Considering The Effect Of Uncertainty And Variability In The Synthetic Generation Of Influent Wastewater Time Series

The availability of influent wastewater time series is crucial for assessing the performance of a wastewater treatment plant (WWTP) under dynamic flow and loading conditions. Given the difficulty of collecting sufficient data, synthetic generation may be the only option. Usually, the main constituents of the influent time series (e.g. flow, COD, TSS, TKN) show periodic, auto-correlation, and cross-correlation structures in time. Therefore researchers have used statistical models (e.g. auto-regressive time series models) for random generation of the influent time series. However, these regular patterns in time could be significantly distorted during rain events (wet weather flow (WWF) conditions) in which the amount and frequency of rainfall affects the flow and other constituents of the influent. To tackle this problem, a hybrid of statistical and conceptual modeling techniques was adopted. The time series of rainfall and influent in DWF conditions (i.e. inputs to the conceptual model) were generated using two types of statistical models (a periodic-multivariate time series model for influent in DWF conditions and a two-state Markov chain-exponential model for rainfall). These two time series serve as inputs to a conceptual model for generation of influent time series during WWF conditions. The effect of total model uncertainty on the generated outputs was also taken into account through a Bayesian calibration and communicated to the user by constructing uncertainty bands with a desired level of confidence. The proposed influent generator is a powerful tool for realistic generation of the influent time series and is well-suited for probabilistic design of WWTPs as it considers both the effect of input variability (i.e. time variation in rainfall and influent composition during DWF) and total model uncertainty in the generation of the influent

Development, implementation, and validation of a generic nutrient recovery model (NRM) library

The reported research developed a generic nutrient recovery model (NRM) library based on detailed chemical solution speciation and reaction kinetics, with focus on fertilizer quality and quantity as model outputs. Dynamic physicochemical three-phase process models for precipitation/crystallization, stripping and acidic air scrubbing as key unit processes were developed. In addition, a compatible biological-physicochemical anaerobic digester model was built. The latter includes sulfurgenesis, biological N/P/K/S release/uptake, interactions with organics, among other relevant processes, such as precipitation, ion pairing and liquid-gas transfer. Using a systematic database reduction procedure, a 3- to 5-fold improvement of model simulation speeds was obtained as compared to using full standard thermodynamic databases. Missing components and reactions in existing standard databases were discovered. Hence, a generic nutrient recovery database was created for future applications. The models were verified and validated against a range of experimental results. Their functionality in terms of increased process understanding and optimization was demonstrated

Phosphorus release during treatment of sludge derived from a bench-scale EBPR plant

This thesis describes the development of enhanced biological phosphorus removal (EBPR) in a lab-scale sequencing batch reactor (SBR) and the release of phosphorus during the storage and thickening of sludge produced in this reactor. In the first phase of the experimental work a fast start-up method for EBPR development was established by the addition of a pure culture of Acinetobacter lwoffi to a conventional activated sludge. Investigations revealed that the performance EBPR depended on the combination of influent COD and phosphorus values and that in the investigated range, EBPR functioned independently of the sludge retention time. Low dissolved oxygen levels had no effect on the phosphorus removal properties of the sludge. The second phase of the experimental work involved the investigation of the phosphorus released during sludge handling. It was found that phosphorus resolubilisation during sludge treatment took place in three distinct phases which included an initial period of extremely low phosphorus release. Alterations of the reactor influent and operational parameters and the sludge characteristics, affected the amount of phosphorus released during anaerobic storage and gravity thickening. It was found that for short retention times in the sludge processing units (1-48 hours), decreasing the influent phosphorus concentration, increasing the oxidised nitrogen content of the excess sludge and wasting the excess sludge from the aeration tank decreased the amount of phosphorus resolubilised. For longer retention times (2-7 days), it was found that increasing the influent COD, having a lower total phosphorus sludge content, higher sludge "stabilisation" rates and quiescent conditions of storage, decreased the amount of phosphorus released.This thesis describes the development of enhanced biological phosphorus removal (EBPR) in a lab-scale sequencing batch reactor (SBR) and the release of phosphorus during the storage and thickening of sludge produced in this reactor. In the first phase of the experimental work a fast start-up method for EBPR development was established by the addition of a pure culture of Acinetobacter lwoffi to a conventional activated sludge. Investigations revealed that the performance EBPR depended on the combination of influent COD and phosphorus values and that in the investigated range, EBPR functioned independently of the sludge retention time. Low dissolved oxygen levels had no effect on the phosphorus removal properties of the sludge. The second phase of the experimental work involved the investigation of the phosphorus released during sludge handling. It was found that phosphorus resolubilisation during sludge treatment took place in three distinct phases which included an initial period of extremely low phosphorus release. Alterations of the reactor influent and operational parameters and the sludge characteristics, affected the amount of phosphorus released during anaerobic storage and gravity thickening. It was found that for short retention times in the sludge processing units (1-48 hours), decreasing the influent phosphorus concentration, increasing the oxidised nitrogen content of the excess sludge and wasting the excess sludge from the aeration tank decreased the amount of phosphorus resolubilised. For longer retention times (2-7 days), it was found that increasing the influent COD, having a lower total phosphorus sludge content, higher sludge "stabilisation" rates and quiescent conditions of storage, decreased the amount of phosphorus released

Optimizing the configuration of integrated nutrient and energy recovery treatment trains : a new application of global sensitivity analysis to the generic nutrient recovery model (NRM) library

This paper describes the use of global sensitivity analysis (GSA) for factor prioritization in nutrient recovery model (NRM) applications. The aim was to select the most important factors influencing important NRM model outputs such as biogas production, digestate composition and pH, ammonium sulfate recovery, struvite production, product purity, particle size and density, air and chemical requirements, scaling potential, among others. Factors considered for GSA involve: 1) input waste stream characteristics, 2) process operational factors, and 3) kinetic parameters incorporated in the NRMs. Linear regression analyses on Monte Carlo simulation outputs were performed, and the impact of the standardized regression coefficients on major performance indicators was evaluated. Finally, based on the results, the paper describes the original use of GSA to obtain insight in complex nutrient recovery systems and to propose an optimal nutrient and energy recovery treatment train configuration that maximizes resource recovery and minimizes energy and chemical requirements

Digital solutions for continued operation of WRRFs during pandemics and other interruptions

This paper includes survey results from 17 full-scale water resource recovery facilities (WRRFs) to explore their technical, operational, maintenance, and management-related challenges during COVID-19. Based on the survey results, limited monitoring and maintenance of instrumentation and sensors are among the critical factors during the pandemic which resulted in poor data quality in several WRRFs. Due to lockdown of cities and countries, most of the facilities observed interruptions of chemical supply frequency which impacted the treatment process involving chemical additions. Some plants observed influent flow reduction and illicit discharges from industrial wastewater which eventually affected the biological treatment processes. Delays in equipment maintenance also increased the operational and maintenance cost. Most of the plants reported that new set of personnel management rules during pandemic created difficulties in scheduling operator's shifts which directly hampered the plant operations. All the plant operators mentioned that automation, instrumentation, and sensor applications could help plant operations more efficiently while working remotely during pandemic. To handle emergency circumstances including pandemic, this paper also highlights resources and critical factors for emergency responses, preparedness, resiliency, and mitigation that can be adopted by WRRFs