【作者】 邱广；

【导师】 曹家枞；

【作者基本信息】 东华大学 ， 供热、供燃气、通风与空调工程， 2005， 硕士

【摘要】 在我国工业锅炉由于数量多、热效率低,一直以来都是能源消耗、浪费的大户受到众多研究者的重视。为了提高锅炉的效率,加装蓄热器和多台锅炉的负荷优化分配是比较有效的技术措施。但现在动力系统常用的负荷优化分配方法主要是由传统的等微增率法发展而来,在应用中往往不能适应复杂的实际情况或得不到最优结果,所以在实际运行中主要是凭司炉经验确定锅炉的运行工况。在进行蓄热器设计时,很容易发现,蓄热器容积的最小化与锅炉房运行的最优化是一对矛盾的问题,当蓄热器容积达到最小时,一般都不能保证负荷在锅炉房内达到最优分配,或反之。所以在实际中需要针对问题解决这一对矛盾。 针对这些问题,本文首先提出了一种新的负荷优化分配模型,即最小差模型,用来解决传统优化模型不能满足实际操作的问题,使得分配过程更加易于实现。该算法对锅炉房内各锅炉的特性没有特别要求,且需要的原始数据不是锅炉的整条特性曲线而是任意两点的运行参数,所以更加容易得到精确的优化结果。在优化计算的过程中,通过比较各种锅炉间组合方案的运行结果,找到最优组合方式,以确保最后得到的是最优分配方式。当建立了数学模型后,通过选择合适的优化算法,编制了计算机程序,并应用于一实际的工业锅炉房的优化运行,计算实例证明利用该算法可以节约可观的燃油。 对于装有蓄热器的锅炉房,上述负荷优化分配必须是针对预测的负荷曲线进行的,所以本文紧接着进行了工业锅炉房负荷预测工作。首先通过比较选择了神经网络作为负荷预测方法,然后通过比较各种神经网络,根据实际过程的需要,选择了合适的模型和算法,并通过工程软件MATLAB 6.5实现了工业锅炉房未来24小时逐时负荷的预测,预测结果精度满足要求。对于装有蓄热器的工业锅炉房,预测的负荷曲线完全能保证正确的负荷优化分配。在进行神经网络预测过程中,还分析了输入数据对神经网络预测结果的影响,为进一步的工作奠定了基础。 上述优化过程是在锅炉房蓄热器已定的情况下进行的。为了解决蓄热器设计时最小容积和锅炉房运行最优的矛盾,本文提出了蓄热器设计最优化的寿命期内总费用最小模型,并通过计算机程序针对典型日负荷曲线同时实现蓄热器容积最小、锅炉房最优化运行和总费用最小三种模型的计算结果,以方便实际设计时根据需要选择合适的设计方法和设计容积。 在有蓄热器的工业锅炉房里,一个负荷周期内的运行必然涉及到分段运行的问题,最优的分段方案才是最节能的运行方式。所以本文在优化运行的过程中针对蓄热器容积一定的工业锅炉房,在整条负荷曲线上寻找最优分段方案。该方法为进一步优化研究奠定了基础。本文提出的负荷优化分配的原理,对其他动力系统也将有参考价值。更多还原

【Abstract】 In China, there are a great number of industrial boilers, of which a big part operates at rather low efficiencies. So it is of importance that the efficiency of industrial boiler plants must be improved to contribute to the solution of the energy crisis in our country. Optimization of load assignment in boiler plants can be an effective approach to enhance the energy efficiencies and has a good expectation in its wide use. The existing methods of optimal load assignment for boiler plants are generally based on the famous principle of coordination of incremental fuel costs, and do not work quite well as usual, for actual complexity in real cases. As a rule empirical methods of load assignment have still been used so far, although they may result in waste of energy. Thermal storage is another effective measure of energy saving for boiler plants. However it is a difficult problem in dealing with the optimal design of thermal storage. The smaller the volume of the storage tank, the lower the investment cost of the storage project. On the other hand, it is more difficult to perform the optimal load assignment when the storage volume gets the smallest. The best way to design a thermal storage optimally may be to fix a suitable point of compromise between the smallest volume and the lowest investment cost.In order to solve the current problem of load assignment to boilers in a boiler plant, a new model was proposed in this paper, being referred to as minimal departure model (MDM). The fuel cost curve of the boilers is not required by this model, which depends only on the data of two typical working conditions of boilers however. Unlike certain method, it is not necessary for MDM that the performance of the boilers in a plant be almost the same, which makes MDM applicable to any real cases. Based on the principle of MDM, a model of optimization programming is developed and a suitable algorithm is designed after a series of algorithms have been analyzed. The computer program is completed and applied to an industrial boiler plant. Computation practices show it is easy to find optimal load assignment and the resolutions are obviously better than those by other existing methods.Two optimal design methods of thermal storage for industrial boiler plants have been mentioned before. One is based on minimization of tank volume, with smallest thermal storage but without optimal operation mode of boiler plants. The other is according to the principle of the highest actual efficiencies with which boiler plantsalways work, while the storage volume may increase a lot. The results of both the design methods may not be thought the best. So the third method is proposed in this paper. This method can minimize the life cycle cost (LCC) of boiler plants, considering both the smaller storage volume and the higher actual efficiencies of boiler plants. A comprehensive computer program is developed, embodying the 3 optimal design models. Resolutions of the 3 models can be obtained simultaneously, What is the most preferable design could be determined from the 3 resolutions by the owners who take the charge of decision making.This paper also optimized the segmentation of a load cycle for the industrial boiler plant. In an industrial boiler plant different segmentation pattern means different fuel cost, and the optimal segmentation one provides the most saving of fuel. Boiler plants that work with an optimal segmentation pattern and an optimal load assignment can reach the biggest energy saving.For the sake of optimal operation, forecast of load for boiler plants is a key precondition. In this thesis, artificial neural network (ANN) is adopted to perform the load forecast. After comparing different ANN models, it is found that the cascaded neural network (CNN) is unable to perform well in load forecast for boiler plants. The factors that influence the errors of forecast are analyzed. A structure of BP neural network that yields the most accurate load forecast is constructed after analyses and trials of computation have been made. The final results of更多还原