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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.