Optimization Matching Analysis for Coal-fired Power Plant

• Main Authors: Ming-ChouTsai, 蔡銘洲
• Other Authors: Chia-Yon Chen
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/72607197342122093457

碩士 === 國立成功大學 === 工學院工程管理專班 === 98 === As it is well known, traditional fire power plants are coal-fired. The exhaust gas, however, contains substances such as fly ash, sulfur oxide, nitrogen oxide and carbon dioxide, which worsen the environment and global warming. Unfortunately, there is no single type of coal that can be used to generate electricity with regard to the environmental protection. Apart from that, the quality of coal has a tremendous influence on the operation of the boiler. As a result, it requires a circumspect investigation and evaluation to select and matching the coal. Since various characteristics of the coal will decrease boiler efficiency and exceed the limit set by the environmental protection policy, the cost will consequently increase.To learn to lower the cost, the study investigates the conditions of the equipment in power plants and the data of burning coal. Then, it analyses both the relationship between the coal quality and the materials concerned, and the operation cost that is affected. Using the Linear Programming Model, this study obtains the optimization matching formula of coal with the minimum price and the range of price changes. This case study indicates that, by setting up the Linear Programming Model,the optimization matching formula can be obtained despite various limitations. Since the material purchasing, coal price, coal amount,payment and the emissions of air pollutants can be estimated in advance, it helps the power plant to set up the annual budget, including material purchasing and sales amount, and therefore to improve the overall operational performance. In addition, Situational Analysis can help power plants to increase the accuracy of investment analysis as they try to ameliorate the equipments or expand the power plant.