A data-driven approach to chemical process alarm threshold optimization
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.
process alarmsthresholdsoptimizationkernel density estimation
国家科技期刊平台
NSTL
万方数据
维普
