Generation of diurnal variation for influent data for dynamic simulation

Profiling wastewater characteristics in intra-urban catchments using online monitoring stations

This study aims to investigate the differences in intra-urban catchments with different characteristics through real-time wastewater monitoring. Monitoring stations were installed in three neighbourhoods of Barcelona to measure flow, total chemical oxygen demand (COD), pH, conductivity, temperature, and bisulfide (HS-) for 1 year. Typical wastewater profiles were obtained for weekdays, weekends, and holidays in the summer and winter seasons. The results reveal differences in waking up times and evening routines, commuting behaviour during weekends and holidays, and water consumption. The pollutant profiles contribute to a better understanding of pollution generation in households and catchment activities. Flows and COD correlate well at all stations, but there are differences in conductivity and HS- at the station level. The article concludes by discussing the operational experience of the monitoring stations.

Modelling the benefits of urine source separation scenarios on wastewater treatment plants within an urban water basin

Stringent discharge regulations are encouraging researchers to create innovative and sustainable wastewater treatment solutions. Urine source separation (USS) is among the potent approaches that may reduce nutrient peak loads in the influent wastewater and improve nutrient recovery. A phenomenological model was used to simulate dynamic influent properties and predict the advantages gained from implementing USS in an urban water basin. Several scenarios were investigated assuming different levels of deployment: at the entire city, or specifically in office buildings for men's urine only, or for both men and women employees. The results confirmed that all scenarios of urine source separation offered benefits at the treatment plant in terms of reducing nitrogen influent load. The economic benefits in terms of reducing energy consumption for nitrification and decreasing methanol addition for denitrification were quantified, and results confirmed environmental advantages gained from different USS scenarios. Despite larger advantages gained from a global USS rate in an entire city, implementation of a specific USS in office buildings would remain more feasible from a logistical perspective. A significant benefit in terms of reducing greenhouse gas emissions is demonstrated and this was especially due to the high level of N₂O emissions avoided in nitrifying biological aerated filter.

An essential tool for WRRF modelling: a realistic and complete influent generator for flow rate and water quality based on data-driven methods

Modelling, automation, and control are widely used for water resource recovery facility (WRRF) optimization. An influent generator (IG) is a model, aiming to provide the flowrate and pollutant concentration dynamics at the inlet of a WRRF for a range of modelling applications. In this study, a new data-driven IG model is proposed, only using routine data and weather information, and without need for any additional data collection. The model is constructed by an artificial neural network (ANN) and completed with a multivariate regression to generate time series for certain pollutants. The model is able to generate flowrate and quality data (TSS, COD, and nutrients) at different time scales and resolutions (daily or hourly), depending on various user objectives. The model performance is analyzed by a series of statistical criteria. It is shown that the model can generate a very reliable dataset for different model applications.

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

Variability in the load of pollutants significantly influences the efficiency of activated sludge technology in municipal wastewater treatment plants, both in terms of flow systems and in sequencing batch reactors (SBR). Diversified inflow of wastewater to the treatment plant has a significant impact on the technological efficiency of sequencing batch reactors. Additionally, this problem is intensified in technological systems in which there is no storage tank for raw wastewater. It is assumed, however, that the flexible operation of an SBR reactor allows it to be easily adapted to a variable load of pollutants. The aim of the article is to present the effects of uneven wastewater inflow on the operation of sequencing batch reactors using the example of the wastewater treatment plant in Rabka-Zdrój (Poland). The conducted research has shown that, in wastewater treatment plants, the use of sequencing batch reactors as an independent element of biological wastewater treatment does not always ensure a high degree of pollutant removal in the event of a very uneven wastewater inflow. Therefore, the use treated wastewater equalizing tanks is recommended, which can additionally clean residual contaminants from wastewater.

Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods

One of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at the inflow to wastewater treatment plants, the values of which depend only on the amount of inflowing wastewater. The methodology of an expert system to predict selected indicators of wastewater quality at the inflow to the treatment plant (biochemical oxygen demand, chemical oxygen demand, total suspended solids, and ammonium nitrogen) on the example of a selected WWTP—Sitkówka Nowiny, was presented. In the considered system concept, a division of the values of measured wastewater quality indices into lower (reduced values of indicators in relation to average), average (typical and most common values), and upper (increased values) were adopted. On the basis of the calculations performed, it was found that the values of the selected wastewater quality indicators can be identified with sufficient accuracy by means of the determined statistical models based on the support vector machines and boosted trees methods.

Foul sewer model development using geotagged information and smart water meter data

Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available. Within the proposed method, each sewer manhole is firstly associated with a particular population whose size is estimated from geotagged data. Subsequently, a two-stage optimization framework is developed to identify daily time-series inflows for each manhole based on physical connections between manholes and population as well as sewer sensor observations. Finally, a new uncertainty analysis method is developed by mapping the probability distributions of water consumption captured by smart meters to the stochastic variations of wastewater discharges. Two real-world FSSs are used to demonstrate the effectiveness of the proposed method. Results show that the proposed method can significantly outperform the traditional FSS model development approach in accurately simulating the values and uncertainty ranges of FSS hydraulic variables (manhole water depths and sewer flows). The proposed method is promising due to the easy availability of geotagged information as well as water consumption data from smart water meters in near future.

Passive Sampling of SARS-CoV-2 for Wastewater Surveillance

The shedding of pathogens by infected humans enables the use of sewage monitoring to conduct wastewater-based epidemiology (WBE). Although most WBE studies use data from large sewage treatment plants, timely data from smaller catchments are needed for targeted public health action. Traditional sampling methods, like autosamplers or grab sampling, are not conducive to quick ad hoc deployments and high-resolution monitoring at these smaller scales. This study develops and validates a cheap and easily deployable passive sampler unit, made from readily available consumables, with relevance to the COVID-19 pandemic but with broader use for WBE. We provide the first evidence that passive samplers can be used to detect SARS-CoV-2 in wastewater from populations with low prevalence of active COVID-19 infections (0.034 to 0.34 per 10,000), demonstrating their ability for early detection of infections at three different scales (lot, suburb, and city). A side by side evaluation of passive samplers (n = 245) and traditionally collected wastewater samples (n = 183) verified that the passive samplers were sensitive at detecting SARS-CoV-2 in wastewater. On all 33 days where we directly compared traditional and passive sampling techniques, at least one passive sampler was positive when the average SARS-CoV-2 concentration in the wastewater equaled or exceeded the quantification limit of 1.8 gene copies per mL (n = 7). Moreover, on 13 occasions where wastewater SARS-CoV-2 concentrations were less than 1.8 gene copies per mL, one or more passive samplers were positive. Finally, there was a statistically significant (p < 0.001) positive relationship between the concentrations of SARS-CoV-2 in wastewater and the levels found on the passive samplers, indicating that with further evaluation, these devices could yield semi-quantitative results in the future. Passive samplers have the potential for wide use in WBE with attractive feasibility attributes of cost, ease of deployment at small-scale locations, and continuous sampling of the wastewater. Further research will focus on the optimization of laboratory methods including elution and extraction and continued parallel deployment and evaluations in a variety of settings to inform optimal use in wastewater surveillance.

Prioritize effluent quality, operational costs or global warming? - Using predictive control of wastewater aeration for flexible management of objectives in WRRFs

This study presents a general model predictive control (MPC) algorithm for optimizing wastewater aeration in Water Resource Recovery Facilities (WRRF) under different management objectives. The flexibility of the MPC is demonstrated by controlling a WRRF under four management objectives, aiming at minimizing: (A) effluent concentrations, (B) electricity consumption, (C) total operations costs (sum electricity costs and discharge effluent tax) or (D) global warming potential (direct and indirect nitrous oxide emissions, and indirect from electricity production) . The MPC is tested with data from the alternating WRRF in Nørre Snede (Denmark) and from the Danish electricity grid. Results showed how the four control objectives resulted in important differences in aeration patterns and in the concentration dynamics over a day. Controls B and C showed similarities when looking at total costs, while similarities in global warming potential for controls A and D suggest that improving effluent quality also reduced greenhouse gasses emissions. The MPC flexibility in handling different objectives is shown by using a combined objective function, optimizing both cost and greenhouse emissions. This shows the trade-off between the two objectives, enabling the calculation of marginal costs and thus allowing WRRF operators to carefully evaluate prioritization of management objectives. The long-term MPC performance is evaluated over 51 days covering seasonal and inter-weekly variations. On a daily basis, control A was 9-30% cheaper on average compared to controls A, D and to the current rule-based control. Similarly, control D resulted on average in 35-43% lower greenhouse gasses daily emission compared to the other controls. Difference between control performance increased for days with greater inter-diurnal variations in electricity price or greenhouse emissions from electricity production, i.e. when MPC has greater possibilities for exploiting input variations. The flexibility of the proposed MPC can easily accommodate for additional control objectives, allowing WRRF operators to quickly adapt the plant operation to new management objectives and to face new performance requirements.

Integrated model predictive control of water resource recovery facilities and sewer systems in a smart grid: example of full-scale implementation in Kolding

An integrated model predictive control (MPC) strategy to control the power consumption and the effluent quality of a water resource recovery facility (WRRF) by utilizing the storage capacity from the sewer system was implemented and put into operation for a 7-day trial period. This price-based MPC reacted to electricity prices and forecasted pollutant loads 24 hours ahead. The large storage capacity available in the sewer system directly upstream from the plant was used to control the incoming loads and, indirectly, the power consumption of the WRRF during dry weather operations. The MPC balances electricity costs and treatment quality based on linear dynamical models and predictions of storage capacity and effluent concentrations. This article first shows the modelling results involved in the design of this MPC. Secondly, results from full-scale MPC operation of the WRRF are shown. The monetary savings of the MPC strategy for the specific plant were quantified around approximately 200 DKK per day when fully exploiting the allowed storage capacity. The developed MPC strategy provides a new option for linking WRRFs to smart grid electricity systems.

Assessment of aeration control strategies for biofilm-based partial nitritation/anammox systems

The objective of this work was to compare the nitrogen removal in mainstream, biofilm-based partial nitritation anammox (PN/A) systems employing (1) constant setpoint dissolved oxygen (DO) control, (2) intermittent aeration, and (3) ammonia-based aeration control (ABAC). A detailed water resource recovery facility (WRRF) model was used to study the dynamic performance of these aeration control strategies with respect to treatment performance and energy consumption. The results show that constant setpoint DO control cannot meet typical regulatory limits for total ammonia nitrogen (NH_x-N). Intermittent aeration shows improvement but requires optimisation of the aeration cycle. ABAC shows the best treatment performance with the advantages of continuous operation and over 20% lower average energy consumption as compared to intermittent aeration.

Evaluating the performance of a simple phenomenological model for online forecasting of ammonium concentrations at WWTP inlets

A simple model for online forecasting of ammonium (NH₄ ⁺) concentrations in sewer systems is proposed. The forecast model utilizes a simple representation of daily NH₄ ⁺ profiles and the dilution approach combined with information from online NH₄ ⁺ and flow sensors. The method utilizes an ensemble approach based on past observations to create model prediction bounds. The forecast model was tested against observations collected at the inlet of two wastewater treatment plants (WWTPs) over an 11-month period. NH₄ ⁺ data were collected with ion-selective sensors. The model performance evaluation focused on applications in relation to online control strategies. The results of the monitoring campaigns highlighted a high variability in daily NH₄ ⁺ profiles, stressing the importance of an uncertainty-based modelling approach. The maintenance of the NH₄ ⁺ sensors resulted in important variations of the sensor signal, affecting the evaluation of the model structure and its performance. The forecast model succeeded in providing outputs that potentially can be used for integrated control of wastewater systems. This study provides insights on full scale application of online water quality forecasting models in sewer systems. It also highlights several research gaps which - if further investigated - can lead to better forecasts and more effective real-time operations of sewer and WWTP systems.

Wastewater microbial community structure and functional traits change over short timescales

Wastewater contains microorganisms coming from various sources, e.g. feces discharges, soil infiltrations and sewer biofilms and sediments. The primary objective of this work was to determine if end-of-pipe wastewater microbial community structures exhibits short-timescale variation, and assess possible microbial origins. To this end, we measured hourly physicochemical characteristics of wastewater influent for 2 days and analyzed the microbial community at 4-h intervals using 16S rRNA gene amplicon sequencing. Results showed large variations in the microbial community composition at phylum and genus levels, i.e. Proteobacteria ranged from 44 to 63% of the total relative abundance and Arcobacter ranged from 11 to 22%. Diurnal patterns were observed in the alpha-diversity, beta-diversity and the prevalence of several taxa. Wastewater physicochemical characteristics explained 61% of the total microbial community variance by Canonical Correspondence Analysis (CCA), with flow rate being the main explanatory variable exhibiting a clear diurnal profile. Comparison with public databases using closed reference OTUs revealed that only 7.3% of the sequences were shared with human gut microbiota and 21.7% with soil microbiota, the majority being from the sewer biofilms and sediments. The functional trait, weighted average ribosomal RNA operon (rrn) copy number per genome, was found to be relatively high in the wastewater microbiota (average 3.6, soil 2.1, and human gut 2.6) and significantly correlated with flow, inferring active microbial enrichments in the sewer. The prevalence of Methylophilaceae, methanol oxidation genes and denitrification genes were related to high influent methanol and NO₃^- concentration in the influent wastewater. These functional organisms and genes indicate important carbon and nutrient removal related functions in the sewer. Together, the observed temporal patterns of the microbial community and functional traits suggest that high wastewater flow causes greater transport of active sewer microorganisms which are functionally important.

'Hot topic' - combined energy and process modeling in thermal hydrolysis systems

The thermal hydrolysis process (THP) is applied to enhance biogas production in anaerobic digestion (AD), reduce viscosity for improved mixing and dewatering and to reduce and sterilize cake solids. Large heat demands for steam production rely on dynamic effects like sludge throughput, gas availability and THP process parameters. Here, we propose a combined energy and process model suitable to describe the dynamic behaviour of THP in a full-plant context. The process model addresses interactions of THP with operational conditions covered by the AD model obeying mass continuity. Energy conservation is considered in balancing and converting various energy species dominated by thermal heat and calorific energy. The combined energy and process model was then applied on the THP at Blue Plains advanced WWTP (DC Water) to analyse the process and assess potential energy optimizations. It was found that dynamic effects like mismatched steam production and consumption, temporary gas shortages and underloaded units are responsible for energy inefficiencies with losses in electricity-production up to 29%.

Methods to reliably estimate faecal sludge quantities and qualities for the design of treatment technologies and management solutions

Sanitation access in urban areas of low-income countries is provided through unstandardized onsite technologies containing accumulated faecal sludge. The demand for infrastructure to manage faecal sludge is increasing, however, no reliable method exists to estimate total accumulated quantities and qualities (Q&Q) This proposed approach averages out complexities to estimate conditions at a centralized to semi-centralized scale required for management and treatment technology solutions, as opposed to previous approaches evaluating what happens in individual containments. Empirical data, demographic data, and questionnaires were used in Kampala, Uganda to estimate total faecal sludge accumulation in the city, resulting in 270 L/cap∙year for pit latrines and 280 L/cap∙year for septic tanks. Septic tank sludge was more dilute than pit latrine sludge, however, public toilet was not a distinguishing factor. Non-household sources of sludge represent a significant fraction of the total and have different characteristics than household-level sludge. Income level, water connection, black water only, solid waste, number of users, containment volume, emptying frequency, and truck size were predictors of sludge quality. Empirical relationships such as a COD:TS of 1.09 ± 0.56 could be used for more resource efficient sampling campaigns. Based on this approach, spatially available demographic, technical and environmental (SPA-DET) data and statistical relationships between parameters could be used to predict Q&Q of faecal sludge.

Direct dosage of reactivated carbon from waterworks into the activated sludge tank for removal of organic micropollutants

The thermal reactivation of granular activated carbon is a substantial ecological and economic benefit in the process of drinking water treatment. A significant amount of abraded carbon, which is similar to powdered activated carbon (PAC), is produced that can be brought to application at wastewater treatment plant level for the removal of micropollutants in a powdered activated carbon-activated sludge (PAC-AS) system. This excess carbon derived as a by-product from the reactivation process in a waterworks was applied directly into the activated sludge tank and has been elevated in this study by monitoring the removal efficiencies for benzotriazole, carbamazepine, diclofenac, metoprolol and sulfamethoxazole in the effluent of a semi-technical wastewater treatment plant of 39 m³. A simulation-derived sampling strategy was applied to optimize the recovery rates of the micropollutants. Flow-proportional, 72-hour composite sampling was considered best. The elimination rates obtained for a 10 g PAC·m^-3 dosage were 73% for benzotriazole, 59% for carbamazepine, 60% for diclofenac, 67% for metoprolol and 48% for sulfamethoxazole. The obtained results underline the importance of appropriate sampling strategies, which can be derived from hydraulic modeling.

Impacts of onsite greywater reuse on wastewater systems

Together with significant water savings that onsite greywater reuse (GWR) may provide, it may also affect the performance of urban sewer systems and wastewater treatment plants (WWTPs). In order to examine these effects, an integrated stochastic simulation system for GWR in urban areas was developed. The model includes stochastic generators of domestic wastewater streams and gross solids (GSs), a sewer network model which includes hydrodynamic simulation and a GS transport module, and a dynamic process model of the WWTP. The developed model was applied to a case study site in Israel. For the validation of the sewer simulator, field experiments in a real sewer segment were conducted. The paper presents the integration and implementation of these modules and depicts the results of the effects of various GWR scenarios on GS movement in sewers and on the performance of the WWTP.

A black-box model for generation of site-specific WWTP influent quality data based on plant routine data

This paper presents a simple method for the generation of continuous influent quality datasets for wastewater treatment plants (WWTPs) that is based on incomplete available routine data, only, without referring to any further measurement. In the approach, Weibull-distributed random data are fitted to the available routine data, such that the resulting distribution of influent quality data shows the identical statistical characteristics. Beside the description of the method, this paper contains a comprehensive analysis of robustness and universality of the approach. It is shown that incomplete datasets with only 10% remaining influent quality data can be filled with this method with nearly the same statistical parameters as the original data. In addition, the use with datasets of different WWTP plants sizes results always in a good agreement between original and filled datasets.

Calibration and validation of a phenomenological influent pollutant disturbance scenario generator using full-scale data

The objective of this paper is to demonstrate the full-scale feasibility of the phenomenological dynamic influent pollutant disturbance scenario generator (DIPDSG) that was originally used to create the influent data of the International Water Association (IWA) Benchmark Simulation Model No. 2 (BSM2). In this study, the influent characteristics of two large Scandinavian treatment facilities are studied for a period of two years. A step-wise procedure based on adjusting the most sensitive parameters at different time scales is followed to calibrate/validate the DIPDSG model blocks for: 1) flow rate; 2) pollutants (carbon, nitrogen); 3) temperature; and, 4) transport. Simulation results show that the model successfully describes daily/weekly and seasonal variations and the effect of rainfall and snow melting on the influent flow rate, pollutant concentrations and temperature profiles. Furthermore, additional phenomena such as size and accumulation/flush of particulates of/in the upstream catchment and sewer system are incorporated in the simulated time series. Finally, this study is complemented with: 1) the generation of additional future scenarios showing the effects of different rainfall patterns (climate change) or influent biodegradability (process uncertainty) on the generated time series; 2) a demonstration of how to reduce the cost/workload of measuring campaigns by filling the gaps due to missing data in the influent profiles; and, 3) a critical discussion of the presented results balancing model structure/calibration procedure complexity and prediction capabilities.

Monitoring and modelling to support wastewater system management in developing mega-cities

Urban drainage system models can be useful to assess and manage system performance and to plan its development. However, due to data and computational costs, sophisticated, high-resolution contemporary models of the sewer system may not be applicable. This constraint is particularly marked in developing country mega-cities where catchments can be large, data tend to be scarce, and there are many unknowns, for example regarding sources, losses and wrong connections. This paper presents work undertaken over the last 7 years to develop a suitable monitoring and modelling framework to support operation and development of the wastewater system of Bogotá (Colombia). Components of the framework covered here are: (a) the flow and water quality database, (b) a wastewater pollution load generator, and (c) a semi-distributed sewer network model, which aims at a complexity that matches the information available from the previous two components. Results from a catchment within Bogotá, area 150 km(2) and with 2.5 million inhabitants, show that the model outputs capture the scale and dynamics of the observed concentrations and loads at various points on the sewer system. However uncertainty is high because much of variability of observed dry weather flow profiles is apparently random. Against this variability, the effects of in-sewer processes were not identifiable except where backwaters caused particularly high retention times. Hence the work has resulted in an operational model with a scientifically justified, yet useful, level of complexity for Bogotá. More generally, the work demonstrates the value of monitoring and modelling programmes, including having modellers actively involved in monitoring specification and operations; and the insights into suitable level of model complexity that may be gained by uncertainty and sensitivity analysis.