Dynamic linear modeling of a refrigeration process with electronic expansion valve actuator

Abstract Usually, commercial control solutions for superheat control still use PID controllers as a standard. Although there are several applications of advanced control in refrigeration processes in the literature, there isn't a consensus about the optimal control solution for each system. The implementation of advanced control algorithms ultimately depends on accurate process knowledge in the form of dynamic mathematical models. This study aims to take a first step toward the designing an adaptive stochastic MPC controller for superheat control in an R404 refrigeration cycle with electronic expansion valve by developing stochastic dynamic models of the process. Both time-varying and time-invariant versions of the models are identified. Statistical validation results show whitening of the residuals of the time-invariant models, creating a basis for comparison. The recursive estimation of the time-varying parameters was realized with the Kalman Filter and the Forgetting Factor algorithms. Results of validation tests by simulation show good results, with average output errors between 0.05 and 1.39°C, indicating that the ARMAX with time-varying parameters may be a good presentation for this system.

[1]  Lennart Ljung,et al.  What Can Regularization Offer for Estimation of Dynamical Systems? , 2013, ALCOSP.

[2]  Yunting Ge,et al.  Performance simulation of refrigerated display cabinets operating with refrigerants R22 and R404A , 2008 .

[3]  Performance Comparison of a Truck Refrigeration System with R404A, R134a, R1234yf, and R744 Refrigerants under Frosting Conditions , 2016 .

[4]  Lin Yang,et al.  Electronic expansion valve mass flow rate prediction based on dimensionless correlation and ANN model , 2015 .

[5]  Gabriele Pannocchia,et al.  Disturbance models for offset‐free model‐predictive control , 2003 .

[6]  J. W. Modestino,et al.  Adaptive Control , 1998 .

[7]  Stephen B. M. Beck,et al.  Illustrating the relationship between the coefficient of performance and the coefficient of system performance by means of an R404 supermarket refrigeration system , 2016 .

[8]  Jingyi Wu,et al.  Research on the control laws of the electronic expansion valve for an air source heat pump water hea , 2011 .

[9]  Flávio Vasconcelos da Silva,et al.  A neuro-fuzzy identification of non-linear transient systems: Application to a pilot refrigeration plant , 2011 .

[10]  Kenneth R. Muske,et al.  Disturbance modeling for offset-free linear model predictive control , 2002 .

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[12]  Antonio Messineo,et al.  Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building , 2014 .

[13]  Luisa F. Cabeza,et al.  A novel numerical methodology for modelling simple vapour compression refrigeration system , 2017 .

[14]  H. Ertunç,et al.  Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system , 2008 .

[15]  Ciro Aprea,et al.  Fuzzy control of the compressor speed in a refrigeration plant , 2004 .

[16]  C. H. Chiou,et al.  The application of fuzzy control on energy saving for multi-unit room air-conditioners , 2009 .

[17]  Yucai Zhu,et al.  Multivariable System Identification For Process Control , 2001 .

[18]  Liang-Liang Shao,et al.  Refrigerant flow through electronic expansion valve: Experiment and neural network modeling , 2016 .

[19]  Armin Hafner,et al.  Investigation on CO2 hybrid ground-coupled heat pumping system under warm climate , 2016 .

[20]  James E. Braun,et al.  Semi-empirical modeling and analysis of oil flooded R410A scroll compressors with liquid injection for use in vapor compression systems , 2016 .

[21]  Shengwei Wang,et al.  Multiple ARMAX modeling scheme for forecasting air conditioning system performance , 2007 .

[22]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[23]  Héctor J. Ciro-Velásquez,et al.  Identification and digital control of a household refrigeration system with a variable speed compressor. , 2014 .

[24]  Rodrigo Llopis,et al.  Energy improvements of CO2 transcritical refrigeration cycles using dedicated mechanical subcooling , 2015 .

[25]  Karel J. Keesman,et al.  System Identification: An Introduction , 2011 .

[26]  Henrik Madsen,et al.  Load forecasting of supermarket refrigeration , 2014, 1406.5854.

[27]  Julio Ariel Romero,et al.  A simplified black-box model oriented to chilled water temperature control in a variable speed vapour compression system , 2011 .

[28]  S. Marinetti,et al.  Water-side reversible CO2 heat pump for residential application , 2016 .

[29]  Armin Hafner,et al.  Performance comparison of fixed-and controllable-geometry ejectors in a CO2 refrigeration system. , 2016 .

[30]  Lennart Ljung,et al.  Theory and applications of self-tuning regulators , 1977, Autom..

[31]  Rodrigo Llopis,et al.  Experimental evaluation of a CO2 transcritical refrigeration plant with dedicated mechanical subcooling , 2016 .

[32]  Lennart Ljung,et al.  Theory and Practice of Recursive Identification , 1983 .

[33]  Paride Gullo,et al.  Modelling commercial refrigeration systems coupled with water storage to improve energy efficiency and perform heat recovery , 2016 .

[34]  Wei Liang,et al.  MPC control for improving energy efficiency of a building air handler for multi-zone VAVs , 2015 .

[35]  Stefano Marelli,et al.  Modeling of R-410A variable capacity compressor with Modelica and experimental validation , 2015 .

[36]  B. Saleh,et al.  Artificial neural network models for depicting mass flow rate of R22, R407C and R410A through electronic expansion valves , 2016 .

[37]  Boris G. Vega Lara,et al.  Offset-free model predictive control for an energy efficient tropical island hotel , 2016 .

[38]  T.-J. Yeh,et al.  Identification and control of multi-evaporator air-conditioning systems , 2007 .

[39]  H. Metin Ertunç,et al.  An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower , 2011, Expert Syst. Appl..

[40]  S. Billings.  Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains , 2013 .

[41]  Shiming Deng,et al.  Multivariable control-oriented modeling of a direct expansion (DX) air conditioning (A/C) system , 2008 .

[42]  Martin Guay,et al.  Performance Improvement of Extremum Seeking Control using Recursive Least Square Estimation with Forgetting Factor , 2016 .