Development of Emission Factor and Management Indicators of CO2 for Municiple Solid Waste Incieration Plants

• Main Authors: Mei-Chun Chow, 周美均
• Other Authors: Hung-Yuan Lin
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/11505825037568091413

碩士 === 萬能科技大學 === 工程科技研究所 === 98 === Whether based on voluntary or compulsory projects, green house gas (GHG) reduction has turned out to be one of the priority issues for most countries, especially after the Kyoto Protocol became active in February, 2005. The quality management of a GHG inventory report has then become very important in all aspect. Although the Intergovernmental Panel on Climate Change (IPCC), as well as the ISO organization both have provided some tools for GHG inventory, there still are some zoom for improvement. For example, the emission factor for GHG emission from a incineration process fed with garbage with unstable composition is vulnerably affected by the random sampling scheme. As the carbon reduction pressure arises globally due to the approcling date of Post-Kotyo protocol phase, it is highly encouraged to improve the prediction quality of CO2 inventory for a garbage incineration plants since over 55% of the domestic waste are burned in Taiwan today and it is the motive of this study. The study aims to develop a better CO2 estimation method than the currently used ones, as well as to develop suitabe GHG management indicators for the domestic waste incineration plants. All the field data were collected from two incineration plants located closely in Taipei County, both designed with similar incineration process. parameter cases. These methods include:(1)emission factor approach based on random stack sampling data, (2)exhaust gas flow times CO2 based on both random and continuous measurements, (3)ACC predicted LHV value approach based on continuous measurements, (4)carbon content approach based on random sampling data, (5)heat balance approach based on continuous measurements, (6)IPCC emission factor approach based on ACC predicted LHV values, (7)PCC emission factor approach based on random sampling LHV values. The heat balance approach, as well as several CO2 management indicators, was suggested as the best CO2 emission prediction method after some carefully comparison procedures were performed. Several GHG operating parameters were suggested as the suitable GHG management indicators after tested with the consistency hypothesis.