روشی برای بازیابی طراحی هندسی حرارت مولد بخار با استفاده از الگوریتم ژنتیک
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|8146||2013||7 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Applied Thermal Engineering, Volume 52, Issue 1, 5 April 2013, Pages 77–83
This paper shows how the geometric design of heat recovery steam generators (HRSG) can be achieved. The method calculates the product of the overall heat transfer coefficient (U) by the area of the heat exchange surface (A) as a function of certain thermodynamic design parameters of the HRSG. A genetic algorithm is then applied to determine the best set of geometric parameters which comply with the desired UA product and, at the same time, result in a small heat exchange area and low pressure losses in the HRSG. In order to test this method, the design was applied to the HRSG of an existing plant and the results obtained were compared with the real exchange area of the steam generator. The findings show that the methodology is sound and offers reliable results even for complex HRSG designs.
he Combined Cycle Gas Turbine (CCGT) power plant efficiency is affected by the design of all its components. Among them, the Heat Recovery Steam Generator (HRSG) is one of the most important, since there the union of the gas and steam cycles takes place. The power generated by the steam cycle is strongly dependent on the HRSG thermal efficiency. The power generated by the steam cycle is highly dependent on the HRSG thermal efficiency. Therefore the thermodynamic HRSG design parameters must be carefully selected in order to achieve the optimum performances in the combined cycle. Many authors have directed their researches to the optimization of CCGT power plants and HRSG , ,  and . Certain works are specifically concerned with the design of the HRSG, for instance, that of Franco and Giannini  show a method, based on hierarchical strategy, for the optimal design of the HRSG, considering the maximization of the compactness index and the minimization of the pressure losses. On the same topic, Manassaldi et al.  proposed a methodology for the HRSG design. This methodology applies a mixed nonlinear program model to optimize the design according to three criteria: net power maximization, the ratio between net power and material weight maximization, and net heat transfer maximization. The results of this paper are accurate but the economic optimization problem is not discussed. On the other hand, Rovira et al. , Duran  and Valdés et al.  (the latter is the preceding work to this most recent one) made a thermoeconomic optimization which minimizes the generating cost or maximizes the annual cash flow of the plant, considering as independent design parameters of the HRSG the thermodynamic design variables: namely, drum pressure, pinch points, approach points and temperature differences at the superheater exit. In this work, the selection of a given set of these thermodynamic parameters led to the determination of the products (UA) of the overall heat transfer coefficient (U) by the area (A) of the HRSG heat exchange surfaces. Nevertheless, various pairs of U and A might lead to the same UA product. Thus, as the same thermal CCGT performances could be obtained with different HRSG geometric designs, the HRSG geometric design was undetermined and there is still room for improvement of the optimization process. This more recent work applied the results obtained with the thermoeconomic optimization proposed by Valdés et al.  and Durán and Galindo  and presents a method that solves the uncertainty in the determination of the HRSG geometric design parameters. The method proposed here uses a genetic algorithm in order to find the geometric design of the HRSG which fulfills the desired UA product while, at the same time, obtaining a small heat exchange area and low pressure losses.
نتیجه گیری انگلیسی
In the design of an HRSG it is important to take into account not only the thermodynamic design parameters, but also the economic ones. Because of this, many authors develop thermoeconomic optimization systems that find the best thermodynamic design parameters. However, the first conclusion of the work is that it is possible to continue these methodologies by means of optimizing the geometric design of the HRSG. Through the results, it was observed that the proposed method achieves the desired UA for each section and is able to find an HRSG geometric design with small heat exchange area and low pressure loss. Firstly, the method proposed in this work is useful for obtaining a suitable geometric design of the HRSG that gives accurate UA values and, as a consequence, correct thermodynamic design parameters. Moreover, it is possible to find an HRSG design with a minimum heat exchange area while at the same time controlling the pressure losses. It is also important to point out that the results found by the algorithm applied match with the suggestions for the design of HRSGs established by Dechamps  and Ganapathy , in particular because Ganapathy suggests that a greater area does not necessarily mean a better heat transfer: the most important parameter is the product of the overall heat transfer coefficient by the area. This method, with the thermoeconomic optimization tool proposed previously in Valdés et al., and used in Duran et al.  allows finding an appropriate design of a CCGT power plant (see Fig. 3), with minimum cost or maximum cash flow and, at the same time, with a small HRSG area. Hence, this method is useful for both the design and the optimization of CCGT power plants. Furthermore, for future works this methodology may be applied for the design of supercritical HRSG . References