Prediction of syngas yield from biomass by gasification and related application

Abstract

This research focuses on the prediction of synthesis gas generation from biomass through gasification and specifically estimates the syngas yield from rice straw from 2018 to 2020. The data of 2020 is visualized in the form of a colored world map A comprehensive literature review is conducted to explore previous studies on syngas yield models and gasification methods and the utilization of machine learning models. A machine learning model is built to calculate the prediction of the syngas total yield generated from biomass gasification. The inputs of the model include temperature, carbon content, hydrogen content, and oxygen content, with the latter three representing different types of biomasses. The output of the model is the total synthesis gas yield per kilogram of biomass. Subsequently, this model is utilized to predict the amount of syngas obtained from rice straw, which has a carbon, hydrogen, and oxygen content of 43.9%, 5.6%, and 32.1% respectively. From the model, an optimal gasification temperature of 667 degrees Celsius and a maximum syngas yield of 4.71 Nm3/kg for rice straw is obtained. Based on available data on rice straw production worldwide from 2018 to 2020, the amount of rice straw utilized for biomass gasification is estimated. The syngas yield in different regions of the world is calculated based on the maximum syngas yield and the mass of available rice straw. Outcomes of the calculation are visualized into a global map displaying the distribution of syngas yields which provides valuable insights into the potential for syngas production from rice straw in different regions.