In this study, a three-layer backpropagation neural network (BPNN) model was utilised as an estimation model of biogas production from a continuous stirred microbial electrolysis cell (CSMEC) and a continuous stirred tank reactor (CSTR) treating food waste. The study focuses on the effects of several factors, such as chemical oxygen demand removal, oxidation reduction potential (ORP), volatile fatty acids (VFAs), organic loading rate (OLR), influent pH, effluent pH, and influent ammonium on biogas production. The biogas recovery target for the model was set at 75–85%. Levenberg Marquardt backpropagation algorithm was chosen as the algorithm for the model out of the seven-benchmark comparison. Determination coefficient (R²), index of agreement (IA) and fractional variance (FV) used for the exactitude of optimal BPANN model were 0.8902, 0.925 and 0.0715 in the CSMEC and 0.9414, 0.966 and 0.0484 in the CSTR, respectively. The results of this study showed a higher accuracy and dependability of BPANN in modelling and optimizing the process parameter interactions in relation to biogas production in both the CSMEC and CSTR.

在这项研究中，三层反向传播神经网络（BPNN）模型被用作连续搅拌微生物电解池（CSMEC）和连续搅拌釜反应器（CSTR）处理食物垃圾的沼气产量估算模型。这项研究集中在几个因素的影响上，例如化学需氧量去除，氧化还原电位（ORP），挥发性脂肪酸（VFA），有机负荷率（OLR），进水pH，出水pH和进水铵对沼气生产的影响。 。该模型的沼气回收目标设定为75–85％。从七基准比较中选择了Levenberg Marquardt反向传播算法作为模型的算法。测定系数（R ²），用于最佳BPANN模型准确性的一致性指数（IA）和分数方差（FV）在CSMEC中分别为0.8902、0.925和0.0715，在CSTR中分别为0.9414、0.966和0.0484。这项研究的结果表明，在CSMEC和CSTR中，BPANN在建模和优化与沼气生产相关的过程参数相互作用方面具有更高的准确性和可靠性。