Impact of Uneven Flow Wastewater Distribution on the Technological Efficiency of a Sequencing Batch Reactor

Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning

Precisely predicting the concentration of nitrogen-based pollutants from the wastewater treatment plants (WWTPs) remains a challenging yet crucial task for optimizing operational adjustments in WWTPs. In this study, an integrated approach using factor analysis (FA) and machine learning (ML) models was employed to accurately predict effluent total nitrogen (Ntoteff) and nitrate nitrogen (NO3-Neff) concentrations of the WWTP. The input values for the ML models were honed through FA to optimize factors, thereby significantly enhancing the ML prediction accuracy. The prediction model achieved a highest coefficient of determination (R2) of 97.43 % (Ntoteff) and 99.38 % (NO3-Neff), demonstrating satisfactory generalization ability for predictions up to three days ahead (R2 >80 %). Moreover, the interpretability analysis identified that the denitrification factor, the pollutant load factor, and the meteorological factor were significant. The model framework proposed in this study provides a valuable reference for optimizing the operation and management of wastewater treatment.

Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge

Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a "gas fingerprint", which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.

Achieving Discharge Limits in Single-Stage Domestic Wastewater Treatment by Combining Urban Waste Sources and Phototrophic Mixed Cultures

This work shows the potential of a new way of co-treatment of domestic wastewater (DWW) and a liquid stream coming from the thermal hydrolysis of the organic fraction of municipal solid waste (OFMSW) mediated by a mixed culture of purple phototrophic bacteria (PPB) capable of assimilating carbon and nutrients from the medium. The biological system is an open single-step process operated under microaerophilic conditions at an oxidative reduction potential (ORP) < 0 mV with a photoperiod of 12/24 h and fed during the light stage only so the results can be extrapolated to outdoor open pond operations by monitoring the ORP. The effluent mostly complies with the discharge values of the Spanish legislation in COD and p-values (<125 mg/L; <2 mg/L), respectively, and punctually on values in N (<15 mg/L). Applying an HRT of 3 d and a ratio of 100:7 (COD:N), the presence of PPB in the mixed culture surpassed 50% of 16S rRNA gene copies, removing 78% of COD, 53% of N, and 66% of P. Furthermore, by increasing the HRT to 5 d, removal efficiencies of 83% of COD, 65% of N, and 91% of P were achieved. In addition, the reactors were further operated in a membrane bioreactor, thus separating the HRT from the SRT to increase the specific loading rate. Very satisfactory removal efficiencies were achieved by applying an HRT and SRT of 2.3 and 3 d, respectively: 84% of COD, 49% of N, and 93% of P despite the low presence of PPB due to more oxidative conditions, which step-by-step re-colonized the mixed culture until reaching >20% of 16S rRNA gene copies after 49 d of operation. These results open the door to scaling up the process in open photobioreactors capable of treating urban wastewater and municipal solid waste in a single stage and under microaerophilic conditions by controlling the ORP of the system.

Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor

Small-scale distributed water treatment equipment such as sequencing batch reactor (SBR) is widely used in the field of rural domestic sewage treatment because of its advantages of rapid installation and construction, low operation cost and strong adaptability. However, due to the characteristics of non-linearity and hysteresis in SBR process, it is difficult to construct the simulation model of wastewater treatment. In this study, a methodology was developed using artificial intelligence and automatic control system that can save energy corresponding to reduce carbon emissions. The methodology leverages random forest model to determine a suitable soft sensor for the prediction of COD trends. This study uses pH and temperature sensors as premises for COD sensors. In the proposed method, data were pre-processed into 12 input variables and top 7 variables were selected as the variables of the optimized model. Cycle ended by the artificial intelligence and automatic control system instead of by fixed time control that was an uncontrolled scenario. In 12 test cases, percentage of COD removal is about 91. 075% while 24. 25% time or energy was saved from an average perspective. This proposed soft sensor selection methodology can be applied in field of rural domestic sewage treatment with advantages of time and energy saving. Time-saving results in increasing treatment capacity and energy-saving represents low carbon technology. The proposed methodology provides a framework for investigating ways to reduce costs associated with data collection by replacing costly and unreliable sensors with affordable and reliable alternatives. By adopting this approach, energy conservation can be maintained while meeting emission standards.

Pathways of Nitrogen and Phosphorus Utilization and Removal from Cyanobacteria Wastewater by Combining Constructed Wetlands with Aerobic Reactors

Due to its low C/N ratio and high concentrations of nitrogen and phosphorus, the effluent of anaerobic cyanobacteria fermentation cannot be directly discharged without further treatment. To effectively reduce nutrient loads and utilize the nutrient resources of biogas slurry generated from the anaerobic digestion of stored algae, two different aerobic treatment units (AUs) were combined with an ecological treatment unit (EU) to create two different treatment systems. The two AUEU systems paired a constructed wetland (CW) with either a cascade biological contact reactor (CBCR) or a carrousel oxidation ditch reactor (CODR). In this paper, the water quality characteristics of biogas slurry were measured, and comprehensive experiments on the two trial-treatment systems were carried out to validate their performance in removing pollutants and utilizing resources. Furthermore, the pollutant removal efficiencies of the combined systems, along with the removal mechanisms and utilization of the nitrogen and phosphorus in the CWs, were also investigated. The results showed that the CWs, with aquatic vegetation, took up the majority of removed nitrogen and phosphorus by absorption, which effectively reduced the concentration of pollutants in the effluent and enabled the nitrogen and phosphorus to be reused in plants. Biomass assimilation by the absorption by vegetation took up 75.8%, 66.1%, 70.3%, and 86% of the removed NH4+-N, NOx−-N, TN, and TP, respectively.