Reconstruction of corrupted datasets from ammonium-ISE sensors at WRRFs through merging with daily composite samples

Long-term, continuous datasets of high quality are important for instrumentation, control, and automation efforts of wastewater resources recovery facility (WRRFs). This study presents a methodology to increase the reliability of measurements from ammonium ion-selective electrodes (ISEs). This is done by correcting corrupted ISE data with a data source that often is available at WRRFs (volume-proportional composite samples). A yearlong measurement campaign showed that the existing standard protocols for sensor maintenance might still create corrupted dataset, with poor sensor recalibrations responsible for abrupt and unrealistic jumps in the measurements. The proposed automatic correction methodology removes both recalibration jumps and signal drift by using information from composite samples that already are taken for reporting to legal authorities. Results showed that the developed methodology provided a continuous, high-quality time series without the major data quality issues of the original signal. In fact, the signal was improved for 87% of days when a reference sample was available. The effect of correcting the data before use in a data-driven software sensor was also investigated. The corrected dataset led to noticeably smaller day-to-day variations in estimated NH₄⁺ loads, and to large improvements on both median estimates and prediction bounds. The long time series allowed for an investigation of how much training data that is required to fit a software sensor, which provides estimates that are representative for the entire study period. The results showed that 8 weeks of data allowed for a good median estimate, while 16 weeks are required for obtaining good 80% prediction bounds. Overall, the proposed method can increase the applicability of relatively cheaper ISE sensors for ICA application within WRRFs.

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.

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.

Economic and environmental optimization of phosphorus removal

Do operating costs conform to environmental impact after introduction of charges on discharge of wastewater? A study on optimization of phosphorus removal at two Danish wastewater removal plants shows that this is actually the case. By measurement of inlet and outlet concentrations and of chemicals added it was possible to determine the relationships between chemical dosing and phosphorus discharge and thus calculate the operational cost and environmental impact of different dosing/discharge levels.

Kinetic start-up performance of two large treatment plants for nutrient removal

In 1987 an action plan was passed in the Danish Parliament demanding a considerable reduction of the discharge of nutrients to the aquatic environment in Denmark. Consequently, the two largest wastewater treatment plants in the Copenhagen area had to be upgraded to include nutrient removal. For more than 8 years an extensive effort has been made to determine an optimum solution for this upgrading from a technical and financial point of view. The work included six years of comprehensive pilot plant investigations with the aim of thoroughly studying and interpreting the kinetics of the processes involved. The investigations revealed valuable information particularly concerning limitations of the nitrification process. Consequently, the investigations contributed to an expectation of no unforeseen problems during the implementation of the upgraded plants. This paper presents the results of the start-up of the two full-scale plants with the main emphasis laid on the kinetic performance in relation to the information achieved from the pilot tests. The results showed that the start-up of the full scale plants proceeded with great accuracy as expected from the investigations. Accordingly, the implementation of the plants was carried out successfully, ending an era of more than 10 years in total.