气固流化床以其高效、可灵活操作等优点广泛应用于煤化工、煤燃烧和煤炭分选等领域。最小流化速度作为气固流化床最主要的操作参数之一，与流化床的操作设计紧密相关。现有的最小流化速度预测模型大多为经验或半经验公式，其准确性和便捷性还存在较大问题。为准确预测气固流化床最小流化速度，基于机器学习建立气固流化床最小流化速度预测模型，并探索模型的内部信息。从颗粒性质与设备条件等方面综合考虑，研究气固流化床的最小流化速度，以系统评估对最小流化速度的综合影响。采用随机森林模型验证了其预测最小流化速度的可行性，并考察了设备参数、颗粒密度和颗粒粒度3个影响因素在预测最小流化速度时的相对重要性。结果表明，最小流化速度与颗粒粒径、颗粒密度和床体直径均呈正相关，皮尔逊相关系数分别为0.79、0.31、0.14，颗粒粒径与最小流化速度相关性最强。随机森林能够根据颗粒性质(密度、粒度)与床体直径准确预测最小流化速度，模型的决定系数达到0.875。特征相关性分析揭示了各特征因素对目标变量的影响方式，颗粒粒度与最小流化速度相关性最强，为预测气固流化床最小流化速度提供借鉴。

Gas-solid fluidized bed is widely used in coal chemical industry，coal combustion，coal separation and other fields due to its high efficiency，flexible operation and other advantages. As one of the most important operating parameters of gas-solid fluidized bed，the minimum fluidization velocity is closely related to the operation design of fluidized bed. Most of the existing models for predicting the minimum fluidization velocity are empirical or semi-empirical formulae，and their accuracy and convenience are still insufficient. In order to accurately predict the minimum fluidization velocity of gas-solid fluidized bed,a prediction model of the minimum fluidization velocity in gas-solid fluidized bed was established based on machine learning，and the internal information behind the model was explored. The minimum fluidization velocity of gas-solid fluidized bed was studied from the aspects of particle properties and equipment conditions. The comprehensive influence on the minimum fluidization velocity was systematically evaluated. The feasibility of predicting the minimum fluidization velocity was verified by using the random forest model，and the relative importance of equipment parameters，particle density and particle size in predicting the minimum fluidization velocity was investigated. The results show that the minimum fluidization velocity is positively correlated with particle size，particle density and bed diameter. The Pearson correlation coefficients are 0.79，0.31 and 0.14，respectively. The particle size has the strongest correlation with the minimum fluidization velocity. Random forest can accurately predict the minimum fluidization velocity according to the particle properties (density，particle size) and the bed diameter，and the determination coefficient of the model is up to 0.875. The characteristic correlation analysis reveals the influence of each characteristic factor on the target variable. The correlation between particle size and minimum fluidization velocity is the strongest，which provides a new idea for predicting the minimum fluidization velocity of gas-solid fluidized bed.

0 引言

1 研究方法

   1.1 数据的收集

   1.2 随机森林回归和验证

2 结果和讨论

   2.1 特征变量统计结果和任意2个变量线性关系

   2.2 最小流化速度的随机森林模型性能评估

   2.3 最小流化速度预测部分相关图

3 结论