Guodian Jiantou Inner Mongolia Energy Investment Co. , Ltd.
Dadi Engineering Development Co. , Ltd.
School of Chemical Engineering and Technology, China University of Mining and Technology

为了预测GeldartA类加重质的膨胀特征,通过床层塌落试验,研究了床层流化过程中的气泡相和乳化相的组成与操作因素之间的关联,构建了操作气速、静止床高以及床层膨胀高度的数据集,进行了数据统计分布和相关性分析,利用具有最佳超参数的GBDT模型成功模拟了膨胀高度和影响变量之间的非线性关系,分析了特征变量的敏感性。结果表明,随着气速的逐渐增加,乳化相与气泡相的膨胀呈现先增加并略有减小的规律。对于不同的初始床高与床层膨胀高度,气泡相组成不受其变化的影响且组成比例相对稳定。操作气速的重要性得分是0.68,是膨胀高度最敏感的变量。而静止床高的重要得分仅为0.32,表明此变量对床层膨胀的影响较小。此外,由部分相关性分析可知,床层的膨胀高度对操作气速的依赖性具有一定的敏感性区间。

In order to predict the expansion characteristics of Geldart A particles, we investigated the correlation between the composition of the bubble and emulsion phases in the fluidization process and the operational factors, constructed data sets of operating gas velocity, static bed height and bed expansion height by bed collapse experiments, performed statistical distribution and correlation analysis, and then successfully simulated the nonlinear relationship between expansion height and influencing variables by using GBDT model with optimal hyperparameters; and finally, performed sensitivity analysis of the characteristic variables. The results show that, with the gradual increase of gas velocity, the expansion of emulsified phase and bubble phase shows a pattern of increasing and slightly decreasing first. For different initial bed heights and bed expansion heights, the composition of the bubble phase is not affected by their changes and the composition ratio is relatively stable. The importance score of operating air velocity is 0. 68, which is the most sensitive variable for the expansion height. Compared to the operating gas velocity, the importance score of the static bed height is 0. 32, and the influence of the static bed height on the bed expansion is smaller. In addition, from the partial correlation analysis, it can be seen that the dependence of bed expansion height on operating gas velocity has a certain sensitivity interval.

国家自然科学基金项目(52104276);大地工程开发(集团)有限公司技术开发项目(2022040047)

于大伟, 邵 明, 王 宾, 等. 基于机器学习的 Geldart A 类加重质流化床的床层膨胀特性研究 [J]. 煤炭工程, 2024, 56(2): 165-170.