Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors.

On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. We develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.

The value of human data annotation for machine learning based anomaly detection in environmental systems.

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants.

On-site wastewater treatment plants (OSTs) are usually unattended, so failures often remain undetected and lead to prolonged periods of reduced performance. To stabilize the performance of unattended plants, soft sensors could expose faults and failures to the operator. In a previous study, we developed soft sensors and showed that soft sensors with data from unmaintained physical sensors can be as accurate as soft sensors with data from maintained ones. The monitored variables were pH and dissolved oxygen (DO), and soft sensors were used to predict nitrification performance. In the present study, we use synthetic data and monitor three plants to test these soft sensors. We find that a long solids retention time and a moderate aeration rate improve the pH soft-sensor accuracy and that the aeration regime is the main operational parameter affecting the accuracy of the DO soft sensor. We demonstrate that integrated design of monitoring and control is necessary to achieve robustness when extrapolating from one OST to another in the absence of plant-specific fine-tuning. Additionally, we provide a unique labeled dataset for further feature and data-driven soft-sensor development. Our benchmarking results indicate that it is feasible to monitor OSTs with unmaintained sensors and without plant-specific tuning of the developed soft sensors. This is expected to drastically reduce monitoring costs for OST-based sanitation systems.

Electrochemical nitrite sensing for urine nitrification.

Sensing nitrite in-situ in wastewater treatment processes could greatly simplify process control, especially during treatment of high-strength nitrogen wastewaters such as digester supernatant or, as in our case, urine. The two technologies available today, i.e. an on-line nitrite analyzer and a spectrophotometric sensor, have strong limitations such as sample preparation, cost of ownership and strong interferences. A promising alternative is the amperometric measurement of nitrite, which we assessed in this study. We investigated the sensor in a urine nitrification reactor and in ex-situ experiments. Based on theoretical calculations as well as a practical approach, we determined that the critical nitrite concentrations for nitrite oxidizing bacteria lie between 12 and 30 mgN/L at pH 6 to 6.8. Consequently, we decided that the sensor should be able to reliably measure concentrations up to 50 mgN/L, which is about double the value of the critical nitrite concentration. We found that the influences of various ambient conditions, such as temperature, pH, electric conductivity and aeration rate, in the ranges expected in urine nitrification systems, are negligible. For low nitrite concentrations, as expected in municipal wastewater treatment, the tested amperometric nitrite sensor was not sufficiently sensitive. Nevertheless, the sensor delivered reliable measurements for nitrite concentrations of 5-50 mgN/L or higher. This means that the amperometric nitrite sensor allows detection of critical nitrite concentrations without difficulty in high-strength nitrogen conversion processes, such as the nitrification of human urine.

N2O emission in full-scale wastewater treatment: Proposing a refined monitoring strategy.

Nitrous oxide (N2O) emissions from wastewater treatment contribute significantly to greenhouse gas emissions. They have been shown to exhibit a strong seasonal and daily profile in previously conducted monitoring campaigns. However, only two year-long online monitoring campaigns have been published to date. Based on three monitoring campaigns on three full-scale wastewater treatment plants (WWTPs) with different activated sludge configurations, each of which lasted at least one year, we propose a refined monitoring strategy for long-term emission monitoring with multiple flux chambers on open tanks. Our monitoring campaigns confirm that the N2O emissions exhibited a strong seasonal profile and were substantial on all three plants (1-2.4% of the total nitrogen load). These results confirm that N2O is the most important greenhouse gas emission from wastewater treatment. The temporal variation was more distinct than the spatial variation within aeration tanks. Nevertheless, multiple monitoring spots along a single lane are crucial to assess representative emission factors in flow-through systems. Sequencing batch reactor systems were shown to exhibit comparable emissions within one reactor but significant variation between parallel reactors. The results indicate that considerable emission differences between lanes are to be expected in cases of inhomogeneous loading and discontinuous feeding. For example, N2O emission could be shown to depend on the amount of treated reject water: lanes without emitted <1% of the influent load, while parallel lanes emitted around 3%. In case of inhomogeneous loading, monitoring of multiple lanes is required. Our study enables robust planning of monitoring campaigns on WWTPs with open tanks. Extensive full-scale emission monitoring campaigns are important as a basis for reliable decisions about reducing the climate impact of wastewater treatment. More specifically, such data sets help us to define general emission factors for wastewater treatment plants and to construct and critically evaluate N2O emission models.

Characterizing long-term wear and tear of ion-selective pH sensors.

Today, the development and testing of methods for fault detection and identification in wastewater treatment research relies on two important assumptions: (i) that sensor faults appear at distinct times in different sensors and (ii) that any given sensor will function near-perfectly for a significant amount of time following installation. In this work, we show that such assumptions are unrealistic, at least for sensors built around an ion-selective measurement principle. Indeed, long-term exposure of sensors to treated wastewater shows that sensors exhibit fault symptoms that appear simultaneously and with similar intensity. Consequently, this suggests that future research should be reoriented towards methods that do not rely on the assumptions mentioned above. This study also provides the first empirically validated sensor fault model for wastewater treatment simulation, which is useful for effective benchmarking of both fault detection and identification methods and advanced control strategies. Finally, we evaluate the value of redundancy for remote sensor validation in decentralized wastewater treatment systems.

Stabilizing control of a urine nitrification process in the presence of sensor drift.

Sensor drift is commonly observed across engineering disciplines, particularly in harsh media such as wastewater. In this study, a novel stabilizing controller for nitrification of high strength ammonia solutions is designed based on online signal derivatives. The controller uses the derivative of a drifting nitrite signal to determine if nitrite-oxidizing bacteria (NOB) are substrate limited or substrate inhibited. To ensure a meaningful interpretation of the derivative signal, the process is excited in a cyclic manner by repeatedly exposing the NOB to substrate-limited and substrate-inhibited conditions. The resulting control system successfully prevented nitrite accumulations for a period of 72 days in a laboratory-scale reactor. Slow disturbances in the form of feed composition changes and temperature changes were successfully handled by the controller while short-term temperature disturbances are shown to pose a challenge to the current version of this controller. Most importantly, we demonstrate that drift-tolerant control for the purpose of process stabilization can be achieved without sensor redundancy by combining deliberate input excitation, qualitative trend analysis, and coarse process knowledge.

How Urban Storm- and Wastewater Management Prepares for Emerging Opportunities and Threats: Digital Transformation, Ubiquitous Sensing, New Data Sources, and Beyond - A Horizon Scan.

Ubiquitous sensing will create many opportunities and threats for urban water management, which are only poorly understood today. To identify the most relevant trends, we conducted a horizon scan regarding how ubiquitous sensing will shape the future of urban drainage and wastewater management. Our survey of the international urban water community received an active response from both the academics and the professionals from the water industry. The analysis of the responses demonstrates that emerging topics for urban water will often involve experts from different communities, including aquatic ecologists, urban water system engineers and managers, as well as information and communications technology professionals and computer scientists. Activities in topics that are identified as novel will either require (i) cross-disciplinary training, such as importing new developments from the IT sector, or (ii) research in new areas for urban water specialists, for example, to help solve open questions in aquatic ecology. These results are, therefore, a call for interdisciplinary research beyond our own discipline. They also demonstrate that the water management community is not yet prepared for the digital transformation, where we will experience a data demand, i.e. a "pull" of urban water data into external services. The results suggest that a lot remains to be done to harvest the upcoming opportunities. Horizon scanning should be repeated on a routine basis, under the umbrella of an experienced polling organization.

Beyond signal quality: The value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors.

Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89-91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring small-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.

The future of WRRF modelling - outlook and challenges.

The wastewater industry is currently facing dramatic changes, shifting away from energy-intensive wastewater treatment towards low-energy, sustainable technologies capable of achieving energy positive operation and resource recovery. The latter will shift the focus of the wastewater industry to how one could manage and extract resources from the wastewater, as opposed to the conventional paradigm of treatment. Debatable questions arise: can the more complex models be calibrated, or will additional unknowns be introduced? After almost 30 years using well-known International Water Association (IWA) models, should the community move to other components, processes, or model structures like 'black box' models, computational fluid dynamics techniques, etc.? Can new data sources - e.g. on-line sensor data, chemical and molecular analyses, new analytical techniques, off-gas analysis - keep up with the increasing process complexity? Are different methods for data management, data reconciliation, and fault detection mature enough for coping with such a large amount of information? Are the available calibration techniques able to cope with such complex models? This paper describes the thoughts and opinions collected during the closing session of the 6th IWA/WEF Water Resource Recovery Modelling Seminar 2018. It presents a concerted and collective effort by individuals from many different sectors of the wastewater industry to offer past and present insights, as well as an outlook into the future of wastewater modelling.

Microbial residence time is a controlling parameter of the taxonomic composition and functional profile of microbial communities.

A remaining challenge within microbial ecology is to understand the determinants of richness and diversity observed in environmental microbial communities. In a range of systems, including activated sludge bioreactors, the microbial residence time (MRT) has been previously shown to shape the microbial community composition. However, the physiological and ecological mechanisms driving this influence have remained unclear. Here, this relationship is explored by analyzing an activated sludge system fed with municipal wastewater. Using a model designed in this study based on Monod-growth kinetics, longer MRTs were shown to increase the range of growth parameters that enable persistence, resulting in increased richness and diversity in the modeled community. In laboratory experiments, six sequencing batch reactors treating domestic wastewater were operated in parallel at MRTs between 1 and 15 days. The communities were characterized using both 16S ribosomal RNA and non-target messenger RNA sequencing (metatranscriptomic analysis), and model-predicted monotonic increases in richness were confirmed in both profiles. Accordingly, taxonomic Shannon diversity also increased with MRT. In contrast, the diversity in enzyme class annotations resulting from the metatranscriptomic analysis displayed a non-monotonic trend over the MRT gradient. Disproportionately high abundances of transcripts encoding for rarer enzymes occur at longer MRTs and lead to the disconnect between taxonomic and functional diversity profiles.

Biomass segregation between biofilm and flocs improves the control of nitrite-oxidizing bacteria in mainstream partial nitritation and anammox processes.

The control of nitrite-oxidizing bacteria (NOB) challenges the implementation of partial nitritation and anammox (PN/A) processes under mainstream conditions. The aim of the present study was to understand how operating conditions impact microbial competition and the control of NOB in hybrid PN/A systems, where biofilm and flocs coexist. A hybrid PN/A moving-bed biofilm reactor (MBBR; also referred to as integrated fixed film activated sludge or IFAS) was operated at 15 °C on aerobically pre-treated municipal wastewater (23 mgNH4-N L-1). Ammonium-oxidizing bacteria (AOB) and NOB were enriched primarily in the flocs, and anammox bacteria (AMX) in the biofilm. After decreasing the dissolved oxygen concentration (DO) from 1.2 to 0.17 mgO2 L-1 - with all other operating conditions unchanged - washout of NOB from the flocs was observed. The activity of the minor NOB fraction remaining in the biofilm was suppressed at low DO. As a result, low effluent NO3- concentrations (0.5 mgN L-1) were consistently achieved at aerobic nitrogen removal rates (80 mgN L-1 d-1) comparable to those of conventional treatment plants. A simple dynamic mathematical model, assuming perfect biomass segregation with AOB and NOB in the flocs and AMX in the biofilm, was able to qualitatively reproduce the selective washout of NOB from the flocs in response to the decrease in DO-setpoint. Similarly, numerical simulations indicated that flocs removal is an effective operational strategy to achieve the selective washout of NOB. The direct competition for NO2- between NOB and AMX - the latter retained in the biofilm and acting as a "NO2-sink" - was identified by the model as key mechanism leading to a difference in the actual growth rates of AOB and NOB (i.e., µNOB < µAOB in flocs) and allowing for the selective NOB washout over a broad range of simulated sludge retention times (SRT = 6.8-24.5 d). Experimental results and model predictions demonstrate the increased operational flexibility, in terms of variables that can be easily controlled by operators, offered by hybrid systems as compared to solely biofilm systems for the control of NOB in mainstream PN/A applications.

Identification of Biokinetic Models Using the Concept of Extents.

The development of a wide array of process technologies to enable the shift from conventional biological wastewater treatment processes to resource recovery systems is matched by an increasing demand for predictive capabilities. Mathematical models are excellent tools to meet this demand. However, obtaining reliable and fit-for-purpose models remains a cumbersome task due to the inherent complexity of biological wastewater treatment processes. In this work, we present a first study in the context of environmental biotechnology that adopts and explores the use of extents as a way to simplify and streamline the dynamic process modeling task. In addition, the extent-based modeling strategy is enhanced by optimal accounting for nonlinear algebraic equilibria and nonlinear measurement equations. Finally, a thorough discussion of our results explains the benefits of extent-based modeling and its potential to turn environmental process modeling into a highly automated task.

Batch settling curve registration via image data modeling.

To this day, obtaining reliable characterization of sludge settling properties remains a challenging and time-consuming task. Without such assessments however, optimal design and operation of secondary settling tanks is challenging and conservative approaches will remain necessary. With this study, we show that automated sludge blanket height registration and zone settling velocity estimation is possible thanks to analysis of images taken during batch settling experiments. The experimental setup is particularly interesting for practical applications as it consists of off-the-shelf components only, no moving parts are required, and the software is released publicly. Furthermore, the proposed multivariate shape constrained spline model for image analysis appears to be a promising method for reliable sludge blanket height profile registration.

Optimal flow sensor placement on wastewater treatment plants.

Obtaining high quality data collected on wastewater treatment plants is gaining increasing attention in the wastewater engineering literature. Typical studies focus on recognition of faulty data with a given set of installed sensors on a wastewater treatment plant. Little attention is however given to how one can install sensors in such a way that fault detection and identification can be improved. In this work, we develop a method to obtain Pareto optimal sensor layouts in terms of cost, observability, and redundancy. Most importantly, the resulting method allows reducing the large set of possibilities to a minimal set of sensor layouts efficiently for any wastewater treatment plant on the basis of structural criteria only, with limited sensor information, and without prior data collection. In addition, the developed optimization scheme is fast. Practically important is that the number of sensors needed for both observability of all flows and redundancy of all flow sensors is only one more compared to the number of sensors needed for observability of all flows in the studied wastewater treatment plant configurations.

Estimation of nitrite in source-separated nitrified urine with UV spectrophotometry.

Monitoring of nitrite is essential for an immediate response and prevention of irreversible failure of decentralized biological urine nitrification reactors. Although a few sensors are available for nitrite measurement, none of them are suitable for applications in which both nitrite and nitrate are present in very high concentrations. Such is the case in collected source-separated urine, stabilized by nitrification for long-term storage. Ultraviolet (UV) spectrophotometry in combination with chemometrics is a promising option for monitoring of nitrite. In this study, an immersible in situ UV sensor is investigated for the first time so to establish a relationship between UV absorbance spectra and nitrite concentrations in nitrified urine. The study focuses on the effects of suspended particles and saturation on the absorbance spectra and the chemometric model performance. Detailed analysis indicates that suspended particles in nitrified urine have a negligible effect on nitrite estimation, concluding that sample filtration is not necessary as pretreatment. In contrast, saturation due to very high concentrations affects the model performance severely, suggesting dilution as an essential sample preparation step. However, this can also be mitigated by simple removal of the saturated, lower end of the UV absorbance spectra, and extraction of information from the secondary, weaker nitrite absorbance peak. This approach allows for estimation of nitrite with a simple chemometric model and without sample dilution. These results are promising for a practical application of the UV sensor as an in situ nitrite measurement in a urine nitrification reactor given the exceptional quality of the nitrite estimates in comparison to previous studies.

Blood-derived proteins in milk at start of lactation: Indicators of active or passive transfer.

Colostrum has a different composition compared with milk in established lactation. This difference is in part due to the partially open blood-milk barrier, which, when closed, is designed to prevent the interdiffusion of blood and milk components. In the first days of lactation, α-lactalbumin (α-LA), a milk protein, is typically present in blood and several blood-derived proteins are also present in milk, such as IgG1, IgG2, serum albumin (SA), and lactate dehydrogenase (LDH). With the exception of IgG1, which is known to be transferred by active transcellular transport, the other proteins are thought to pass paracellularly through the temporarily open barrier. Along with an exchange of blood and milk components, somatic cell count (SCC) is typically high in colostrum. The decline of these proteins and SCC can be used as indicators to determine transcellular or paracellular transport. Two hypotheses were tested. The first hypothesis was that the decline curve for a protein or SCC would be the same as IgG1, indicating transcellular transport, or the decline curve would be different than IgG1, indicating paracellular transport. The second hypothesis was that the decline curves of SCC and all proteins that are thought to have paracellular transport would be the same. Ten Holstein cows were milked at 4 h after parturition, the next 5 consecutive milkings, and the afternoon milking on d 5, 8, 10, and 14 of lactation for a total of 10 milking time points, and sequential jugular blood samples were also taken. Blood and milk samples were analyzed for the concentrations of LDH, SA, IgG1, IgG2, and α-LA and milk samples were measured for SCC. Protein concentration and SCC curves were generated from all 10 time points and were evaluated using the tau time constant model to determine the rate of decline of the slope of each protein. When examining the first hypothesis, the concentration of IgG1 declined significantly faster in the milk than the proteins IgG2 and LDH, but declined at the same rate as SA. Immunoglobulin G1 also declined significantly faster than SCC and α-LA in plasma. The second hypothesis showed that IgG2, LDH, and SA in milk were declining at the same rate, but were declining significantly faster than SCC and α-LA in plasma. These results indicate that only active transcellular transport of IgG1 occurred, with a sharp decline at parturition, compared with IgG2, SA, LDH, α-LA, and SCC, which are likely following paracellular transport.

Membrane bioreactor fouling behaviour assessment through principal component analysis and fuzzy clustering.

Adequate membrane bioreactor operation requires frequent evaluation of the membrane state. A data-driven approach based on principal component analysis (PCA) and fuzzy clustering extracting the necessary monitoring information solely out of transmembrane pressure data was investigated for this purpose. Out of three tested PCA techniques the two functional methods proved useful to cope with noise and outliers as opposed to the common standard PCA, while all of them presented similar capabilities for revealing data trends and patterns. The expert functional PCA approach enabled linking the two major trends in the data to reversible fouling and irreversible fouling. The B-splines approach provided a more objective way for functional representation of the data set but its complexity did not appear justified by better results. The fuzzy clustering algorithm, applied after PCA, was successful in recognizing the data trends and placing the cluster centres in meaningful positions, as such supporting data analysis. However, the algorithm did not allow a correct classification of all data. Factor analysis was used instead, exploiting the linearity of the observed two dimensional trends, to completely split the reversible and irreversible fouling effects and classify the data in a more pragmatic approach. Overall, the tested techniques appeared useful and can serve as the basis for automatic membrane fouling monitoring and control.