Development of a Predictive Model for Biogas Yield Using Artificial Neural Networks (ANNs) Approach

摘要

The modelling of anaerobic co-digestion of household food solid wastes and wastewater are complex and this is due to the rigorous processes that take place during the digestion process. The development of a predictive model that is capable of the simulation of anaerobic digester (AD) performances can go a long way in helping the operation of the AD processes and the optimization for biogas yield. The artificial neural networks (ANNs) approach is considered to be suitable and straightforward modelling method for AD process. In this research work, a multi-layer ANNs model with six input layer, ten hidden layers was trained using Lavenberg-Marquardt back propagation algorithm to simulate the digester operation and to predict the outcome of biogas yield. The performance of the developed ANNs models was validated and the results obtained from the research work reveal the effectiveness of the model to predict biogas yield with a mean squared error (MSE) of best validation performance of 5.1 x 10-4. Moreover, the anticipated artificial neural networks model has a close correlation between the outputs and the targets. The outcome of the results showed that the R values of the training set, the testing set, the validation set and the all data set were found to be high, the values being 0.97193, 0.96510, 0.98378 and 0.97229 respectively.