MODELLING AN ABSORPTION SYSTEM ASSISTED BY SOLAR ENERGY

Abstract This paper studies the global modelling of an absorption system working with LiBr–H2O assisted by solar energy. It satisfies the air-conditioning necessities of a classroom in an educational centre in Puerto Lumbreras, Murcia, Spain. The absorption system utilises a set of solar collectors to satisfy the thermal necessities of the vapour generator. Several models have been developed for the characterisation of the absorption equipment, one of them based on the manufacturer data catalogue and the others based on neural networks. These are based on the Adaptive Resonance Theory. They are proved to predict the outlet temperature of the absorption system with efficiency. The experimental data obtained during two years of performance have been used for training and validation. For the dynamical simulation of the global system, TRNSYS software is proposed. The model can easily be programmed in Fortran and included in TRNSYS code. The paper is closed drawing some conclusions.

[1]  R. Lizarte,et al.  Air conditioning using an air-cooled single effect lithium bromide absorption chiller: Results of a trial conducted in Madrid in August 2005 , 2008 .

[2]  Eduardo Gómez-Sánchez,et al.  Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro fuzzy systems , 2004 .

[3]  Adnan Sözen,et al.  Performance prediction of a solar driven ejector-absorption cycle using fuzzy logic , 2004 .

[4]  Adnan Sözen,et al.  Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle , 2004 .

[5]  R. J. Romero,et al.  Comparison of the theoretical performance of a solar air conditioning system operating with water/lithium bromide and an aqueous ternary hydroxide , 2000 .

[6]  S. C. Kaushik,et al.  Assessment of diffuse solar energy under general sky condition using artificial neural network , 2009 .

[7]  Da-Wen Sun,et al.  Computer Simulation and Optimization of Ammonia-Water Absorption Refrigeration Systems , 1997 .

[8]  Juan Luis Castro,et al.  Fuzzy systems with defuzzification are universal approximators , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[9]  José Ramón García Cascales,et al.  Aspectos sobre el modelado y diseño de un sistema de refrigeración por absorción asistido con energía solar , 2008 .

[10]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[11]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[12]  M. C. Rodríguez Hidalgo,et al.  Energy and carbon emission savings in Spanish housing air-conditioning using solar driven absorption system , 2008 .

[13]  O. Kaynakli,et al.  THERMODYNAMIC ANALYSIS OF ABSORPTION REFRIGERATION SYSTEM BASED ON ENTROPY GENERATION , 2007 .

[14]  Yannis A. Dimitriadis,et al.  Learning from noisy information in FasArt and FasBack neuro-fuzzy systems , 2001, Neural Networks.

[15]  G. Mihalakakou,et al.  On estimating soil surface temperature profiles , 2002 .