A recent study by Pariyani et al. (in press) introduced a new approach for identifying near-misses in chemical plants. Abnormal events, which occur when plant variables depart from and return to their normal operating ranges, are recognized as near-misses, as they are precursors to incidents. In this paper, a new technique involving near-miss utilization and management is proposed to identify escalations in the probabilities of the occurrence of the incidents, particularly shutdowns, and to flag alert signals – permitting operators to be forewarned of major incidents likely to occur in the near future. Also, the alerts detect the onset and/or presence of inherent faults, or special-causes, likely to lead to most-critical abnormal events and trips. As an example, consider a chemical process in normal operation with few variables out of their normal operating ranges. Shortly after a disturbance, a high frequency of its variables move out of their normal operating ranges, creating a flood of alarms. For such a dynamic process, machine learning techniques (involving support vector machines) are used to: (a) track patterns of absolute increases in abnormal events in real-time by groups of variables, and (b) detect gradual shifts in various performance indicators introduced by Pariyani et al. (in press), and flag alerts to forewarn the operators of potential undesirable events several hours before they occur. These techniques have been targeted to minimize the number of false-positives (alerts having lead-times greater than 24-48 hours) and false-negatives (undetected incidents) – providing a more proactive warning system, leading to reduced risk levels.
Also, when creating an alert, as new abnormal events are recorded, ASPEN DYNAMICS simulations are used to identify the special-causes leading to the abnormal events underlying the alert. Then, a novel moving-horizon predictive method is introduced to predict the lead-times prior to incidents. The results and conclusions are presented for a case study involving an industrial-scale air-separation plant.
1. Pariyani A., Seider W. D., Oktem U. G., and Soroush M., “Incident investigation and dynamic analysis of large alarm databases in chemical plants: A fluidized-catalytic-cracking-unit case study,” Ind. Chem. Eng. Res., in press.
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