Sensor bias impact on efficient aeration control during diurnal load variations.

This study highlights the need to increase our understanding of the interplay between sensor drift and the performance of the automatic control system. The impact from biased sensors on the automatic control systems is rarely considered when different control strategies are assessed in water resource recovery facilities. Still, the harsh measurement environment with negative effects on sensor data quality is widely acknowledged. Simulations were used to show how sensor bias in an ammonium cascade feedback controller impacts aeration energy efficiency and total nitrogen removal in an activated sludge process. Response surface methodology was used to reduce the required number of simulations, and to consider the combined effect of two simultaneously biased sensors. The effects from flow variations, and negatively biased ammonium (-1 mg/L) and suspended solids sensors (-500 mg/L) reduced the nitrification aeration energy efficiency by between 7 and 25%. Less impact was seen on total nitrogen removal. There were no added non-linear effects from the two simultaneously biased sensors, apart from an interaction between a biased ammonium sensor and dissolved oxygen sensor located in the last aerated zone. Negative effects from sensor bias can partly be limited if the expected bias direction is considered when the controller setpoint-limits are defined.

Fault signatures and bias progression in dissolved oxygen sensors.

Biofilm fouling is known to impact the data quality of sensors, but little is known about the exact effects. We studied the effects of artificial and real biofilm fouling on dissolved oxygen (DO) sensors in full-scale water resource recovery facilities, and how this can automatically be detected. Biofilm fouling resulted in different drift direction and bias magnitudes for optical (OPT) and electrochemical (MEC) DO sensors. The OPT-sensor was more affected by biofilm fouling compared to the MEC-sensor, especially during summer conditions. A bias of 1 mg/L was detected by analysing the impulse response (IR) of the automatic air cleaning system in the DO sensor. The IR is an effect of a temporal increase in DO concentration during the automatic air cleaning. The IRs received distinct pattern changes that were matched with faults including: biofilm fouling, disturbances in the air supply to the cleaning system, and damaged sensor membrane, which can be used for fault diagnosis. The results highlight the importance of a condition-based sensor maintenance schedule in contrast to fixed cleaning intervals. Further, the results stress the importance of understanding and detecting bias due to biofilm fouling, in order to maintain a robust and resource-efficient process control.

Gaussian process regression for monitoring and fault detection of wastewater treatment processes.

Monitoring and fault detection methods are increasingly important to achieve a robust and resource efficient operation of wastewater treatment plants (WWTPs). The purpose of this paper was to evaluate a promising machine learning method, Gaussian process regression (GPR), for WWTP monitoring applications. We evaluated GPR at two WWTP monitoring problems: estimate missing data in a flow rate signal (simulated data), and detect a drift in an ammonium sensor (real data). We showed that GPR with the standard estimation method, maximum likelihood estimation (GPR-MLE), suffered from local optima during estimation of kernel parameters, and did not give satisfactory results in a simulated case study. However, GPR with a state-of-the-art estimation method based on sequential Monte Carlo estimation (GPR-SMC) gave good predictions and did not suffer from local optima. Comparisons with simple standard methods revealed that GPR-SMC performed better than linear interpolation in estimating missing data in a noisy flow rate signal. We conclude that GPR-SMC is both a general and powerful method for monitoring full-scale WWTPs. However, this paper also shows that it does not always pay off to use more sophisticated methods. New methods should be critically compared against simpler methods, which might be good enough for some scenarios.

Fault detection and isolation of sensors in aeration control systems.

In this paper, we consider the problem of fault detection (FD) and isolation in the aeration system of an activated sludge process. For this study, the dissolved oxygen in each aerated zone is assumed to be controlled automatically. As the basis for an FD method we use the ratio of air flow rates into different zones. The method is evaluated in two scenarios: using the Benchmark Simulation Model no. 1 (BSM1) by Monte Carlo simulations and using data from a wastewater treatment plant. The FD method shows good results for a correct and early FD and isolation.

Automated data transfer for digital twin applications: Two case studies.

Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor-made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process to digital twin is low compared to the simulation frequency in both systems. The presented digital twin objectives can be achieved using either of the presented methods. The method of Case 1 is better suited for automatic recalibration of model parameters, although workarounds exist for the method in Case 2. The method of Case 2 is well suited for objectives such as soft sensors due to its integration with the SCADA system and low latency. The objective of the digital twin, and the required latency of the system, should guide the choice of method. PRACTITIONER POINTS: Various methods can be used for automated data transfer between the physical system and a digital twin. Delays in the data transfer differ depending on implementation method. The digital twin objective determines the required simulation frequency. Implementation method should be chosen based on the required simulation frequency.

To calibrate or not to calibrate, that is the question.

Sensors used for control have become widespread in water resources recovery facilities during the strive for resource efficient operations. However, their accuracy is reliant on uncertain laboratory measurements, which are used for calibration and, in turn, to correct for sensor drift. At the same time, current sensor calibration practices are lacking clear theoretical understanding of how measurement uncertainties impact the final control action. The effects of a customarily, and ad hoc, applied calibration threshold are unknown, leading to the current situation where many wastewater treatment processes are controlled by measurements with unknown accuracy. To study how sensor accuracy is affected by calibration, including varying calibration thresholds, we developed a simple theoretical model with closed-form expressions based on the variance and bias in sensor and laboratory measurements. The model was then simulated to yield the results, which showed no practical gain of using a calibration threshold, apart from the situation when calibration is more time-consuming than validation. By contrast, the best accuracy was obtained when consistently executing calibration, which opposes common practice. Further, the sensor calibration error was shown to be transferred to the process, causing a similar deviation from the setpoint when the same sensor was used for control. This emphasizes the importance of minimizing laboratory measurement uncertainties during calibration, which otherwise directly impact operations. Due to these findings we strongly advice shifting mindset from considering calibration as a sequential detection and correction approach, towards an estimation approach, aiming to estimate bias magnitude and drift speed.

Spatial heterogeneity ensures long-term stability in vegetation and Fritillaria meleagris flowering in Uppsala Kungsäng, a semi-natural meadow.

Semi-natural grasslands are becoming increasingly rare, and their vegetation may be affected by environmental changes and altered management. At Kungsängen Nature Reserve, a wet to mesic semi-natural meadow near Uppsala, Sweden, we analysed long-term changes in the vegetation using data from 1940, 1982, 1995 and 2016. We also analysed the spatial and temporal dynamics in the Fritillaria meleagris population based on countings of flowering individuals in 1938, 1981-1988 and 2016-2021. Between 1940 and 1982 the wet part of the meadow became wetter, which led to an increased cover of Carex acuta and pushed the main area of flowering of F. meleagris up towards the mesic part. Annual variation in the flowering propensity of F. meleagris (in May) was affected by temperature and precipitation in the phenological phases of growth and bud initiation (June in the previous year), shoot development (September in the previous year) and initiation of flowering (March-April). However, the response to weather was in opposite directions in the wet and mesic parts of the meadow, and the flowering population showed large year-to-year variation but no long-term trend. Variation in management (poorly documented) led to changes in different parts of the meadow, but the overall composition of the vegetation, species richness and diversity changed little after 1982. Species richness and species composition of the meadow vegetation, and the long-term stability of the F. meleagris population are maintained by the variation in wetness, highlighting the importance of spatial heterogeneity as an insurance against biodiversity loss in semi-natural grasslands and nature reserves generally.

Changes versus homeostasis in alpine and sub-alpine vegetation over three decades in the sub-arctic.

Plant species distributions are expected to shift and diversity is expected to decline as a result of global climate change, particularly in the Arctic where climate warming is amplified. We have recorded the changes in richness and abundance of vascular plants at Abisko, sub-Arctic Sweden, by re-sampling five studies consisting of seven datasets; one in the mountain birch forest and six at open sites. The oldest study was initiated in 1977-1979 and the latest in 1992. Total species number increased at all sites except for the birch forest site where richness decreased. We found no general pattern in how composition of vascular plants has changed over time. Three species, Calamagrostis lapponica, Carex vaginata and Salix reticulata, showed an overall increase in cover/frequency, while two Equisetum taxa decreased. Instead, we showed that the magnitude and direction of changes in species richness and composition differ among sites.

Model-based monitoring of diffuser fouling using standard sensors.

Fouling of fine-pore diffusers can cause substantial aeration energy wastage. It remains challenging to monitor their condition and decide the optimal time for labour-intensive and costly cleaning actions. In this study, we show that data from standard sensors (airflow rate, dissolved oxygen concentration, pressure and airflow valve position), which are fed to simple models, can track the diffuser's condition. Additionally, the parameter estimation of diffuser dynamic wet pressure, oxygen transfer rate, respiration rate and the joint alpha fouling factor ( α F ) was facilitated by an active fault detection inspired method. The method executes a sequence with piecewise constant valve positions via the control system. As a result, airflow rates in a sequence similar to a staircase are obtained, which simplifies the estimation of dissolved oxygen dynamics and airflow rate dynamics. The proposed method was evaluated on a full scale over 18 months and successfully detected a reduced cleaning in the diffusers and several sensor-related disturbances. Ultimately, the findings motivate further research on how modelling combined with repetitive process disturbances can leverage data-driven insights from standard instrumentation.

Automated active fault detection in fouled dissolved oxygen sensors.

Biofilm formation causes bias in dissolved oxygen (DO) sensors, which hamper their usage for automatic control and thereby balancing energy- and treatment efficiency. We analysed if a dataset that was generated with deliberate perturbations, can automatically be interpreted to detect bias caused by biofilm formation. We used a challenging set-up with realistic conditions that are required for a full-scale application. This included automated training (adapting to changing normal conditions) and automated tuning (setting an alarm threshold) to assure that the fault detection (FD)-methods are accessible to the operators. The results showed that automatic usage of FD-methods is difficult, especially in terms of automatic tuning of alarm thresholds when small training datasets only represent the normal conditions, i.e. clean sensors. Despite the challenging set-up, two FD-methods successfully improved the detection limit to 0.5 mg DO/L bias caused by biofilm formation. We showed that the studied dataset could be interpreted equally well by simpler FD-methods, as by advanced machine learning algorithms. This in turn indicates that the information contained in the actively generated data was more vital than its interpretation by advanced algorithms.

Analysis of photobioreactors in series.

A photobioreactor (PBR) contains microalgae which under illumination consume carbon dioxide and substrate dissolved in water, and produce oxygen. The process is used in water recovery resource facilities with a continuous flow of wastewaster through the PBR. With several PBRs in series the reduction of substrate can be improved. This paper contains a thorough analysis of a model of PBRs in series, where each PBR is modelled with a system of three ordinary differential equations for the concentrations of dissolved substrate and biomass (algae), and the internal cell quota of substrate to biomass. Each PBR has a certain volume and irradiation. The absorption rate of substrate into the cells is modelled with Monod kinetics, whereas the biomass growth rate is modelled with Droop kinetics, in which both a minimum and a maximum internal cell quota are assumed. The main result is that the model has a unique stable steady-state solution with algae in all PBRs. Another stable steady-state solution is the wash-out solution with no algae in the system. Other steady-state solutions are combinations of these two with no algae in some of the first PBRs and algae in the rest of the PBRs in the series. Conditions on the illumination, volumetric flow and volumes of the PBRs are given for the respective solution. Numerical solutions illustrate the theoretical results and indicate further properties.

Changes in species abundance after seven years of elevated atmospheric CO2 and warming in a Subarctic birch forest understorey, as modified by rodent and moth outbreaks.

A seven-year long, two-factorial experiment using elevated temperatures (5 °C) and CO2 (concentration doubled compared to ambient conditions) designed to test the effects of global climate change on plant community composition was set up in a Subarctic ecosystem in northernmost Sweden. Using point-frequency analyses in permanent plots, an increased abundance of the deciduous Vaccinium myrtillus, the evergreens V. vitis-idaea and Empetrum nigrum ssp. hermaphroditum and the grass Avenella flexuosa was found in plots with elevated temperatures. We also observed a possibly transient community shift in the warmed plots, from the vegetation being dominated by the deciduous V. myrtillus to the evergreen V. vitis-idaea. This happened as a combined effect of V. myrtillus being heavily grazed during two events of herbivore attack-one vole outbreak (Clethrionomys rufocanus) followed by a more severe moth (Epirrita autumnata) outbreak that lasted for two growing seasons-producing a window of opportunity for V. vitis-idaea to utilize the extra light available as the abundance of V. myrtillus decreased, while at the same time benefitting from the increased growth in the warmed plots. Even though the effect of the herbivore attacks did not differ between treatments they may have obscured any additional treatment effects. This long-term study highlights that also the effects of stochastic herbivory events need to be accounted for when predicting future plant community changes.

Cost-efficient operation of a denitrifying activated sludge process.

In this paper, the choice of optimal set-points and cost minimizing control strategies for the denitrification process in an activated sludge process are treated. In order to compare different criterion functions, simulations utilizing the COST/IWA simulation benchmark are considered. By means of operational maps the results are visualized. It is found that it is easy to distinguish set-point areas where the process can be said to be efficiently controlled in an economic sense. The characteristics of these set-point areas depend on the chosen effluent nitrate set-point as well as the distribution of the different operating costs. It is also discussed how efficient control strategies may be accomplished.

Hydrological change--climate change impact simulations for Sweden.

Climate change resulting from the enhanced greenhouse effect is expected to give rise to changes in hydrological systems. This hydrological change, as with the change in climate variables, will vary regionally around the globe. Impact studies at local and regional scales are needed to assess how different regions will be affected. This study focuses on assessment of hydrological impacts of climate change over a wide range of Swedish basins. Different methods of transferring the signal of climate change from climate models to hydrological models were used. Several hydrological model simulations using regional climate model scenarios from Swedish Regional Climate Modelling Programme (SWECLIM) are presented. A principal conclusion is that subregional impacts to river flow vary considerably according to whether a basin is in northern or southern Sweden. Furthermore, projected hydrological change is just as dependent on the choice of the global climate model used for regional climate model boundary conditions as the choice of anthropogenic emissions scenario.