Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle

Theoretical thermodynamic analysis of the absorption thermal systems is at present too complex because of analytic functions calculating the thermodynamic properties of fluid couples involving the solution of complex differential equations and simulation programs. This paper proposes a new approach to performance analysis of a solar-driven ejector-absorption refrigeration system (EARS) with an aqua/ammonia working fluid. Use of artificial neural-networks (ANNs) has been proposed to determine the performance parameters as functions of only the working temperature, under various working conditions. Thus, this study is considered to be helpful in predicting the performance of an EARS prior to it being set up in an environment where the temperatures are known. The statistical coefficient of multiple determinations (R2 - value) equals to 0.976, 0.9825, 0.9855 for the coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F), respectively. These accuracies are acceptable for the design of an EARS. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modelling programs. The importance of the ANN approach, apart from reducing the time required, is that it is possible to find solutions that make solar-energy applications more viable and thus more attractive to potential users, such as solar engineers. Also, this approach has the advantages of computational speed, low cost for feasibility, rapid turnaround (which is especially important during iterative design-phases), and ease of design by operators with little technical experience.

[1]  Rodney L. McClain,et al.  Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data , 2001 .

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

[3]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

[4]  Ashish Dwivedi,et al.  Potential applications of artificial neural networks to thermodynamics: vapor–liquid equilibrium predictions , 1999 .

[5]  L. Puigjaner,et al.  Use of neural networks and expert systems to control a gas/solid sorption chilling machine , 1999 .

[6]  Soteris A. Kalogirou,et al.  Applications of artificial neural networks in energy systems , 1999 .

[7]  Mohamed Mohandes,et al.  Estimation of global solar radiation using artificial neural networks , 1998 .

[8]  E. Arcaklioğlu,et al.  Use of artificial neural networks for mapping of solar potential in Turkey , 2004 .

[9]  Adnan Sözen,et al.  Performance improvement of absorption refrigeration system using triple-pressure-level , 2003 .

[10]  S. Biswas,et al.  Liquid—vapour coexistence curve of methyl fluoride in the critical region , 1989 .

[11]  Soteris A. Kalogirou,et al.  Artificial neural networks for modelling the starting-up of a solar steam-generator , 1998 .

[12]  M. Ranjan,et al.  Solar resource estimation using artificial neural networks and comparison with other correlation models , 2003 .

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

[14]  Adnan Sözen,et al.  Effect of heat exchangers on performance of absorption refrigeration systems , 2001 .

[15]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[16]  Soteris A. Kalogirou,et al.  MODELING OF SOLAR DOMESTIC WATER HEATING SYSTEMS USING ARTIFICIAL NEURAL NETWORKS , 1999 .

[17]  Avi Levy,et al.  Numerical study on the design parameters of a jet ejector for absorption systems , 2002 .

[18]  Ch. Trepp,et al.  Equation of state for ammonia-water mixtures , 1984 .

[19]  Soteris A. Kalogirou,et al.  Artificial neural networks used for the performance prediction of a thermosiphon solar water heater , 1999 .

[20]  Adnan Sözen,et al.  Development and testing of a prototype of absorption heat pump system operated by solar energy , 2002 .

[21]  Adnan Sözen,et al.  A new approach to thermodynamic analysis of ejector–absorption cycle: artificial neural networks , 2003 .

[22]  Zhang Lin,et al.  Global optimization of absorption chiller system by genetic algorithm and neural network , 2002 .

[23]  Ch. Trepp,et al.  Simulation of a solar driven aqua-ammonia absorption refrigeration system Part 1: mathematical description and system optimization , 1987 .

[24]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[25]  Soteris A. Kalogirou,et al.  Optimization of solar systems using artificial neural-networks and genetic algorithms , 2004 .

[26]  D. Richon,et al.  Modeling of thermodynamic properties using neural networks: Application to refrigerants , 2002 .