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Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge

机译：初级沉淀污泥厌氧消化的自适应神经模糊模型

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Modelling of anaerobic digestion systems is difficult because their performance is complex and varies significantly with influent characteristics and operational conditions. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for modelling of anaerobic digestion system of primary sludge of Kayseri municipal WasteWater Treatment Plant (WWTP). Effluent Volatile Solid (VS) and methane yield were predicted by the ANFIS. Two stage models were performed. In the first stage, effluent VS concentration was predicted using pH, VS concentration, flowrate of pre-thickened sludge and temperature of the influent as input parameters. In the second stage, effluent VS concentration in addition to first stage input parameters were used as input parameters to predict methane yield. The low Root Mean Square Error (RMSE) and high Index of agreement (IA) values were obtained with subtractive clustering method of a first order Sugeno type inference. The model performance was evaluated with statistical parameters. According to statistical evaluations, the models satisfactorily predict effluent VS concentration and methane yield.
机译：厌氧消化系统的建模很困难，因为它们的性能很复杂，并且随着进水特性和操作条件的不同而有很大差异。在这项研究中，使用自适应神经模糊推理系统（ANFIS）对开塞利市政废水处理厂（WWTP）的主要污泥厌氧消化系统进行建模。 ANFIS预测了废水的挥发性固体（VS）和甲烷产率。进行了两个阶段的模型。在第一阶段，以pH，VS浓度，预增稠污泥的流量和进水温度作为输入参数来预测废水的VS浓度。在第二阶段，除了第一阶段的输入参数外，废水的VS浓度还用作预测甲烷产量的输入参数。通过一阶Sugeno类型推断的减法聚类方法获得了低均方根误差（RMSE）和高一致性指数（IA）值。使用统计参数评估模型性能。根据统计评估，这些模型可以令人满意地预测废水中的VS浓度和甲烷产率。

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