Microbial fuel cell (MFC) power performance strongly depends on the biofilm growth, which in turn is affected by the feed flow rate. In this work, an artificial neural network (ANN) approach has been used to simulate the effect of the flow rate on the power output by ceramic MFCs fed with neat human urine. To this aim, three different second-order algorithms were used to train our network and then compared in terms of prediction accuracy and convergence time: Quasi-Newton, Levenberg-Marquardt, and Conjugate Gradient. The results showed that the three training algorithms were able to accurately simulate power production. Amongst all of them, the Levenberg-Marquardt was the one that presented the highest accuracy (R = 95%) and the fastest convergence (7.8 s). These results show that ANNs are useful and reliable tools for predicting energy harvesting from ceramic-MFCs under changeable flow rate conditions, which will facilitate the practical deployment of this technology.

中文翻译：

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用于预测尿液流速对微生物燃料电池功率性能影响的人工神经网络算法的评估

微生物燃料电池 (MFC) 的功率性能在很大程度上取决于生物膜的生长，而生物膜的生长又受进料流速的影响。在这项工作中，人工神经网络 (ANN) 方法已被用于模拟流速对用纯人类尿液喂养的陶瓷 MFC 的功率输出的影响。为此，我们使用了三种不同的二阶算法来训练我们的网络，然后在预测精度和收敛时间方面进行比较：准牛顿、Levenberg-Marquardt 和共轭梯度。结果表明，三种训练算法均能准确模拟发电量。在所有这些中，Levenberg-Marquardt 是具有最高准确度（R = 95%）和最快收敛（7.8 s）的方法。