Neural network model for predicting the biomethane yield in an anaerobic digester using biomass composition profiles

The production of biomethane or renewable natural gas from anaerobic digestion (AD) of agriculture and agroindustry wastes has gained increasing interest towards decarbonisation of fuels and energy supply. Methane yield is a critical step for assessing biomethane projects. In this work, an artificial neural network (ANN) model was developed to predict the methane yield using a collected dataset of 340 experimental data. The input pa-rameters to the network were biomass composition in terms of total solids (TS), volatile solids (VS), lipids, protein, and lignin. The root mean square error (RMSE) of the model was 0.031 L CH4/g VS, with a mean ab-solute percentage error of 9.2%. The performance of the ANN models was validated using additional mono-and co-digestion experimental datasets. ANN-prediction power was also compared to the outputs of five multiple linear regression models from the literature. Results clearly demonstrated that, compared to the conventional multiple regression models, the proposed ANN-based model has a superior predictive power with lower RMSE values and lower prediction error in most cases. As such, the model is useful for preliminary stages of process design and evaluation of AD-based bioenergy projects. The model has the advantage of requiring only the analysis for five composition values and it has been implemented in a Python function which can also facilitate its wide application.

Automated summary

In this study, an artificial neural network (ANN) model was developed to predict methane yield using a collected dataset of 340 experimental data. The model's input parameters were biomass composition in terms of total solids (TS), volatile solids (VS), lipids, protein, and lignin. The results showed that the ANN-based model had a superior predictive power compared to conventional multiple regression models, with lower RMSE values and lower prediction errors in most cases. Therefore, the model is useful for preliminary stages of process design and evaluation of AD-based bioenergy projects.