In order to improve the performance of chemical process alarm systems

it is imperative to optimize assignments of process alarm thresholds.In response to limitations of traditional threshold assignment methods

based on historical data

this paper firstly invokes kernel density estimation methods to identify process alarm states before an objective associated with alarm threshold optimization in terms of minimizing the probabilities of false and missed alarms is established along with enabling numerical solvers.Simulation results on TE process demonstrate that the proposed approaches can effectively reduce the number of false alarms as well as limit that of missed alarms.