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对燃煤锅炉结渣特性建模预测并结合优化算法实现燃烧优化是降低锅炉结渣几率有效的方法。文中将煤的软化温度tST、硅铝比w(SiO2)/w(Al2O3)、碱酸比J、硅比G以及锅炉的无因次炉膛平均温度φt、无因次切圆直径φd等作为输入变量,以结渣程度作为输出,建立最小二乘支持向量回归机燃煤锅炉结渣预测模型。同时采用显微镜原理对惩罚参数γ和核参数σ进行寻优,快速有效地获得二者的最优组合。通过对5台锅炉结渣特性进行预测评判,结果表明此方法是合理可行的。同时依据本方法及面向对象的高级语言,开发了相应的预测评判系统。
Modeling and predicting the slagging characteristics of a coal-fired boiler combined with the optimization algorithm to achieve combustion optimization is an effective way to reduce the probability of slagging in the boiler. In this paper, the softening temperature of coal tST, the ratio of silicon to aluminum w (SiO2) / w (Al2O3), alkalinity ratio J, silicon ratio G and the furnace dimensionless furnace average temperature φt, dimensionless tangent diameter φd as input Variables, with the degree of slagging as output, the least squares support vector regression model of coal-fired boiler slagging is established. At the same time, the principle of microscope is used to optimize the penalty parameter γ and the nuclear parameter σ so that the optimal combination of the two can be obtained quickly and effectively. By predicting the slagging characteristics of 5 boilers, the results show that this method is reasonable and feasible. At the same time, according to the method and object-oriented high-level language, the corresponding predictive and judgment system has been developed.