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研究了燃烧优化降低NO_x排放量的方法,介绍了3类NO_x的生成机理,利用集成支持向量机建立NO_x排放预测模型,并利用粒子群算法优化NO_x排放.为了有效克服粒子群的早熟问题,提出了带系数的距离学习粒子群算法.将所提方法应用于某电厂的NO_x减排优化中,并与其他方法进行对比.结果表明:集成支持向量机可以有效提高预测结果的准确性,改进的优化算法可以使NO_x排放量更低,搜索结果也更加稳定.
The method of NO x emission reduction by combustion optimization was studied, the formation mechanism of NO x was introduced, the NO x emission prediction model was established by using integrated support vector machine, and the NO_x emission was optimized by using particle swarm optimization.In order to overcome the premature problem of particle swarm optimization The algorithm of distance learning particle swarm optimization with coefficients is applied to the NO_x emission reduction optimization of a power plant and compared with other methods.The results show that the integrated support vector machine can effectively improve the accuracy of the prediction results and improve Optimization algorithm can make NO_x emissions lower, the search results are more stable.