中文摘要：

为提高燃烧效率及降低污染排放，基于运行数据的建模与优化是一种有效途径，但现场运行数据因传感器故障或传输失败等原因不可避免地存在缺失值，进而导致信息不完备，无法直接进行建模与优化．针对这一问题，采用一种基于时间相关性的缺失值填补算法，基于线性插值原理对平稳运行过程的缺失数据进行填补；针对非平稳运行工况，提出一种类平均值填补算法，并对其分类结果进行加权修正，进一步提高填补准确性；在此基础上，提出一种基于遗传算法的自适应加权类平均值填补方法，并在实际数据上进行测试分析。结果表明该方法具有更高的填补准确率．

英文摘要：

In order to improve combustion efficiency and reduce pollution emissions, the operation-based modeling and optimization of the data is an effective way. But the operation data often suffers value miss- ing inevitably due to sensor error, transmission error, etc, which leads to incomplete information and the data can not be directly modelled and optimized. To solve this problem, a supplement algorithm based on time correlation was used for missing value supplement, which is based on the principle of linear interpola- tion to fill the values missed during stationary operation process. As for the non-stationary operating con- ditions, the class-mean （CM） supplement method was proposed and its classification result was corrected with weighting to further improve the supplement accuracy. On this basis an adaptive weighting class- mean supplement method was presented based on genetic algorithm and this method was tested with actual operation data. The result showed that this method had higher supplement accuracy.