A comparison of empirically based steady-state models for vapor-compression liquid chillers

Applied Thermal Engineering

Volumn 23, Issue 5, 2003, Pages 539-556

  Swider, Derk J ^a  

^a UNIVERSITY OF STUTTGART   (Germany)

Author keywords

Chiller; Coefficient of performance; Model; Neural network; Regression; Steady state

Indexed keywords

MATHEMATICAL MODELS; RADIAL BASIS FUNCTION NETWORKS; REFRIGERATION; REGRESSION ANALYSIS; VAPORS;

LIQUID CHILLERS;

THERMODYNAMICS;

EID: 0037382315     PISSN: 13594311     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1359-4311(02)00242-9     Document Type: Article

References (21)

1. Browne M.W., Bansal P.K. An elemental NTU-e model for vapour-compression liquid chillers. International Journal of Refrigeration. 24(7):2001;612-627.
2. Bourdouxhe J.P., Grodent M., Lebrun J.J., Saavedra C.A. A toolkit for primary HVAC system energy calculation - Part 2: Reciprocating chiller models. ASHRAE Transactions. 100(2):1994;774-786.
3. Khan J.R., Zubair S.M. Design and performance evaluation of reciprocating refrigeration systems. International Journal of Refrigeration. 22(3):1999;235-243.
4. Van Hout U., Van den Bulck E. Modelling chiller performance using simultaneous equation-solving procedures. International Journal of Refrigeration. 17(3):1994;191-198.
5. Comstock M.C., Braun J.E., Groll E.A. The sensitivity of chiller performance to common faults. International Journal of HVAC&R Research. 7(3):2001;263-279.
6. Peitsman H.C., Bakker V.E. Application of black-box models to HVAC systems for fault detection. ASHRAE Transactions. 102(1):1996;628-640.
7. Stylianou M., Nikanpour D. Performance monitoring, fault detection, and diagnosis of reciprocating chillers. ASHRAE Transactions. 102(1):1996;615-627.
8. Gordon J.M., Ng K.C. Cool Thermodynamics. 2000;International Science Publishing, Cambridge.
9. DOE, DOE-2, Reference Manual, Part 1, Version 2.1, Lawrence Berkeley National Laboratory, Berkeley 1980.
10. Yik F.W.H., Lam V.K.C. Chiller models for plant design studies. Building Services Engineering Research and Technology. 19(4):1998;233-242.
11. Swider D.J., Browne M.W., Bansal P.K., Kecman V. Modelling of vapour-compression liquid chillers with neural networks. Applied Thermal Engineering. 21(3):2001;311-329.
12. Reddy T.A., Andersen K.K. An evaluation of classical steady-state off-line linear parameter estimation methods applied to chiller performance monitoring. International Journal of HVAC&R Research. 8(1):2002;101-124.
13. D.J. Swider, Experimental investigation and neural network modelling of vapour-compression liquid chillers, Student Research Project, University of Auckland, Auckland, 1999.
14. ASHRAE, Engineering analysis of experimental data, Guideline 2-1986 (RA 96), ASHRAE, Atlanta, 1996.
15. Nabney I.T. NETLAB: Algorithms for Pattern Recognition. 2002;Springer, London.
16. Kecman V. Learning and Soft Computing. 2001;MIT Press, Cambridge.
17. Bishop C.M. Neural Networks for Pattern Recognition. 1995;Oxford University Press, Oxford.
18. Montgomery D. Introduction to Statistical Quality Control. 2000;John Wiley and Sons, New York.
19. Rossi T.M., Braun J.E. A statistical, rule-based fault detection and diagnostic method for vapor-compression air conditioners. International Journal of HVAC&R Research. 3(1):1997;19-37.
20. Grimmelius H.T., Klein Woud J., Been G. On-line failure diagnosis for compression refrigeration plants. International Journal of Refrigeration. 18(1):1995;31-41.
21. Pau L.F. Failure Diagnosis and Performance Monitoring. 1981;Dekker, New York.