Resilience of a technical system is the ability to overcome minor failures and thus to avoid a complete breakdown of its vital functions. A possible failure of the system's components is one critical case the system designer should keep in mind. From another perspective resilience can be interpreted as the existence of alternative paths in a process network if resources break down. In this context we deal with process networks corresponding to systems which must be designed to operate in different scenarios. In order to ensure the system's functionality and to step in as a replacement in case of failure a set of optional resources must be available. This means that the process network must have several degrees of freedom allowing to react to uncertain events. With those restrictions we try to find a preferably resource-efficient network. Hence, an optimization problem arises which can be modeled using quantified mixed-integer linear programming. As an example of a process which can be modeled using process networks we investigate the problem of finding cost-efficient resilient topologies of fluid systems that are able to fulfill different load scenarios.

Individual technical components are usually well optimized. However, the design process of entire technical systems, especially in its early stages, is still dominated by human intuition and the practical experience of engineers. In this context, our vision is the widespread availability of software tools to support the human-driven design process with the help of modern mathematical methods. As a contribution to this, we consider a selected class of technical systems, so-called thermofluid systems. From a technical point of view, these systems comprise fluid distribution as well as superimposed heat transfer. Based on models for simple fluid systems as extensively studied in literature, we develop model extensions and algorithmic methods directed towards the optimized synthesis of thermofluid systems to a practical extent. Concerning fluid systems, we propose a Branch-and-Bound framework, exploiting problem-specific characteristics. This framework is then further analyzed using the application example of booster stations for high-rise buildings. In addition, we demonstrate the application of Quantified Programs to meet possible resilience requirements with respect to the systems generated. In order to model basic thermofluid systems, we extend the existing formulation for fluid systems by including heat transfer. Since this consideration alone is not able to deal with dynamic system behavior, we face this challenge separately by providing a more sophisticated representation dealing with the temporal couplings that result from storage components. For the considered case, we further show the advantages of this special continuous-time representation compared to the more common representation using discrete time intervals.

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