对一种具有余热回收系统的LNG联合循环电站分析

提出了一种深度回收以液化天然气为燃料的联合循环电站中的蒸汽轮机排汽潜热和余热锅炉排烟中水蒸气潜热的系统.热力学分析表明,相对于240 MW的常规联合循环,所提系统的净电效率和火用效率分别提高1.6%和2.84%,其燃料利用率达62.88%;相对于传统联合循环,所提系统从每千克排烟中可多回收86.27 kJ/s的热量.可从低压省煤器出口抽取质量流率大约为46.1 kg/s,温度为167.45℃,压力为0.84 MPa 的热水送入余热利用子系统;经济性生分析显示,回收余热所增加投资的回收期为0.117年.这些特征对新建和已有LNG联合循环电站的改造具有很强的吸引力.     

Experimental study on liquid/solid phase change for cold energy storage of Liquefied Natural Gas (LNG) refrigerated vehicle

The present paper addresses an experimental investigation of the cold storage with liquid/solid phase change of water based on the cold energy recovery of Liquefied Natural Gas (LNG) refrigerated vehicles. Water as phase change material (PCM) was solidified outside the heat transfer tubes that were internally cooled by cryogenic nitrogen gas substituting cryogenic natural gas. The ice layer profiles were recorded in different cross-sections observed by digital cameras. The temperatures of cryogenic gas, tube wall and bulk region were measured by embedded thermocouples continuously. The results of the smooth tube experiments and the thermal resistance analysis prove that the main thermal resistance occurs in the gaseous heat transfer fluid (HTF) inner the tube. The enhancement of the inner heat transfer is achieved by adding wave-like internal fins. Besides, the results show that the ice layer not only increases in radial direction but also propagates in axial direction. It distributes in parabolic shape along the tube length due to the parabolic axial distribution of the tube wall temperatures. This investigation provides valuable references for the design and optimization of the cold energy storage unit of LNG refrigerated vehicles and for the numerical study on the unsteady two-dimensional conjugated heat transfer with phase change.     

Neural models of solar collectors for prediction of daily performance

This paper presents the application of an online identification neural technique to the prediction of the in-situ daily performance of solar collectors. First, it is shown that the use of the Laplace transform helps to find the order of an approximated model; the input of the studied system being the solar radiation. Then it is shown that an NNOE model can be more accurate using the right size of the regression vector; the learning database consisting of the data obtained during half a day. Finally, it is shown that a multiple inputs single output (MISO) NNOE model can be accurate; the inputs being the solar radiation and the thermal heat loss conductance that depends on the wind velocity. In any case the differential between the actual value of the daily energy and the value computed by a neural model is less than 0.5%.     

一种基于低品位热源的LNG冷能回收低温动力系统

在分析LNG物理冷Yong的基础上,提出了一种基于低品位热源的LNG冷能回收低温动力系统,并对影响系统循环效率的相关参数进行了研究。结果表明,在较低的热源温度下,系统的热效率和烟效率可以达到30％以上;对影响循环的主要参数分析表明,二次冷煤的冷凝温度及膨胀机进口压力对循环的效率影响很大。随着冷凝温度的降低及膨胀机进口压力的提高,循环热效率、Yong效率都将有所提高。