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In this study, two data-driven models, artificial neural networks and support vector regression (SVR), have been trained and optimized to predict the biogas yield from anaerobic digesters at the South-Tehran municipal wastewater treatment plant. The auto-tuning approach, including feature selection and hyperparameter population-based optimization, was applied through the genetic algorithm (GA) and particle swarm optimization to improve the training models' performance and help them obtain the best input parameters. The Shapley Additive Explanations (SHAP) analysis was also done to interpret models effectively and assign credit for a model's prediction to each feature. The findings demonstrated that biogas prediction using SVR-GA achieved the highest accuracy, with R2 values of 0.725 and 0.773, and RMSE values (regarding normalized datasets) of 0.477 and 0.492 for the train and test, respectively, while requiring the least computational time compared to other models. The auto-tuning technique, by removing the less important inputs, was able to show that temperature, pH, effluent and influent dry solids, effluent volatile solids (VS), and influent VS of waste sludge were the best input parameters for optimal biogas production modeling. The SHAP analysis revealed that VS eff and temperature were two of the most important features affecting biogas production, exhibiting an inverse impact. [Display omitted] [ABSTRACT FROM AUTHOR]

In this study, the ozonation impact on anaerobic digestion of wasted thickened activated sludge of Tehran wastewater treatment plant has been investigated. Regarding that, the thickened activated sludge, a final solid waste of treatment plants, was subjected to ozonation pretreatment to evaluate its characteristics. Three major sludge characteristics were considered for investigating ozonation effects on anaerobic digestion. A comparison of two different ozone doses, i.e., 0.05 and 0.1 gO 3 g−1TS was performed for short-term (10 days) and long-term (30 days) sludge retention times. Furthermore, biogas production, sludge composition, dewaterability, and energy balancing, were studied during the periods mentioned above. The results show that the lower ozone dose has an acceptable performance in a short-term operation for all parameters except energy balance which was negative in all the scenarios. Higher ozone dosage not only decreased biogas production and volatile solids reduction but also significantly increased the energy demand of the system. In addition, a longer retention time reduced the effectiveness of the ozone pretreatment during that time. Eventually, the feasibility of ozone pretreatment capacity for sustainable management of different sludge from economic and technological perspectives can be evaluated by further assessing this case study. [Display omitted] • Comparative investigation of different ozone doses on activated sludge composition. • Performance evaluation of ozonation during short and long sludge retention times. • Study of ozonation effect on biogas production and sludge dewaterability. • Assessment of ozone impact on energy production, consumption, and energy balancing. [ABSTRACT FROM AUTHOR]