Real-time management and control of groundwater flow field and quality

Real-time control is a powerful tool for steering complex and dynamical systems into the desired direction. Parallel to the increase in computational capacity and the availability of measurements in real-time, real-time control has become interesting for management of environmental systems that are under pressure. One such system is the Hardhof well field in Zurich, Switzerland. The location within the city boundaries and downstream of contaminated sites make its water quality vulnerable to multiple threats, some of which have to be controlled on a daily basis. This thesis analyses the real-time control in operation at the Hardhof well field. The efficacy of the real-time flow model as well as the real-time control of the flow field was confirmed. Although the existing optimal control, that is computed with a genetic algorithm, performs well, the routine is time consuming and its understanding requires extensive expert knowledge. As an alternative, an expert system control was designed for the well field. Compared to the historically applied management, the expert system was able to reduce the risk of potentially polluted water reaching the drinking water production wells to a similar level as the optimal control currently in place at least during the analysed time period. The result was achieved in a fraction of the time needed for the optimal control and the algorithm of the expert system is intuitively understandable. The disadvantage of the expert system control is that, contrary to the genetic algorithm, it is not able to automatically adjust to new boundary conditions that were not included in the knowledge base of the design phase. Further, the method of ensemble control was applied to the 2D flow model of the Hardhof well field. It includes the ensemble information that is already available from the real-time modelling in the control routine. Results show a considerable improvement of the resilience of the control with regard to extreme events compared to traditional deterministic and stochastic control methods. It could further be shown that the feedback from the model update which the control receives as an input from the real-time model enhances the performance of the control substantially. The ensemble control further allows the characterization of the risk to draw city water which is not possible with the control currently in operation at the Hardhof. Last but not least the threat of rising groundwater temperatures in the well field was addressed. A temperature control routine was coupled to the 3D-heat transport model of the Hardhof. A one year simulation of 3D heat transport showed that seasonally, the abstraction rates in the well field can be re-distributed to decrease the temperature in the drinking water produced by 1 to 2 ◦C. Long term simulations with the 2D heat transport model revealed a seasonal cold water bubble in the center of the well field where a further drinking water production well could be constructed to pump colder water.