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.
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Tufaner, F., Demirci, Y. Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions models. Clean Techn Environ Policy 22, 713–724 (2020). https://doi.org/10.1007/s10098-020-01816-z
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DOI: https://doi.org/10.1007/s10098-020-01816-z