Mari Heinonen

Do wastewater treatment plants act as a potential point source of microplastics?Preliminary study in the coastal Gulf of Finland, Baltic Sea

This study on the removal of microplastics during different wastewater treatment unit processes was carried out at Viikinmäki wastewater treatment plant (WWTP). The amount of microplastics in the influent was high, but it decreased significantly during the treatment process. The major part of the fibres were removed already in primary sedimentation whereas synthetic particles settled mostly in secondary sedimentation. Biological filtration further improved the removal. A proportion of the microplastic load also passed the treatment and was found in the effluent, entering the receiving water body. After the treatment process, an average of 4.9 (±1.4) fibres and 8.6 (±2.5) particles were found per litre of wastewater. The total textile fibre concentration in the samples collected from the surface waters in the Helsinki archipelago varied between 0.01 and 0.65 fibres per litre, while the synthetic particle concentration varied between 0.5 and 9.4 particles per litre. The average fibre concentration was 25 times higher and the particle concentration was three times higher in the effluent compared to the receiving body of water. This indicates that WWTPs may operate as a route for microplastics entering the sea.

Nitrous Oxide Production at a Fully Covered Wastewater Treatment Plant: Results of a Long-Term Online Monitoring Campaign

The nitrous oxide emissions of the Viikinmäki wastewater treatment plant were measured in a 12 month online monitoring campaign. The measurements, which were conducted with a continuous gas analyzer, covered all of the unit operations of the advanced wastewater-treatment process. The relation between the nitrous oxide emissions and certain process parameters, such as the wastewater temperature, influent biological oxygen demand, and ammonium nitrogen load, was investigated by applying online data obtained from the process-control system at 1 min intervals. Although seasonal variations in the measured nitrous oxide emissions were remarkable, the measurement data indicated no clear relationship between these emissions and seasonal changes in the wastewater temperature. The diurnal variations of the nitrous oxide emissions did, however, strongly correlate with the alternation of the influent biological oxygen demand and ammonium nitrogen load to the aerated zones of the activated sludge process. Overall, the annual nitrous oxide emissions of 168 g/PE/year and the emission factor of 1.9% of the influent nitrogen load are in the high range of values reported in the literature but in very good agreement with the results of other long-term online monitoring campaigns implemented at full-scale wastewater-treatment plants.

Adaptive data-derived anomaly detection in the activated sludge process of a large-scale wastewater treatment plant

This work examines real-time anomaly detection and isolation in a full-scale wastewater treatment application. The Viikinmaki plant is the largest municipal wastewater treatment facility in Finland. It is monitored with ample instrumentation, though their potential is not yet fully exploited. One reason that prevents the use of the instrumentation in plant control is the occasional insufficient measurement performance. Therefore, we investigate an intelligent anomaly detection system for the activated sludge process in order to motivate a more efficient use of sensors in the process operation. The anomaly detection methodology is based on principal component analysis. Because the state of the process fluctuates, moving-window extensions are used to adapt the analysis to the time-varying conditions. The results show that both instrument and process anomalies were successfully detected using the proposed algorithm and the variables responsible for the anomalies correctly isolated. We also demonstrate that the proposed algorithm represents a convenient improvement for supporting the efficient operation of wastewater treatment plants. HighlightsAnomaly detection is investigated in a biological process of a full-scale WWTP.The aim is to design a system motivating an efficient use of sensors in the operation.The proposed intelligent anomaly detection system is used for real-time monitoring.Adaptive techniques are used to adjust to the time-varying process conditions.Instrument and process anomalies are successfully detected with the proposed system.

N2O emissions from secondary clarifiers and their contribution to the total emissions of the WWTP

Recent studies have indicated that the emissions of nitrous oxide, N2O, constitute a major part of the carbon footprint of wastewater treatment plants (WWTPs). Denitrification occurring in the secondary clarifier basins has been observed by many researchers, but until now N2O emissions from secondary clarifiers have not been widely reported. The objective of this study was to measure the N2O emissions from secondary clarifiers and weigh the portion they could represent of the overall emissions at WWTPs. Online measurements over several days were carried out at four different municipal WWTPs in Finland in cold weather conditions (March) and in warm weather conditions (June-July). An attempt was made to define the conditions in which N2O emissions from secondary clarifiers may occur. It was evidenced that large amounts of N2O can be emitted from the secondary clarifiers, and that the emissions have long-term variation. It was assumed that part of the N2O released in secondary clarification was originally formed in the activated sludge basin. The emissions from secondary clarification thus seem to be dependent on conditions of the nitrification and denitrification accomplished in the denitrification-nitrification process and on the amount of sludge stored in the secondary clarifiers.

Shall we use hardware sensor measurements or soft-sensor estimates? Case study in a full-scale WWTP

An array of data derived soft-sensors for estimating nitrate-nitrogen concentrations in the denitrifying post-filtration unit of a municipal wastewater treatment plant have been designed in our earlier work. The array was designed to back up the existing hardware instrumentation in the process unit. The importance of trustworthy information on nitrate-nitrogen concentrations is emphasized by their employment in control loops enhancing nitrogen removal. In this paper, we propose an automatic system for deciding whether the use of estimates or corresponding hardware measurements is preferable given the current condition in filters. The presented switching system is based on ranking the estimates and the measurements, and a preferability index that combines the assigned ranks. The ranking procedure employs a robust principal component analysis and statistics used as the measures of fit. The switching system was demonstrated to enable the employment of measurements and estimates in a complementary manner in operating the post-filtration unit. This study concerns data derived soft-sensors and corresponding hardware measurements.The soft-sensors estimate nitrate concentration in the tertiary filters of a WWTP.A system for deciding whether to use estimates or hardware measurements is proposed.The switching system is based on ranking the estimates and the measurements.The system allows the use of estimates and measurements in a complementary manner.

Nitrate estimation in the denitrifying post-filtration unit of a municipal wastewater treatment plant: the Viikinmäki case.

In this work we present and discuss the design of an array of soft-sensors to estimate the nitrate concentration in the denitrifying post-filtration unit at the Viikinmäki wastewater treatment plant in Helsinki (Finland). The developed sensors aim at supporting the existing hardware analyzers by providing a reliable back-up system in case of malfunction of the instruments. In the attempt to design easy to implement and interpretable sensors, computationally light linear models have been considered. However, due to the intrinsic nonlinearity of the process, also nonlinear but still computationally affordable models have been considered for comparison. The experimental results demonstrate the potential of the developed soft-sensors and the possibility for an on-line implementation in the plants control system as alternative monitoring devices.

An application of predictive control to the Viikinmäki wastewater treatment plant

Abstract This paper deals with the development of a multivariable predictive control structure for improving the nitrogen removal of a biological wastewater treatment plant while reducing the operational costs. A simple dynamic matrix control algorithm is utilised as predictive controller and applied to a full-scale municipal wastewater treatment plant for controlling nitrogen concentrations at the end of the biological process. The complex calibrated model of the process is implemented in a commercial simulator that acts as a real-time testing platform for the proposed control structure, and allows the identification of the multivariable input-output model for the predictive control. Simulation results show the potentialities of the chosen predictive control, which allows the reduction of ammonia peaks in the effluent and at the same time permits a reduction of the energy consumption costs.

Direct precipitation on demand at large Scandinavian WWTPs reduces the effluent phosphorus load

On demand use of direct precipitation of wastewater has been successfully implemented at several large Scandinavian wastewater treatment plants (WWTPs) as a cost-efficient method of treating wastewater bypassing secondary treatment. During wet weather situations or when the capacity of secondary treatment is reduced excess wastewater can be treated through efficient direct precipitation. This increases the total capacity of the WWTP to remove phosphorus during these periods. This treatment strategy allows the WWTPs to meet stringent effluent phosphorus limits without extending secondary treatment of the main plant, despite high wet weather flows. The gain in terms of reduced phosphorus emissions varies depending on local conditions such as climate, collection system and secondary treatment capacity. It also varies from year to year depending on the weather and reductions of capacity due to planned refurbishing or unplanned breakdown of equipment. Operating chemical precipitation on demand has proved to contain challenges to operation and organisation of the WWTP. These challenges include logistics of start-up, training of staff and maintaining the system between occasions of operation. Sufficient up-stream storage capacity, reliable weather forecasts and good contracts with suppliers of chemicals are keys of success.

Development of an Extended ASM3 Model for Predicting the Nitrous Oxide Emissions in a Full-Scale Wastewater Treatment Plant

An Activated Sludge Model #3 (ASM3) based, pseudomechanistic model describing nitrous oxide (N2O) production was created in this study to provide more insight into the dynamics of N2O production, consumption, and emissions at a full-scale wastewater treatment plant (WWTP). N2O emissions at the studied WWTP are monitored throughout the plant with a Fourier transform infrared analyzer, while the developed model encountered N2O production in the biological reactors via both ammonia oxidizing bacteria (AOB) nitrification and heterotrophic denitrifiers. Additionally, the stripping of N2O was included by applying a KLa-based approach that has not been widely used before. The objective was to extend the existing ASM3-based model of the plant and assess how well the full-scale emissions could be predicted with the selected model. The validity and applicability of the model were tested by comparing the simulation results with the comprehensive online data. The results show that the ASM3-based model can be successfully extended and applied to modeling N2O production and emissions at a full-scale WWTP. These results demonstrate that the biological reactor can explain most of the N2O emissions at the plant, but a significant proportion of the liquid-phase N2O is further transferred during the process.

Seasonal and Diurnal Variations of GHG Emissions Measured Continuously at the Viikinmäki Underground WWTP

The gaseous emissions of an underground activated sludge treatment plant with total nitrogen removal have been measured on-line since 2012. The continuous Fourier transform infrared (FT-IR) measurement of CO2, NO, NO2, N2O, NH3 and CH4 is situated in the single exhaust air pipe, thus covering the whole wastewater treatment process. The measurement data from 2015 showed that the seasonal and diurnal variations of N2O and CH4 are considerable. When comparing N2O and CH4 emissions as CO2 equivalents to CO2 formed in the activated sludge process, the impact of N2O was dominant. The CH4 emission was considerably smaller, and the emissions of NOx and NH3 from biological treatment were small. The long term variations of N2O or CH4 production were not linked directly to load variations or other “apparent” factors such as temperature, nitrogen load, nitrogen reduction or anoxic volume. There was neither a clear seasonal pattern. The short interval variations of N2O were similar to but not identical with CO2 variations.