Abstract

In the present study, a three-layer artificial neural network (ANN) and nonlinear regression models were developed to predict the performance of biogas production from the anaerobic hybrid reactor (AHR). Firstly, the performance of an AHR which is filled with perlite (2.38–4.36 mm) at fill rates of 1/3, 1/4 and 1/5 for the treatment of synthetic wastewater was investigated at a loading rate of 5, 7.5, 10, 12.5 and 15 kg COD m⁻³ day with 12, 24, 36 and 48 h of hydraulic retention time (HRT) under mesophilic conditions (37 ± 1 °C). In this study, experimental data were used to estimate the biogas production rate with models produced using both ANNs and nonlinear regression methods. Moreover, ten related variables, such as reactor fill ratio, influent pH, effluent pH, influent alkalinity, effluent alkalinity, organic loading rate, effluent chemical oxygen demand, effluent total suspended solids, effluent suspended solids and effluent volatile suspended solids, were selected as inputs of the model. Finally, ANN and nonlinear regression models describing the biogas production rate were developed. The R², IA, FA2, RMSE, MB for ANNs and nonlinear regression models were found to be 0.9852 and 0.9878, 0.9956 and 0.9945, 0.9973 and 0.9254, 217.4 and 332, 36 and 222, respectively. The statistical quality of ANNs and nonlinear regression models were found to be significant due to its high correlation between experimental and simulated biogas values. The ANN model generally showed greater potential in determining the relationship between input data and the biogas production rate according to statistical parameters (except R² and R). The results showed that the proposed ANNs and nonlinear regression models performed well in predicting the biogas production rate of AHR on behalf of avoiding economic and environmental sustainability problems.

Graphic abstract

中文翻译：

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人工神经网络和非线性回归模型预测厌氧混合反应器沼气产量

摘要

在本研究中，开发了三层人工神经网络（ANN）和非线性回归模型来预测厌氧混合反应器（AHR）生产沼气的性能。首先，研究了填充率分别为1 / 3、1 / 4和1/5的珍珠岩（2.38–4.36 mm）填充的AHR在处理速率为5、7.5， 10、12.5和15 kg COD m ^-3在中温条件下（37±1°C），有12、24、36和48小时的水力停留时间（HRT）。在这项研究中，通过使用人工神经网络和非线性回归方法生成的模型，利用实验数据估算了沼气的生产率。此外，选择了十个相关变量，例如反应器填充率，进水pH，出水pH，进水碱度，出水碱度，有机物负载率，出水化学需氧量，出水总悬浮固体，出水悬浮固体和出水挥发性悬浮固体。模型的输入。最后，建立了描述沼气生产率的ANN和非线性回归模型。的- [R ²人工神经网络和非线性回归模型的IA，FA，FA2，RMSE，MB分别为0.9852和0.9878、0.9956和0.9945、0.9973和0.9254、217.4和332、36和222。人工神经网络和非线性回归模型的统计质量因其在实验沼气值和模拟沼气值之间的高度相关性而被认为具有重要意义。ANN模型通常显示出更大的潜力，可根据统计参数（R ²和R除外）确定输入数据与沼气生产率之间的关系。结果表明，所提出的人工神经网络和非线性回归模型在避免经济和环境可持续性问题方面，在预测AHR的沼气产量方面表现良好。

图形摘要