Modern water system infrastructures are equipped with a large amount of sensors. In recent years machine-learning (ML) algorithms became a promising option for data analysis. However, currently ML algorithms are not frequently used in real-world applications. One reason is the costly and time-consuming integration and maintenance of ML algorithms by data scientists. To overcome this challenge, this paper proposes a generic, adaptable platform for real-time data analysis in water distribution networks. The architecture of the platform allows to connect to different types of data sources, to process its measurements in realtime with and without ML algorithms and finally pushing the results to different sinks, like a database or a web-interface. This is achieved by a modular, plugin based software architecture of the platform. As a use-case, a data-driven anomaly detection algorithm is used to monitor the water quality of several water treatment plants of the city of Berlin.

Many industrial and environmental processes are characterized as complex spatio-temporal systems. Such systems known as distributed parameter systems (DPSs) are usually highly complex and it is difficult to establish the relation between model inputs, model outputs and model parameters. Moreover, the solutions of physics-based models commonly differ somehow from the measurements. In this work, appropriate Uncertainty Quantification (UQ) approaches are selected and combined systematically to analyze and identify systems. However, there are two main challenges when applying the UQ approaches to nonlinear distributed parameter systems. These are: (1) how uncertainties are modeled and (2) the computational effort, as the conventional methods require numerous evaluations of the model to compute the probability density function of the response. This paper presents a framework to solve these two issues. Within the Bayesian framework, incomplete knowledge about the system is considered as uncertainty of the system. The uncertainties are represented by random variables, whose probability density function can be achieved by converting the knowledge of the parameters using thePrinciple of Maximum Entropy. The generalized Polynomial Chaos (gPC) expansion is employed to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analyzing systems systematically and reducing the disagreement between model predictions and measurements of the real processes to fulfill user defined performance criteria. The efficiency of the framework is assessed by applying it to a benchmark model (neutron diffusion equation) and to a model of a complex rheological forming process. These applications illustrate that the framework is capable of systematically analyzing the system and optimally calibrating the model parameters.

Climate change is leading to a general shortage of raw water availability combined with more pronounced seasonality and dry phases. The goal of the collaborative research project TwinOptPRO is to contribute to EU-wide climate neutrality in 2050 by the minimization of energy supply for water treatment and pumps in drinking water distribution systems. For that purpose, a digital platform that combines different forecasting models with simulation and optimization modules was developed. The aim is to ensure secure and compliant supply to customers in the future while maximizing the use of renewable energy and minimizing costs.

The control of complex forming processes (e.g. glass forming processes) is a challenging topic due to the mostly strongly nonlinear behavior and the spatial distributed nature of the process. In this paper a new approach for the real-time control of a spatial distributed temperature profile of an industrial glass forming process is presented. As the temperature in the forming zone cannot be measured directly, it is estimated by the numerical solution of the partial differential equation for heat transfer by a finite element scheme. As the dimension of the state space model, which is yield by the FE algorithm, is too large for real-time optimization, a model reduction concept has been developed. The numerical solution of the optimization problem is performed by the solver HQP (Huge Quadratic Programming). Results of the NMPC concept are compared with conventional PI control results. It is shown that NMPC stabilizes the temperature of the forming zone much better than PI control. The proposed NMPC scheme is robust against model mismatch of the disturbance model. Furthermore, the allowed parameter settings for a real-time application (i.e. control horizon, sampling period) have been determined. The approach can easily be adapted to other forming processes where the temperature profile shall be controlled.

Zusammenfassung: Trinkwasserversorgungsnetze sind komplexe Systeme, welche zurzeit mit einer großen Anzahl von Wasserqualitätssensoren ausgestattet werden. Eine wichtige Aufgabe der Sensoren ist hierbei, die Betreiber des Wassernetzes über mögliche Kontaminationen im Netz zu informieren. Die Analyse der Sensorwerte erfolgt dazu normalerweise manuell über den Betreiber. Datenbasierte Methoden haben sich in der Praxis noch nicht bewährt, da diese unter anderem eine große Anzahl an falsch detektierten Alarmen auslösen, welche beispielsweise aufgrund betrieblicher Änderungen auftreten. In diesem Beitrag wird daher ein Verfahren basierend auf der dynamischen PCA entwickelt, welches die Anzahl an Fehlalarmen reduziert, gleichzeitig aber weiterhin sensitiv bei tatsächlichen Kontaminationen reagiert. Der entwickelte Ansatz wird anhand von Experimenten an einem Laborprozess evaluiert.