Abstract Machine learning methods were applied to circulating fluidized bed (CFB) riser data. The goals were to (i) provide insights on various fluidization phenomena through determining the relative dominance of the process variables, and (ii) develop a model to provide predictive capability in the absence of first-principles understanding that remains elusive. The Random Forest results indicate radial position had the most dominant influence on local mass flux and species segregation, overall mass flux was the most dominant for local particle concentration, while no variable was particularly dominant or negligible for the local clustering characteristics. Furthermore, the Neural Network can be trained to provide good predictive capability, without any mechanistic understanding needed, if a sufficiently large dataset is used for training and if the input variables fully account for all the effects at play. This study underscores the value of machine learning methods in fluidization to advance understanding and provide adequate predictions.

中文翻译：

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机器学习方法在理解和预测循环流化床立管流动特性中的应用

摘要 将机器学习方法应用于循环流化床 (CFB) 立管数据。目标是 (i) 通过确定过程变量的相对优势来提供对各种流化现象的见解，以及 (ii) 开发一个模型，在缺乏第一性原理理解的情况下提供预测能力。随机森林结果表明径向位置对局部质量通量和物种分离的影响最大，总体质量通量对局部粒子浓度的影响最大，而没有变量对局部聚类特征特别占优势或可忽略不计。此外，可以训练神经网络以提供良好的预测能力，无需任何机械理解，如果使用足够大的数据集进行训练，并且输入变量是否完全考虑了所有的影响。这项研究强调了机器学习方法在流化中的价值，以促进理解并提供足够的预测。