Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach

Abstract

Biomass gasification is one of the primary thermal conversion processes where fluidized bed reactors are often used to produce syngas with low heating values. However, there has not yet been an effective model to predict gasification yield with broad applicability. In this study, machine learning was adopted to realize the prediction of syngas compositions and lower heating values (LHV) using various lignocellulosic biomass feedstocks at a wide range of operating conditions. Three machine learning techniques, i.e., Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were adopted after determining hyperparameters optimization. Pearson correlation and permutation importance were used for the sensitivity analysis. RF and ANN were found to have high prediction accuracy with R2 and RMSE results (RF: R2=0.809-0.946, RMSE=1.39-11.54%; ANN: R2=0.565-0.924, RMSE=1.46-10.56%). Monte Carlo filtering (MCF) was integrated into the three machine learning algorithms to forecast the desired products by predicting the important features of the operating conditions and biomass characteristics. Considering the desired H2/CO > 1.1 and LHV > 5.86 MJ/m3, the RF-MCF was a more suitable approach with R2=0.791-0.902 for H2, CO and LHV features. The machine learning approach can be widely adapted in various scenarios predicting output features as well as MCF for finding the significant variables for optimization.