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.

这项研究提出了一种新方法，可以对从厌氧消化处理厂获得的沼气产量进行建模，并提高了准确性。该方法基于人工神经网络 (ANN)，更具体地说，基于径向基函数 (RBF) 网络的高效架构。提出了一种基于非对称模糊均值 (NSFM) 算法的新型 RBF 训练方案，与其他 ANN 方法相比，该算法已被证明可提供更高的准确性，但无法有效处理大量输入变量。由于沼气生产建模就是这种情况，该算法通过基于差分进化 (DE) 的优化器进行了增强，这有助于正确调整算法，最终提高生成模型的准确性。所提出的方法应用于模拟现实世界中的沼气生产，全面运营的污水处理厂。一项比较研究表明，与不同的机器学习方法相比，所提出的模型具有优越性。