Application of machine learning methods to understand and predict circulating fluidized bed riser flow characteristics

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.