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提出了一种锅炉运行优化的系统框架,并着重对建模方法和优化算法进行了讨论。采用一种改进的在线支持向量机方法对学习样本进行处理,并与常规支持向量机模型相结合,形成了一种自适应建模方法,以适应煤质与锅炉实际运行工况的变化。对多目标优化算法进行讨论,并介绍了NSGA II遗传算法在锅炉优化中的应用。以某大型电站锅炉为对象,对本文方法进行了应用研究,所建模型的趋势分析和测试结果均表明了本文算法的正确性,所提出的优化模型以NOx为目标函数,综合考虑了锅炉运行经济性和安全性等约束条件,优化结果表明通过运行参数的优化调整可有效降低锅炉污染物的排放。
A system framework of boiler operation optimization is put forward, and the modeling method and optimization algorithm are emphatically discussed. An improved online support vector machine (SVM) method was used to process the learning samples and combined with the conventional support vector machine model to form an adaptive modeling method to adapt to the changes of coal quality and boiler operating conditions. The multi-objective optimization algorithm is discussed, and the application of NSGA II genetic algorithm in boiler optimization is introduced. Taking a large power station boiler as an example, the method of this paper is applied and studied. The trend analysis and test results of the model show the correctness of the proposed algorithm. The proposed optimization model takes NOx as the objective function and considers the boiler operation Economy and safety constraints, the optimization results show that through the optimal adjustment of operating parameters can effectively reduce boiler emissions.