Model Forecasting of Hydrogen Yield and Lower Heating Value in Waste Mahua Wood Gasification with Machine Learning

Biomass is an excellent source of green energy with numerous benefits such as abundant availability, net carbon zero, and renewable nature. However, the conventional methods of biomass combustion are polluting and poor efficiency processes. Biomass gasification overcomes these challenges and provides a sustainable method for the supply of greener fuel in the form of producer gas. The producer gas can be employed as a gaseous fuel in compression ignition engines in dual-fuel systems. The biomass gasification process is a complex as well as a nonlinear process that is highly dependent on the ambient environment, type of biomass, and biomass composition as well as the gasification medium. This makes the modeling of such systems quite difficult and time-consuming. Modern machine learning (ML) techniques offer the use of experimental data as a convenient approach to modeling and forecasting such systems. In the present study, two modern and highly efficient ML techniques, random forest (RF) and AdaBoost, were employed for this purpose. The outcomes were employed with results of a baseline method, i.e., linear regression. The RF could forecast the hydrogen yield with R2 as 0.978 during model training and 0.998 during the model test phase. AdaBoost ML was close behind with R2 at 0.948 during model training and 0.842 during the model test phase. The mean squared error was as low as 0.17 and 0.181 during model training and testing, respectively. In the case of the low heating value model, during model testing, the R2 was 0.971 and RF and AdaBoost, respectively, during model training and 0.842 during the model test phase. Both ML techniques provided excellent results compared to linear regression, but RFt was the best among all three.