Sludge bulking monitoring in industrial wastewater treatment plants through graphical methods: A dynamic graph embedding and Bayesian networks approach.

Sludge bulking is a prevalent issue in wastewater treatment plants (WWTPs) that negatively impacts effluent quality by hindering the normal functioning of treatment processes. To tackle this problem, we propose a novel graph-based monitoring framework that employs advanced graph-based techniques to detect and diagnose sludge bulking events. The proposed framework utilizes historical datasets under normal operating conditions to extract pertinent features and causal relationships between process variables. This enables operators to trigger alarms and diagnose the root cause of the bulking event. Sludge bulking detection is carried out using the dynamic graph embedding (DGE) method, which identifies similarities among process variables in both temporal and neighborhood dependencies. Consequently, the dynamic Bayesian network (DBN) computes the prior and posterior probabilities of a belief, updated at each time step. Variations in these probabilities indicate the potential root cause of the sludge bulking event. The results demonstrate that the DGE outperforms other linear and non-linear feature extraction methods, achieving a detection rate of 99%, zero false alarms, and less than one percent incorrect detections. Additionally, the DBN-based diagnostic method accurately identified the majority of sludge bulking root causes, primarily those resulting from sudden drops in COD concentration, with an accuracy of 98% an improvement of 11% over state-of-the-art techniques.

Dynamic calibration of process-wide partial-nitritation modeling with airlift granular for nitrogen removal in a full-scale wastewater treatment plant.

A main challenge in rapid nitrogen removal from rejected water in wastewater treatment plants (WWTPs) is growth of biomass by nitrite-oxidizing bacteria (NOB) and ammonia-oxidizing bacteria (AOB). In this study, partial nitritation (PN) coupled with air-lift granular unit (AGU) technology was applied to enhance nitrogen-removal efficiency in WWTPs. For successful PN process at high-nitrogen-influent conditions, a pH of 7.5-8 for high free-ammonia concentrations and AOB for growth of total bacterial populations are required. The PN process in a sequential batch reactor (SBR) with AGU was modeled as an activated sludge model (ASM), and dynamic calibration using full-scale plant data was performed to enhance aeration in the reactor and improve the nitrite-to-ammonia ratio in the PN effluent. In steady-state and dynamic calibrations, the measured and modeled values of the output were in close agreement. Sensitivity analysis revealed that the kinetic and stoichiometric parameters are associated with growth and decay of heterotrophs, AOB, and NOB microorganisms. Overall, 80% of the calibrated data fit the measured data. Stage 1 of the dynamic calibration showed NO2 and NO3 values close to 240 mg/L and 100 mg/L, respectively. Stage 2 showed NH4 values of 200 mg/L at day 30 with the calibrated effluent NO2 and NO3 value of 250 mg/L. In stage 3, effluent NH4 concentration was 200 mg/L at day 60.