Multivariate alarm systems to recognize rare unpostulated abnormal events

Abstract

Most chemical and manufacturing plants have safety/reliability systems in place that are well equipped to handle commonly occurring postulated abnormal events, but often prove to be ineffective in predicting highly unanticipated and randomly occurring unpostulated abnormal events. In this paper, an advisory system is developed to analyze and recognize such events, consisting of novel, multivariate alarm systems and response actions introduced using process modeling and path‐sampling for unpostulated abnormal events. It augments existing safety/reliability systems, suggesting actions when unanticipated abnormal events are approached. Forward‐flux sampling (FFS), developed to discover rare molecular dynamics pathways, is applied. With an approximate process model, for an exothermic continuous‐stirred tank reactor using a perturbed feed concentration, the FFS algorithm is applied to identify rare trajectories between high‐conversion and low‐conversion steady‐states, with key process variables saved at various “crossing points.” Then, committer probabilities, , are computed at each crossing point, yielding a mathematical model that expresses as a function of the key process variables; that is, the reactor temperature (T), cooling‐water flow rate (), and cooling‐water temperature (), selected as choices for the primary alarm variables. For these, alarm thresholds; that is, L—low, LL—low low, and LLL—low low low, are suggested by computing their critical ranges, given the ranges for every alarm threshold.