This paper discusses the comparison of three different methods for the optimization of an air-conditioning system from an exergonomic point of view. The reference design is an all air system in typical summer operation. To optimize the system on the basis of the exergonomic approach, it was necessary to the set up the physical model (based on mass, energy, and exergy balance equations) and the economic model in order to define the decision variables and the objective function. The latter is the total cost of the whole assembly, obtained by summing up the capital costs to the running costs. The Szargut-Tsatsaronis (S-T), the Montecarlo, and the Lagrange multipliers methods are then applied with the aim of minimizing the objective function. The results may be summarized as follows: the S-T method is an iterative procedure which allows the designer to control and guide the optimization process; however, the outcome may depend on the designer’s judgment and skill. The Monte Carlo method is an almost totally automated approach; it is based on stochastic techniques and may require relatively long run time. The Lagrange multipliers method, in addition to the optimum value for the decision variables, makes it possible to deduce the sensitivity coefficients, thus providing further information about system performance. For the case under consideration, the three methods provide comparable results in terms of decision variables and minimum global costs.
A Comparison of Methods for Optimizing Air-Conditioning Systems According to the Exergonomic Approach
Contributed by the Advanced Energy Systems Division for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received by the AES Division, April 30, 1999; revised manuscript received June 15, 2001. Associate Editor: W. J. Wepfer.
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Marletta, L. (June 15, 2001). "A Comparison of Methods for Optimizing Air-Conditioning Systems According to the Exergonomic Approach ." ASME. J. Energy Resour. Technol. December 2001; 123(4): 304–310. https://doi.org/10.1115/1.1410934
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