Calculation Methods On Carbon Dioxide Emission Of Chinese Coal-fired Power Plants

Climate Change has become the important issues for the cooperative games of politics, economics and culture around the world. Environmental problems followed by the Greenhouse effect have gradully got people’s attection. In order to comprehensive controlling the emission of carbon dioxide and other greenhouse gases (GHG), to mitigate the negative impact of climate warming on human economic and society, international countries has started to take action as setting emission cap to binding emissions and reducing emissions, together efforts on Climate Change.The primary step on emission reduction is getting through understanding of the current situation of carbon dioxide. Electric power industry is a major emission source of carbon dioxide in China. Although there are lots of surveys on carbon ermissions calculation methods of coal-fired power plants in the world, most parts of the methods are designed by the coal statistics and from other countries. While, there are wide varieties of coal and wide range on coal property to choose in China. Related electrical statictical data is far from perfect and big difference is existed between power equipment running status in China and that in other countries. Directly and mechanically copy and apply of the existing methods is bound to get big error from the true value. Above all, the establish of calculation methods on carbon dioxide emission of China’s coal-fired power plant needs refering to the fully fledged methodology in the world and considering China’s national conditions of coal-fired power plant.This article developed a deeply survey on the quality of coal and the effects on the electric generating in China. Firstly, from the analysis of the distributed situation and quality characteristic of coal, it can be found the coal resources are unevenly distributed and large differences of coal qualities are existed among regions. Moreover, the difference of the coal index data, as ash, sulfur and volatile contents, of china and other countries is significant. In addition, the source distribution is extremely uncoordinated with the consume distribution. The coal resources in the regions with higher demands as Jiangsu, Zhejiang and Shandong are barren, resulting in the supply of electricity-coal limiting the quality assurance. Eternally resulting that the quality of coal have a big fluctuations and mostly don’t meet the coal request of plants. At the same time, we also chooses10typical plants as the main research objects. Respectively from the calorific value, ash content, sulfur content and moisture content, analyzing the influence of coal on generating equipments. Hence, the default coefficients in IPCC shouldn’t be mechanically applied in the calculation of carbon dioxide emission of Chinese electric power industry.In order to establish a more accurate calculating method of carbon dioxide emission of coal-fired power plants, this article fully combined the equipment operating theory of power plants. Forecasting the carbon content as received basis Car by industry analysis data as total moisture Mar, ash content as received basis Aar, volatile as received basis Var and fixed carbon as received basis FCar. According to the theory of boiler combustion, formulating the calculating methods of generating process and desulfurization.In the process of forecasting Car by industry analysis data, we take advantage of the nonlinear mapping characteristic of BP neural network (BPNN). Establish the neural network model to forecast Car by by industry analysis data as total moisture Mar, ash content as received basis Aar, volatile as received basis Var and fixed carbon as received basis FCar by Matlab. Ultimately, we reached the goal that the relative absolute value of an error of predicted value of trained data is0.602%and the relative absolute value of an error of predicted value of untrained data is2.827%by net learning and optimizing.In the end of this paper, we take the example of a power plant in Jiangsu to verify the accuracy of this calculation method. The relative error of average value of forecasting data Car received by BP neural network can reduce to0.24%. The total carbon dioxide emission of stationary source of this power plant is4.923×106t/n by the calculating method established in this article. Serve as a contrast, the emission data received by the default factor provided by "2006IPCC Guide" is5.244×106t/n, higher than the actual power plant carbon dioxide emission by6.5percentage points.