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We combine a previously developed strategy for Fault Detection and Identification (FDI) with a supervisory controller in closed loop. The combined method is applied to a model of a pilot-scale cooling loop of a nuclear plant, which includes Kalman filters and a model-based predictive controller as part of normal operation. The system has two valves available for flow control meaning that some redundancy is available. The FDI method is based on likelihood ratios for different fault scenarios which in turn are derived from the application of the Kalman filter. A previously introduced extension of the FDI method is used here to enable detection and identification of non-linear faults like stuck valve problems and proper accounting of the time of fault introduction. The supervisory control system is designed so to take different kinds of actions depending on the status of the fault diagnosis task and on the type of identified fault once diagnosis is complete. Some faults, like sensor bias and drift, are parametric in nature and can be adjusted without need for reconfiguration of the regulatory control system. Other faults, like a stuck valve problem, require reconfiguration of the regulatory control system. The whole strategy is demonstrated for several scenarios.