Machine learning approach for determining and optimizing influential factors of biogas production from lignocellulosic biomass

Machine learning (ML) was used to predict specific methane yields (SMY) with a dataset of 14 features from lignocellulosic biomass (LB) characteristics and operating conditions of completely mixed reactors under continuous feeding mode. The random forest (RF) model was best suited for predicting SMY with a coefficient of determination (R2) of 0.85 and root mean square error (RMSE) of 0.06. Biomass compositions greatly influenced SMYs from LB, and cellulose prevailed over lignin and biomass ratio as the most important feature. Impact of LB to manure ratio was assessed to optimize biogas production with the RF model. Under typical organic loading rates (OLR), optimum LB to manure ratio of 1:1 was identified. Experimental results confirmed influential factors revealed by the RF model and provided the highest SMY of 79.2% of the predicted value. Successful applications of ML for anaerobic digestion modelling and optimization specifically for LB were revealed in this work.

Automated summary

In this study, machine learning was used to predict the specific methane yields (SMY) using 14 features from lignocellulosic biomass (LB) characteristics and operating conditions in completely mixed reactors. The random forest (RF) model performed the best with a coefficient of determination (R2) of 0.85 and root mean square error (RMSE) of 0.06. Biomass compositions, particularly cellulose, had a significant influence on SMYs from LB. The RF model was also used to assess the impact of LB to manure ratio and determine the optimum ratio of 1:1 under typical organic loading rates (OLR). Experimental results confirmed the influential factors identified by the RF model and achieved a highest SMY of 79.2% of the predicted value. This work demonstrates the successful application of machine learning for anaerobic digestion modeling and optimization specifically for LB.