 113–120.

13.

Belman-Flores

Ledesma

Garcia

, et al. Analysis of a variable speed vapor compression system using artificial neural networks. Expert Syst Appl 2013; 40: 4362–4369.

14.

Yang

Zhao

Zhang

, et al. Loss-efficiency model of single and variable-speed compressors using neural networks. Int J Refrig 2009; 32: 1423–1432.

15.

Wan

Cao

Hwang

, et al. Machine-learning-based compressor models: a case study for variable refrigerant flow systems. Int J Refrig 2021; 123: 23–33.

16.

Zhao

Zhang

. Neural-network-based polynomial correlation of single-and variable-speed compressor performance. HVAC&R Res 2009; 15: 255–268.

17.

Bacak

Pınarbaşı

Dalkılıç

. An experimental study on the determination of a hermetic reciprocating compressor’s discharge and suction losses. Proc Inst Mech Eng, Part E 2023; 09544089231154350.

18.

Bacak

Pınarbaşı

Dalkılıç

. A 3-D FSI simulation for the performance prediction and valve dynamic analysis of A hermetic reciprocating compressor: une Simulation FSI 3-D Pour La Prédiction Des Performances Et L'analyse Dynamique Des Vannes D'un Compresseur Hermétique. Int J Refrig 2023; 150: 135–148.

19.

American Society of Heating, Refrigerating and Air- Conditioning Engineers. Methods of Testing for Rating Positive Displacement Refrigerant Compressors and Condensing Units. American Society of Heating, Refrigerating and Air-Conditioning Engineers, USA, 1993.

20.

Redlich

Kwong

. On the thermodynamics of solutions. V. An equation of state. Fugacities of gaseous solutions. Chem Rev 1949; 44: 233–244.

21.

Issa

. Solution of the implicitly discretised fluid flow equations by operator-splitting. J Comput Phys 1986; 62: 40–65.

22.

Yakhot

Orszag

. Renormalization group analysis of turbulence. I. Basic theory. J Sci Comput 1986; 1: 3–51.

23.

Rhie

Chow

. Numerical study of the turbulent flow past an airfoil with trailing edge separation. AIAA J 1983; 21: 1525–1532.

24.

Karypis G, Kumar V. A Software Package For Partitioning Unstructured graphs, Partitioning meshes, and Computing Fill-Reducing Orderings of Sparse Matrices. Minneapolis, MN: University of Minnesota, Department of Computer Science and Engineering, Army HPC Research Center, 1998: p. 38.

25.

Senecal

Richards

Pomraning

, et al. A new parallel cut-cell Cartesian CFD code for rapid grid generation applied to in-cylinder diesel engine simulations (No. 2007-01-0159). SAE Technical Paper. 2007.