Applying Artificial Intelligence to Predict the Composition of Syngas Using Rice Husks: A Comparison of Artificial Neural Networks and Gradient Boosting Regression

Abstract

The purpose of this study is to utilize two artificial intelligence (AI) models to predict the syngas composition of a fixed bed updraft gasifier for the gasification of rice husks. Air and steam-air mixtures are the gasifying agents. In the present work, the feeding rate of rice husks is kept constant, while the air and steam flow rates vary in each case. The consideration of various operating conditions provides a clear comparison between air and steam-air gasification. The effects of the reactor temperature, steam-air flow rate, and the ratio of steam to biomass are investigated here. The concentrations of combustible gases such as hydrogen, carbon monoxide, and methane in syngas are increased when using the steam-air mixture. Two AI models, namely artificial neural network (ANN) and gradient boosting regression (GBR), are applied to predict the syngas compositions using the experimental data. A total of 74 sets of data are analyzed. The compositions of five gases (CO, CO2, H2, CH4, and N2) are predicted by the ANN and GBR models. The coefficients of determination (R2) range from 0.80 to 0.89 for the ANN model, while the value of R2 ranges from 0.81 to 0.93 for GBR model. In this study, the GBR model outperforms the ANNs model based on its ensemble technique that uses multiple weak learners. As a result, the GBR model is more convincing in the prediction of syngas composition than the ANN model considered in this research.