Modeling nitrogen removal performance based on novel microbial activity indicators in WWTP by machine learning and biological interpretation.

Modeling the pollutant removal performance of wastewater treatment plants (WWTPs) plays a crucial role in regulating their operation, mitigating effluent anomalies and reducing operating costs. Pollutants removal in WWTPs is closely related to microbial activity. However, there is extremely limited knowledge on the models accurately characterizing pollutants removal performance by microbial activity indicators. This study proposed a novel specific oxygen uptake rate (SOURATP) with adenosine triphosphate (ATP) as biomass. Firstly, it was found that SOURATP and total nitrogen (TN) removal rate showed similar fluctuated trends, and their correlation was stronger than that of TN removal rate and common SOURMLSS with mixed liquor suspended solids (MLSS) as biomass. Then, support vector regressor (SVR), K-nearest neighbor regressor (KNR), linear regressor (LR), and random forest (RF) models were developed to predict TN removal rate only with microbial activity as features. Models utilizing the novel SOURATP resulted in better performance than those based on SOURMLSS. A model fusion (MF) algorithm based on the above four models was proposed to enhance the accuracy with lower root mean square error (RMSE) of 2.25 mg/L/h and explained 75% of the variation in the test data with SOURATP as features as opposed to other base learners. Furthermore, the interpretation of predictive results was explored through microbial community structure and metabolic pathway. Strong correlations were found between SOURATP and the proportion of nitrifiers in aerobic pool, as well as between heterotrophic bacteria respiratory activity (SOURATP_HB) and the proportion of denitrifies in anoxic pool. SOURATP also displayed consistent positive responses with most key enzymes in Embden-Meyerhof-Parnas pathway (EMP), tricarboxylic acid cycle (TCA) and oxidative phosphorylation cycle. In this study, SOURATP provides a reliable indication of the composition and metabolic activity of nitrogen removal bacteria, revealing the potential reasons underlying the accurate predictive result of nitrogen removal rates based on novel microbial activity indicators. This study offers new insights for the prediction and further optimization operation of WWTPs from the perspective of microbial activity regulation.

Lighting the way to sustainable development: Physiological response and light control strategy in microalgae-based wastewater treatment under illumination.

The Sustainable Development Goals link pollutant control with carbon dioxide reduction. Toward the goal of pollutant and carbon reduction, microalgae-based wastewater treatment (MBWT), which can simultaneously remove pollutants and convert carbon dioxide into biomass with value-added metabolites, has attracted considerable attention. The photosynthetic organism microalgae and the photobioreactor are the functional body and the operational carrier of the MBWT system, respectively; thus, light conditions profoundly influence its performance. Therefore, this review takes the general rules of how light influences the performance of MBWT systems as a starting point to elaborate the light-influenced mechanisms in microalgae and the light control strategies for photobioreactors from the inside out. Wavelength, light intensity and photoperiod solely or interactively affect biomass accumulation, pollutant removal, and value-added metabolite production in MBWT. Physiological processes, including photosynthesis, photooxidative damage, light-regulated gene expression, and nutrient uptake, essentially explain the performance influence of MBWT and are instructive for specific microalgal strain improvement strategies. In addition, light causes unique reactions in MBWT systems as it interacts with components such as photooxidative damage enhancers present in types of wastewater. In order to provide guidance for photobioreactor design and light control in a large-scale MBWT system, wavelength transformation, light transmission, light source distribution, and light-dark cycle should be considered in addition to adjusting the light source characteristics. Finally, based on current research vacancies and challenges, future research orientation should focus on the improvement of microalgae and photobioreactor, as well as the integration of both.

Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration.

Wastewater treatment based on the activated sludge process is complex process, which is easily affected by influent quality, aeration time and other factors, leading to unstable effluent. Facing increasingly stringent sewage discharge standards in China, it is necessary to build a prediction model for early warning of effluent quality. In this study, nine machine learning algorithms were adopted to construct models for the prediction of effluent Chemical Oxygen Demand (COD). In order to improve the prediction accuracy of the models, model optimization was conducted by introducing the hysteresis condition [Hydraulic Retention Time (HRT) of 18 h], data processing method (K-FOLD) and process parameters [dissolved oxygen (DO), sludge return ratio (SRR) and mixed liquid suspended solids (MLSS)]. Results showed that both K-Nearest Neighbour (KNN) and Gradient Boosting Decision Tree (GBDT) displayed excellent prediction effects, the best results of MAPE, RMSE and R2 were 7.34%/1.29/0.92(COD, KNN). The optimized models were further applied to the prediction of effluent total phosphorus (TP), total nitrogen (TN) and pH. The MAPE/RMSE/R2 were 7.43%/0.92/0.93(TN, GBDT), 17.81%/0.19/0.99(TP, KNN), 0.53%/0.16/0.99 (pH, KNN) respectively, indicating high prediction accuracy. The change and comparison of modeling conditions provide a new insight to wastewater prediction models. In addition, this study is close to the actual application scenario of WWTPs operation and management, also laying a foundation for the reverse regulation of energy saving and consumption reduction of wastewater treatment plants (WWTPs).