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The Determination of Optimal Excess Air Coefficient Based on Data Mining in Power Plant

机译:基于数据挖掘的电厂最优空气过剩系数的确定

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Coal-fired boiler combustion system in power plant is a complex multi-input and multi-output plant with strong nonlinear and large time-delay. The determination of the optimal excess air coefficient is very important for economical analysis and operation optimization and it is a difficulty and bottleneck for operation optimization in power plants. Based on the association characteristic in electric industrial data, this paper proposes the operation optimization based on data mining in power plant. The improved fuzzy association rule mining algorithm is proposed and introduced to find the operation optimization values to guide the operation in power plant. Based on the actual history data in 300MW unit, the optimization values in typical load ranges are found out by data mining to provide better guidance. Experiment results show that the operation optimization value determined by the improved fuzzy association rule mining algorithm can improve the efficiency and can be used to guide the operation online.
机译:电厂的燃煤锅炉燃烧系统是一个复杂的多输入多输出设备,具有很强的非线性和较大的时延。最优过剩空气系数的确定对于经济分析和运行优化非常重要,并且是电厂运行优化的困难和瓶颈。基于电力工业数据的关联特征,提出了基于数据挖掘的电厂运行优化方法。提出并提出了改进的模糊关联规则挖掘算法,以寻找运行优化值,指导电厂运行。根据300MW机组的实际历史数据,通过数据挖掘找出典型负荷范围内的优化值,以提供更好的指导。实验结果表明,改进的模糊关联规则挖掘算法确定的操作优化值可以提高效率,可用于在线指导操作。

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