Modeling and optimization of semi-continuous anaerobic co-digestion of activated sludge and wheat straw using Nonlinear Autoregressive Exogenous neural network and seagull algorithm

Anaerobic digestion of bio-wastes has a great potential to substitute fossil fuel consumption and reduce air pollution. In this study, semi-continuous anaerobic co-digestion of waste activated sludge and wheat straw using different mixing ratios under mesophilic conditions has been performed. Furthermore, modeling and optimization of anaerobic co-digestion process have been carried out using the Nonlinear Autoregressive Exogenous (NARX) neural network and Seagull optimization algorithm (SOA). The anaerobic co-digestion improved C/N ratio from 6.64 to 17.85 and enhanced the biogas production by 350% at 2% mixing ratio compared with the mono sludge digestion. Moreover, methane content of biogas ranged from 55% to 65%. The modeling results showed that the simulations of the proposed NARX neural network presented accurate results in predicting the digested sample characteristics and the biogas production. The correlation coefficient R for the data is close to 1 and the results show the stability of the optimum NARX neural network outputs for all predicted values. Moreover, it has high accuracy and effectiveness with minimum average root mean square error (RMSE) of 0.1518564. These findings can support the decision maker and stakeholders in renewable energy sector and sustainable biomass waste management. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Anaerobic co-digestion of waste activated sludge and wheat straw under mesophilic conditions improved C/N ratio and biogas production, with methane content ranging from 55% to 65%. The NARX neural network model showed high accuracy and stability in predicting sample characteristics and biogas production. These findings can benefit decision makers in renewable energy sector and sustainable biomass waste management.