Simulation and Optimization of Steam Power Plant by MATLAB

• Main Authors: Jung-Chieh Chiu, 邱榮傑
• Other Authors: Cheng-Liang Chen
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/mbscad

碩士 === 國立臺灣大學 === 化學工程學研究所 === 105 === Steam power plant is one of the main power sources for supplying mechanical power, electricity and heat in most typical chemical plants. A better understanding and proper management of steam distribution systems is crucial to maintain normal plant operation and to reduce the operating costs. In the simulation of steam power plants, equation oriented method usually takes a shorter calculation time. However, sequential modular approach (SMA) can provide more flexibility as well as simplicity when one is modifying the steam supply strategy of the power plant(Smith, 2005). These features of SMA are more suitable for steam power plants since modification of a utility system is more likely required during the whole lifetime of a plant. Though similar works can be achieved easily by using some commercial software such as Aspen Utilities Planner® (AUP), the use of SMA for power plant simulation and optimization is more economic if the studied utility system is not complicated. This work develops relevant models for several common units in a typical steam power plant, including boiler, steam turbine, valve, steam header, deaerator, pump and condenser. The developed basic models are then configured to simulate steam power plants by the method of SMA (Holmgren, 2006; Sun & Smith, 2015). The validated plant model can be used for simulation, analysis and optimization under various operating scenarios(Knopf, 2011). To demonstrate the developed plant simulation program, a typical steam power plant adopted from the literature (Nicholas & Jack, 2013)is established to compare with the result of the commercial software. The second steam system is based on actual plant, and total cost which include fuel cost and feed water cost is calculated. The total cost of this design is 3967 USD/h and with optimization the cost can be reduced to 3765 USD/h, which is a 5.1% cut-down.