低温余热发电系统的运行优化研究

发布时间：2018-03-28 20:02

本文选题：有机朗肯循环　切入点：余热发电　出处：《华北电力大学》2014年硕士论文

【摘要】：随着经济的高速发展,中国对能源的需求越来越大,造成能源与环境问题日益突出,利用有机朗肯循环系统(ORC)回收低温余热进行发电能够有效地提高能源利用率,减少污染排放以及降低工业成本,因而近些年得到国内外学者广泛的关注。现实中,低温余热热源是不稳定的,而且外部环境也是时刻发生变化的,从而造成系统工况的变化,对低温余热发电有机朗肯循环系统性能造成很大影响。系统蒸发压力设定值应随着系统工况的变化而变化,使得系统输出功率和系统整体效率尽可能达到最大,从而实现对烟气余热的充分利用。本文首先对基本有机朗肯循环热力模型进行分析,在对各个部件能耗总结的基础上分析系统性能评价指标。其次,本文以ASPEN HYSYS软件为平台搭建了以蒸发器、膨胀机、冷凝器、工质泵为主要部件的有机朗肯循环系统模型,针对给定工况,通过调节系统参数,研究参数对系统性能的影响并分析其原因。最后,为了实现系统的输出功率和系统整体效率目标最大化,本文提出了两种不同运行优化策略对系统蒸发压力的设定值进行优化,一种是基于支持向量机(SVM)模型,一种基于遗传算法—最小二乘支持向量机模型(GA-LS-SVM)。实验结果表明,在系统工况发生变化的情况下,两种模型都能快速准确的预测出系统蒸发压力最优(次优)设定值,但比较发现,GA-LS-SVM模型精度要略优于SVM模型。
[Abstract]:With the rapid development of economy, the demand for energy in China is increasing, and the energy and environment problems are becoming more and more serious. Using the organic Rankine cycle system (ORC) to recover low temperature waste heat for power generation can effectively improve the energy efficiency. Reducing pollution emissions and reducing industrial costs have attracted extensive attention from domestic and foreign scholars in recent years. In reality, the low temperature waste heat source is unstable, and the external environment is changing at all times, resulting in the change of system working conditions. It has a great influence on the performance of organic Rankine cycle system for low temperature waste heat generation. The system evaporation pressure setting value should change with the change of system working conditions, so that the system output power and the overall efficiency of the system can be maximized as far as possible. Thus, the waste heat of flue gas can be fully utilized. In this paper, the basic organic Rankine cycle thermodynamic model is first analyzed, and the system performance evaluation index is analyzed on the basis of summing up the energy consumption of each component. Secondly, the evaporator, expander and condenser are built on the platform of ASPEN HYSYS software. The organic Rankine circulatory system model with working fluid pump as the main component, according to the given working condition, by adjusting the system parameters, the influence of the parameters on the system performance is studied and the reasons are analyzed. In order to maximize the output power of the system and the overall efficiency of the system, two different operation optimization strategies are proposed to optimize the evaporation pressure of the system. One is based on the support vector machine (SVM) model. A GA-LS-SVM-based genetic algorithm (LS-SVM) model is proposed. The experimental results show that both models can predict the optimal (sub-optimal) evaporation pressure of the system quickly and accurately when the operating conditions of the system change. However, the accuracy of GA-LS-SVM model is slightly better than that of SVM model.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM617

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