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

• Published in: Energy (Oxford) Vol. 241; p. 122939
• Main Authors: Abdel daiem, Mahmoud M., Hatata, Ahmed, Said, Noha
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.02.2022; Elsevier BV
• Subjects: Activated sludge; Air pollution; Algorithms; Alternative energy sources; Anaerobic Co-digestion; Anaerobic conditions; Anaerobic digestion; Anaerobic processes; Artificial neural network; Biogas; Carbon/nitrogen ratio; Correlation coefficient; Correlation coefficients; Decision making; Fossil fuels; Mixing ratio; Modelling; Neural networks; Optimization; Pollution control; Renewable energy; Root-mean-square errors; Sludge; Sludge digestion; Straw; Sustainable development; Waste management; Wheat; Wheat straw; Activated sludge; Anaerobic Co-digestion; Artificial neural network; Renewable energy; Sustainable development; Wheat straw
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.122939

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. [Display omitted] •Anaerobic co-digestion of sludge with straw enhanced the biogas production by 350%.•Methane content of the biogas produced ranged between (55–65%).•New Seagull optimization algorithm is applied to optimize the NARX neural network.•The optimal NARX neural network presents accurate results in predicting the values.