ABSTRACT
In the current study, artificial neural network (ANN) and modified Gompertz equation (MG) were applied to develop integrated based models for the prediction of cumulative biogas and methane yield (CBY and CMY) from anaerobic digestion (AD) of several organic wastes. Volatile solid to total solid ratio (VS/TS), carbon content (C), carbon-to-nitrogen ratio (C/N) and digestion time (DT) were selected as input features for the implementation of ANN approach. Genetic algorithm (GA) was employed in order to optimize the ANN architecture as well as the kinetic parameters of the MG to provide reliable and fast learning for better prediction performance. To evaluate model performances, determination coefficient (R2) and root mean square error (RMSE) were used. Both the approaches performed well in predicting CBY and CMY and showed a good agreement with the experimental data. However, GA-ANN models exhibit smaller deviation and higher predictive accuracy with satisfactory RMSE and R2 of about 0.0045 and 0.9996 for CBY, and 0.0046 and 0.9998 for CMY, compared with GA-MG models. This evinces the effectiveness of the developed approach to forecast CBY and CMY and can be an effective tool for the scale up of anaerobic digestion units and technico-economic studies.
Nomenclature
Declaration of interests
We declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
We would like to thank to the DGRSDT of the Algerian Ministry of Higher Education and Scientific Research for supporting this work.