期刊
INTERNATIONAL JOURNAL OF GREEN ENERGY
卷 18, 期 15, 页码 1584-1596出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/15435075.2021.1914630
关键词
Modeling; biogas; artificial neural network; modified Gompertz equation; genetic algorithm
资金
- DGRSDT of the Algerian Ministry of Higher Education and Scientific Research
In this study, an integrated model using artificial neural network and modified Gompertz equation was developed to predict biogas and methane yield from anaerobic digestion of organic wastes. The GA-ANN models showed better predictive accuracy and closer fit to experimental data compared to GA-MG models, demonstrating the effectiveness of the approach in forecasting CBY and CMY.
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 (R-2) 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 R-2 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.
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