炉内结渣是影响火电机组和气化工艺可靠运行的关键因素之一，准确及时测量灰熔融温度可提高火电机组和气化炉运行的安全性和经济性。但灰熔融温度测量过程中存在诸多不确定因素，建立灰熔融温度预测方法不仅能验证试验数据可靠性，也可在一定程度上代替繁琐复杂的试验。论述了煤灰和生物质灰的组成、分类方法及异同点，综述了不同氧化物对灰熔融性的影响。阐述了经验公式、机器学习模型、多元相图这3种主要煤和生物质灰熔融温度预测方法，并分析了各类方法的优缺点和适用范围。认为经验公式更适合品种单一且数量较少的煤灰数据集，但不适用于生物质灰熔融温度预测。机器学习模型对煤灰和生物质灰预测效果优良，但建模难度更大，所需训练样本数据更多。基于相图预测灰熔融温度受限于灰熔融性测试方法，预测效果并不优于经验公式和机器学习模型，但对4种典型煤种有较好的预测精度，而生物质灰相较煤灰而言特殊样本更多，能否用于生物质灰熔融温度预测需进一步研究。今后可考虑构建K近邻回归、随机森林等解决回归问题突出的模型，扩充生物质数据库样本，提升预测模型的精度和泛化能力。

Slagging in the furnace is one of the key factors affecting the reliable operation of thermal power units and gasification process. The safety and economy of the operation of thermal power units and gasifiers can be improved by accurate and timely measurement of ash melting temperature. However, there are many uncertain factors in the process of ash melting temperature measurement. The establishment of ash melting temperature prediction method cannot only verify the reliability of test data, but also replace the complicated test to a certain extent. The composition, classification methods, similarities and differences of coal ash and biomass ash were discussed, and the effects of different oxides on ash fusibility were summarized. Three main methods for predicting the melting temperature of coal and biomass ash, including empirical formulas, machine learning models, and multivariate phase diagrams, were described, and the advantages, disadvantages, and applicability of each method were analyzed. It is considered that the empirical formula is more suitable for the coal ash data set with single variety and small quantity, but it is not suitable for the prediction of biomass ash melting point. The machine learning model has good prediction effect on coal ash and biomass ash, but it is more difficult to model, which requires more training sample data. The prediction of ash melting temperature based on phase diagram is limited by the ash fusibility test method, and the prediction effect is not better than the empirical formula and machine learning model, but it has good prediction accuracy for four typical coal types, and the biomass ash has more special samples than coal ash. Further research is needed to determine whether it can be used for the prediction of biomass ash melting temperature. In the future, it is possible to consider building K nearest neighbor regression, random forest and other more outstanding models to solve regression problems and expand biomass database samples to improve the accuracy and generalization ability of the prediction model.

0 引言

1 煤灰和生物质灰的组成分类及异同

   1.1 煤灰和生物质灰的组成

   1.2 煤灰和生物质灰的分类方法

   1.3 煤灰和生物质灰组成成分异同

2 主要氧化物对灰熔融温度的影响

3 灰熔融温度预测方法

   3.1 经验公式构建

   3.2 机器学习模型构建

   3.3 基于相图预测灰熔融温度

4 结论