Modeling biogas production from anaerobic wastewater treatment plants using radial basis function networks and differential evolution

This study presents a new method for modeling biogas production obtained from anaerobic digestion treatment plants with increased accuracy. The method is based on artificial neural networks (ANNs) and more specifically on the efficient architecture of radial basis function (RBF) networks. A novel RBF training scheme is proposed, based on the non-symmetric fuzzy means (NSFM) algorithm, which has been shown to offer increased accuracy compared to other ANN methods, but cannot handle efficiently a large number of input variables. As this is the case in biogas production modeling, the algorithm is enhanced with an optimizer based on differential evolution (DE), which helps to properly tune the algorithm, ultimately boosting the accuracy of the produced models. The proposed approach is applied for modeling the biogas production on a real world, full-scale operational wastewater treatment plant. A comparison study shows the superiority of the proposed model, compared to different machine learning approaches. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study introduces a new method for modeling biogas production from anaerobic digestion treatment plants using artificial neural networks with an efficient radial basis function architecture. The novel RBF training scheme based on the NSFM algorithm is enhanced with a DE optimizer to boost model accuracy, showing superiority over different machine learning approaches in a comparison study.