Integrated data-driven strategy to optimize the processes configuration for full-scale wastewater treatment plant predesign

Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods

The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features. The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination (R2) increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification. During TEC prediction, R2 increased by 0.068 and 0.042, reaching 0.884, and the RMSE decreased by 232.444 and 197.065 kWh, reaching 1305.829 kWh, respectively.

Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion

Microplastics as emerging pollutants have been heavily accumulated in the waste activated sludge (WAS) during biological wastewater treatment, which showed significantly diverse impacts on the subsequent anaerobic sludge digestion for methane production. However, a robust modeling approach for predicting and unveiling the complex effects of accumulated microplastics within WAS on methane production is still missing. In this study, four automated machine learning (AutoML) approach was applied to model the effects of microplastics on anaerobic digestion processes, and integrated explainable analysis was explored to reveal the relationships between key variables (e.g., concentration, type, and size of microplastics) and methane production. The results showed that the gradient boosting machine had better prediction performance (mean squared error (MSE) = 17.0) than common neural networks models (MSE = 58.0), demonstrating that the AutoML algorithms succeeded in predicting the methane production and could select the best machine learning model without human intervention. Explainable analysis results indicated that the variable of microplastic types was more important than the variable of microplastic diameter and concentration. The existence of polystyrene was associated with higher methane production, whereas increasing microplastic diameter and concentration both inhibited methane production. This work also provided a novel modeling approach for comprehensively understanding the complex effects of microplastics on methane production, which revealed the dependence relationships between methane production and key variables and may be served as a reference for optimizing operational adjustments in anaerobic digestion processes.

The response of sediment microbial communities to temporal and site-specific variations of pollution in interconnected aquaculture pond and ditch systems

Sediment microbial communities play critical roles in the health of fish and the biogeochemical cycling of elements in aquaculture ecosystems. However, the response of microbial communities to temporal and spatial variations in interconnected aquaculture pond and ditch systems remains unclear. In this study, 61 sediment bacterial samples were collected over one year from 11 sites (including five ponds and six ditches) in a 30-year-old fish aquaculture farm. The 16S rRNA approach was used to determine the relative abundances of microbial communities in the sediment samples. The relationships among nutrients, heavy metals, and abundant microorganisms were analyzed. Our results showed that Proteobacteria, Bacteroides and Chloroflexi were the predominant phyla in the sediments of aquaculture pond, with average abundances of 36.33%, 18.60%, and 14.58%, respectively. The microbial diversity in aquaculture sediments was negatively correlated (P < 0.05) with the concentrations of total nitrogen and total phosphorus in sediments, indicating that the microbial diversity is highly associated with the remediation of nutrients in sediments. The sediment samples with high similarities were discovered by the t-distributed stochastic neighbor embedding (t-SNE) method. The site-specific correlations between specific microorganisms and heavy metals were explored. The network analysis revealed that the microbial diversities in aquaculture ponds were more stable than that in aquaculture ditches. The network analysis also illustrated that the microbial genera with low relative abundances may become key groups of microbial communities in sediment ecosystems. Our work deepens the understanding of the relationships between microbial communities and the spatiotemporal characteristics of surface water and sediments in aquaculture farms.

Exploring the feasibility of nitrous oxide reduction and polyhydroxyalkanoates production simultaneously by mixed microbial cultures

Nitrous oxide (N₂O), as a powerful greenhouse gas, has drawn increasing attention in recent years and different strategies for N₂O reduction were explored. In this study, a novel strategy for valuable polyhydroxyalkanoates (PHA) production coupling with N₂O reduction by mixed microbial cultures (MMC) using different substrates was evaluated. Results revealed that N₂O was an effective electron acceptor for PHA production. The highest PHA yield (0.35 Cmmol PHA/Cmmol S) and PHA synthesis rate (227.47 mg PHA/L/h) were obtained with acetic acid as substrate. Low temperature (15℃) and pH of 8.0 were beneficial for PHA accumulation. Results of the thermogravimetric analysis showed that PHA produced with N₂O as electron acceptor has better thermal stability (melting temperature of 99.4℃ and loss 5% weight temperature of 211.4℃). Our work opens up new avenues for simultaneously N₂O reduction and valuable bioplastic production, which is conducive to resource recovery and climate protection.