火电行业是煤炭消耗与大气污染物排放的主要部门之一，2008年，独立火电厂共消耗燃料煤13.7亿吨，占全国工业煤炭消费量的48.9%；二氧化硫排放量为1006万吨。占全国工业二氧化硫排放量的50.5%。“十二五”期间我国将进一步加大污染物总量控制力度，火电厂节能减排形势面临严峻考验。本文在分析燃煤火电厂典型工艺流程和排污环节的基础上，对火电厂资源能源消耗以及主要污染物排放的关键环节进行了识别。对于火电厂而言，能源消耗和大气污染是主要的环境问题，锅炉燃烧发电与烟气处理过程是节能减排的关键环节。为遴选火电行业节能减排先进适用技术，本文根据节能减排技术在生产系统中的功能特点，从煤炭-预处理-发电-产品（电力）-废物-回收利用-末端处理的全流程考虑，构建了火电厂节能减排技术分类体系，并对主要节能减排技术的特点、现状以及发展趋势进行了分析。在此基础上，从资源能源消耗、污染物排放、经济效益和技术性能等四个方面，建立了不同类型的火电厂节能减排技术的评估指标体系框架。针对技术指标数据多为区间值的特点，为反映技术信息的模糊性和不确定性，建立了基于区间值的模糊数学综合评价模型，有效地减少了模糊信息的遗漏。最后，以洁净煤发电技术评估为例，构建了洁净煤发电技术节能减排评价指标体系，并对各项发电技术的节能减排综合性能进行了评价。评价结果表明，在目前的技术状况下，超超临界发电技术的综合性能最好，其后依次是常压循环流化床发电技术（CFBC）、整体煤气化联合循环发电技术（IGCC）和增压流化床联合循环发电技术（PFBC-1），常规火电技术的综合性能最差，这与目前我国火力发电技术推广的整体趋势也基本相符。本文所建立的评估方法体系是火电节能减排单项技术综合评估的实用工具，所得结果反映了客观实际，可为火电行业单项节能减排先进适用技术遴选提供科学依据。火电厂生产工艺的流程性特征突出，节能减排技术之间往往以组合方式实施。为了辅助火电厂决策者优选节能减排技术组合方案，本文建立了基于区间数的火电厂节能减排技术组合多目标优选模型；结合区间数运算规则与排序方法，应用MATLAB软件实现了区间数型多目标遗传算法；最后，本文以北京市高井热电厂改扩建项目为研究实例，通过设定模型的外部参量并求解，结合电厂所在区域的相关条件与限制，最终给出了该项目在不同偏好下的节能减排技术组合推荐方案。决策者对净效益偏好较强的情况下，推荐的技术路径为“超超临界+LNB+电除尘器+湿式氨洗涤法”，或“超超临界+LNB+电除尘器+简易湿法”；若决策者更关注电厂的环境性能，则推荐方案为“超超临界+SCR+电袋复合除尘+石灰石/石灰-石膏法”；若要兼顾两方面的要求，则本研究推荐选择方案“超超临界+LNB-SNCR+电袋复合除尘+石灰石/石灰-石膏法”。结果表明，本文所构建技术组合多目标优选模型及求解算法能有效地解决火电厂节能减排技术组合优选问题，所得方案可为决策者或专家根据不同偏好确定最终方案提供科学依据。

Thermal power industry is one of the main industrial sectors for coal consumption and air pollution. In 2006, the emission of dust, SO2 and NOx of thermal power plants in Chian, respectively, was 370 million tons, 13.5 million tons and 800 tons. Furthermore, the effort to control the total amount of pollutants would be strengthened in "Twelve Five" period and the thermal power plants would face the challenges for energy-saving and emission-reducing.In this study, the key processes of energy consumption and emission were indentified, based on the analysis of typical processes of coal-fired power plants. The energy consumption and air pollution are major environmental problems, and the boiler and treatment processes of flue gas are the key processes for energy saving and emission reduction.According to the functional characteristic of technologies in the production system, this thesis built a classification system for energy-saving and emission-reduction technologies, considering the life cycle of a power plant. This thesis also analysed technology level and trends of technology improvement in China’s thermal power plants. Assessment index of energy-saving and emission-reduction technologies in thermal power plants, with regard to the consumption of resources and energy, pollutant emission, economy and technology, was established. The index of different type of technology is composed of different indicators. To reflect the ambiguity and uncertainty of technology information, the thesis established a fuzzy comprehensive evaluation model based on interval-value. This model could effectively reduce the missing of fuzzy information. Finaly, this paper evaluated some power generation technologies to judge the merits of them. The result showed that, in the current state of technology, the ultra-supercritical power generation technology performed best, followed by CFBC, IGCC and PFBC-1. The conventional thermal power technology was the worst. This is similar to the trend of the development of thermal power technologies. Therefore, the model of this thesis is a useful tool for assessment of a single energy-saving and emission-reducing technology, and the results of assessment can provide recommendations for technology choices in coal-fired power plants.To deal with the problem of choosing a best combination of technologies in energy saving and emission reduction, the paper modeled the processes of a typical thermal power plant, and established a multi-objective decision model. As some indicators have interval value, the algorithm units the method for ranking interval numbers with multi-objective genetic algorithm (MOGA). We used MATLAB to achieve the algorithm, and obtained the technology mixtures. In the end, the thesis discussed these proposals of the technology combination, and recommended some proposals according to different preferences. This can provide a scientific basis for decision-makers when they are making the design of a thermal power plant.To deal with the problem of choosing a best combination of technologies in energy saving and emission reduction, this paper modeled the processes of a typical thermal power plant, and established a multi-objective decision model. As some indicators have interval value, the algorithm units the method for ranking interval numbers with multi-objective genetic algorithm (MOGA). Finally, this paper took an expansion project of Gaojing Power Plant in Beijing for the study example. By setting the model parameters and considering the conditions and restrictions of Beijing, the model offered referral programs for the project in different preference. When the decision-makers have a strong preference on the net benefit, the recommended mixtures of technologies for this project should be USC + LNB + ESP + wet scrubber with ammonia, or USC + LNB + ESP + Simple wet desulphurization. If they are more concerned about environmental performance of power plants, the recommended mixtures should be USC + SCR + electric-bag composite dust-collector + Limestone/lime-gypsum method. Considering various factors, the study recommended USC + LNB-SNCR + electric-bag composite dust-collector + Limestone/lime-gypsum method. The results indicate that the multi-objective optimization model and the algorithm can be used to deal with the problem of optimizing the combinations of energy-saving and emission-reduction technologies.