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A safety instrumented system (SIS) performs \nspecified functions to achieve or maintain a safe state of the \nprocess when unacceptable or dangerous process conditions are \ndetected. A logic solver is required to receive the sensor input \nsignal(s), to make appropriate decisions based on the nature of \nthe signal(s), and to change its outputs according to user-\ndefined logic. The change of the logic solver output(s) results \nin the final element(s) taking action on the process (e.g., \nclosing a valve) to bring it back to a safe state. Alarm \nmanagement is a powerful tool to support the operators’ work to \ncontrol the process in safe operating regions and to detect \nprocess malfunctions. Predictive alarm management (PAM) systems \nshould be able not only to detect a dangerous situation early \nenough, but also to give advice to process operators which \nsafety action (or safety element(s)) must be applied. The aim of \nthis paper is to develop a novel methodology to support the \noperators how to make necessary adjustments in operating \nvariables at the proper time. The essential of the proposed \nmethodology is the simulation of the effect of safety elements \nover a prediction horizon. Since different manipulations have \ndifferent time demand to avoid the evolution of the unsafe \nsituation (safety time), the process operators should know which \nsafety action(s) should be taken at a given time. For this \npurpose a method for model based predictive stability analysis \nhas been worked out based on Lyapunov’s stability analysis of \nsimulated state trajectories. The proposed algorithm can be \napplied to explore the stable and unstable operating regimes of \na process (set of safe states), information that can be used for \nPAM. 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