Machine learning analysis of pressure fluctuations in a gas-solid fluidized bed

Abstract

Pressure fluctuations in a gas-solid fluidized bed involve much information of the dynamic system. To uncover the value and significance of the pressure fluctuations time series, two meaningful tasks, i.e., the fluidization regime classification and the future state prediction, are investigated using Machine Learning (ML) algorithms, including Back Propagation neural network (BP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), Radial Basis Function network (RBF), Random Forest (RF) and Support Vector Machine (SVM). Findings indicate that the six ML methods rank their performance for the classification task from high to low as: BP ∼ RF > SVM > CNN ∼ LSTM ∼ RBF. For the one-step-ahead future state prediction, the accuracy goes from high to low as: BP ∼ RBF > CNN ∼ LSTM > SVM ∼ RF, whilst for the multistep-ahead prediction the LSTM approach shows the best performance. Additionally, the sampling frequency of pressure time series has significant effects on both tasks. Overall, this study demonstrates the capability of machine learning methods for the value analysis of nonlinear time series and the better operation of gas-solid fluidization systems.