A low data requirement model of a variable-speed vapour compression refrigeration system based on neural networks

In this work a model of a vapour compression refrigeration system with a variable-speed compressor, based on a black-box modelling technique, is presented. The kernel of the model consists of a full customized radial basis function network, which has been developed to accurately predict the performance of the system with low cost data requirement in terms of input variables and training data. The work also presents a steady state validation of the model inside and outside the training data set, finding, in both cases, a good agreement between experimental values and those predicted by the model. These results constitute a first step to go through future research on fault detection and energy optimisation in variable-speed refrigeration systems.

[1]  Vojislav Kecman,et al.  New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks , 2001 .

[2]  Guoliang Ding,et al.  Steady-state simulation of screw liquid chillers , 2002 .

[3]  Pradeep Bansal,et al.  Transient simulation of vapour-compression packaged liquid chillers , 2002 .

[4]  Vojislav Kecman,et al.  Modelling of vapour-compression liquid chillers with neural networks , 2001 .

[5]  Stephen A. Billings,et al.  Radial basis function network configuration using genetic algorithms , 1995, Neural Networks.

[6]  Sandro Campos Amico,et al.  Experimental development of an intelligent refrigeration system , 2005 .

[7]  H. Holdack-Janssen,et al.  Continuous and Discontinuous Capacity Control for High Speed Refrigeration Compressors , 1984 .

[8]  P. Fanger,et al.  Spot Cooling - Part 1: Human response to cooling with air jet , 1994 .

[9]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[10]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Evolutionary optimization of RBF networks , 2000, Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks.

[11]  Derk J. Swider,et al.  A comparison of empirically based steady-state models for vapor-compression liquid chillers , 2003 .

[12]  H. C. Peitsman,et al.  Application of black-box models to HVAC systems for fault detection , 1996 .

[13]  Joaquín Navarro-Esbrí,et al.  A vapour compression chiller fault detection technique based on adaptative algorithms. Application to on-line refrigerant leakage detection , 2006 .

[14]  Ramón Cabello López,et al.  Monitorizacion en tiempo real del comportamiento energético de máquinas de producción de frío usando técnicas avanzadas de adquisición de señales , 2005 .

[15]  E. Clothiaux,et al.  Neural Networks and Their Applications , 1994 .

[16]  M. McLinden,et al.  NIST Standard Reference Database 23 - NIST Thermodynamic and Transport Properties REFPROP, Version 7.0 , 2002 .

[17]  Haralambos Sarimveis,et al.  A Fast and Efficient Algorithm for Training Radial Basis Function Neural Networks Based on a Fuzzy Partition of the Input Space , 2002 .

[18]  Syed M. Zubair,et al.  Design and performance evaluation of reciprocating refrigeration systems , 1999 .

[19]  Joaquín Navarro-Esbrí,et al.  Experimental evaluation of the internal heat exchanger influence on a vapour compression plant energy efficiency working with R22, R134a and R407C , 2005 .

[20]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[21]  J.P.H. Bourdouxhe,et al.  A toolkit for primary HVAC system energy calculation. Part 2: Reciprocating chiller models , 1994 .

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