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Ammonia control in municipal wastewater treatment plants typically requires maintenance-intensive instrumentation. A low maintenance alternative is sought for small- to medium-scale applications. To this end, a pH-based soft-sensor is proposed to detect ammonia peak load events. This soft-sensor is based on a newly developed technique for qualitative trend analysis and is combined with a rule-based controller. The use of qualitative trend analysis makes this soft-sensor tolerant towards sensor drifts and thereby reduces the maintenance effort. The method allows controlling any process in which relative changes in the measured output are informative about the system output.

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.

The development and validation of methods for fault detection and identification in wastewater treatment research today relies on two important assumptions: {\em (i)} that sensor faults appear at distinct times in different sensors and {\em (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 important fault symptoms that appear simultaneously and with similar intensity. Consequently, our work suggests that focus of research on methods for fault detection and identification should be reoriented towards methods that do not rely on the assumptions mentioned above. This study also provides the very first empirically validated sensor fault model for wastewater treatment simulation and we recommend its use for effective benchmarking of both fault detection and identification methods and advanced control strategies. Finally, we evaluate the value of redundancy for the purpose of remote sensor validation in decentralized wastewater treatment systems.

For complex systems such as wastewater treatment plants (WWTPs), effective data communication is an important step to enable operators to assess their plant. However, examples in practice show that this step is insufficiently considered. In this article, we describe a fast, relevant, and intuitive decision-support tool for operators. We have developed a key performance indicator (KPI) visualisation tool for energy and process data embedded in a larger process optimisation software. The KPI set consists of indicator values relating to energy and effluent quality. In order to ensure that the visualisation tool will be used and cover the needs of the plant staff, we developed this part of the software in collaboration with two WWTPs. At the time of writing, the tool is used in the daily operation of both plants. The operators see the tool's most important advantages as its ability to quickly assess current plant performance and to simplify the tracking and analysis of inter- and intra-process relationships and dynamics.

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.

Ammonium N H 4 + load based aeration control on biological wastewater treatment plants saves costs and enhances nitrogen removal. However, the need for maintenance intensive N H 4 + sensors hamper the controls application in practice. Alternatives, in the form of soft-sensors are broadly discussed in academia. A soft-sensor recently described in literature exploits the pH effects induced by biological N H 4 + oxidation. This concept is now further developed by means of qualitative trend analysis (QTA). Previously, the qualitative path estimation (QPE) algorithms was proposed as a fast and reliable QTA algorithm for batch process data analysis. It does not allow online application in continuous flow systems however. In this work, a modification of QPE, call qualitative state estimation (QSE), is proposed as a suitable algorithm for continuous-flow systems. Initial tests indicate that the QSE algorithms is a robust technique for extraction of relevant information in a full-scale environment. At the WWTP Hard in Winterthur, this resulted in cost-saving automation of the aeration system. This contribution summarizes these first results.