| 火电行业脱硫成本模型构建及指标敏感性研究 |
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| 引用本文： | 吴春生,周颖,王超,马国霞.火电行业脱硫成本模型构建及指标敏感性研究[J].环境科学研究,2020,33(9):2219-2225. |
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| 作者姓名： | 吴春生 周颖 王超 马国霞 |
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| 作者单位： | 1.生态环境部环境规划院生态环境经济核算研究中心, 北京 100012 |
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| 基金项目： | 国家重点研发计划重点专项（No.2016YFC0208800）；国家自然科学基金项目（No.41371533） |
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| 摘 要： | 受制于生产工艺、处理技术、处理效率等多因素的影响，各行业大气污染治理成本获取较为复杂，所以构建能够真实有效获取大气污染治理成本的综合模型具有很强的必要性.火电行业一直都是大气污染治理的重点，该文将其作为研究对象，基于2011—2015年的环境统计基表数据和现场调查数据，利用最小二乘非线性回归法，对不同脱硫技术和装机容量，从固定成本、变动成本和SO2去除量3个角度构建脱硫成本模型，涉及指标包括装机容量、煤的含硫量、相应煤种的发热量、SO2的脱除效率、机组运行时间、发电标煤耗以及煤炭中硫的转化率等.结果表明:①炉内脱硫法、石灰石石膏脱硫法及其他脱硫方法的拟合优度均在0.01水平下显著，3种方法的模拟成本值与实际环境统计数据接近，误差不超过300元/t，成本模型都具有一定合理性，能够很好地实现各脱硫技术和装机容量的成本预测.②从3种脱硫技术看，石灰石石膏法模型脱硫成本最高，平均值达到3 400元/t；从装机容量看，脱硫成本随装机容量的增加呈逐渐下降趋势；企业可根据自身条件进行脱硫方法和装机容量的最优化调整.③指标敏感度分析结果显示，脱硫设施的SO2脱除效率和煤炭含硫量对成本模型的影响程度较大，所以整个脱硫过程中机组脱硫效率的设定和煤炭质量的选取是脱硫耗费的关键.研究显示，炉内脱硫法、石灰石石膏脱硫法及其他脱硫方法这3种脱硫方法和0~100 MW、100~200 MW及>200 MW这3种装机容量的成本模型可使相应的火电行业进行有效的脱硫成本估计，并根据指标的敏感性进行成本控制，进而为大气污染防治政策的费用效益分析提供参考.
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| 关 键 词： | 大气污染 脱硫 燃煤 成本模型 敏感性分析 |
| 收稿时间： | 2019/8/16 0:00:00 |
| 修稿时间： | 2020/5/11 0:00:00 |
Desulphurization Cost Model and Index Sensitivity Analysis of Thermal Power Plant Sector |
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| WU Chunsheng,ZHOU Ying,WANG Chao,MA Guoxia.Desulphurization Cost Model and Index Sensitivity Analysis of Thermal Power Plant Sector[J].Research of Environmental Sciences,2020,33(9):2219-2225. |
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| Authors: | WU Chunsheng ZHOU Ying WANG Chao MA Guoxia |
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| Affiliation: | 1.The Center for Eco-Environmental Accounting, Chinese Academy of Environmental Planning, Beijing 1000122.Beijing Research & Development Center for Grasses and Environment, Beijing Academy of Agriculture and Forestry Sciences(BAAFS), Beijing 100097 |
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| Abstract: | Affected by factors such as production process, treatment technology and treatment efficiency, the cost of air pollution control is difficult to estimate. Therefore, it is necessary to develop an integrated air pollution control cost model that considers all different factors for the cost-benefit assessment. Because thermal power plant sector is the key area for air pollution control, it is adopted in this study. Based on the environmental statistics data and on-site survey data (2011-2015), the least-squares nonlinear regression method is used to establish the desulphurization cost models, which consists of three components: fixed cost, variable cost and sulfur dioxide removal. The variables also include: installment capacity, sulfur rate of the fuel, SO2 removal rate, operation time, standard coal consumption, conversion rate of sulfur in coal, etc. The results show that: (1) The cost models of each desulfurization technology (furnace desulfurization, limestone-gypsum desulfurization and other methods) have certain rationality, the goodness of fit is significant at 0.01 level, the simulated cost value of the model is close to the environmental statistics and field research data with the errors below 300 RMB/t, and the models can accurately predict the costs of each desulfurization technology. (2) From the perspective of the three desulfurization technologies, the limestone gypsum method has the highest desulfurization cost (3400 RMB/t), while from the perspective of installed capacity, the desulfurization cost gradually decreases with the increase of installed capacity, and enterprises can optimize the desulfurization method and installed capacity according to their own conditions. (3) The sensitivity analysis indicates that the SO2 removal efficiency of the desulfurization facilities and the sulfur content in coal have the greatest impact on the cost model. Therefore, the setting of the desulfurization efficiency and the selection of coal quality are important to the desulfurization cost. The cost models of the three desulfurization methods and three installed capacities can provide assistance for the rational selection of desulfurization technologies and material input in the thermal power sector, and can also be used as a reference for the policy makers to estimate the cost-benefit analysis of the air pollution control policy. |
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| Keywords: | air pollution desulfurization coal combustion cost model goodness of fit |
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