An enhanced advisory system to improve situational awareness and abnormal situation management

Abstract

To illustrate how an advisory system developed on chaining of failures that lead to accidents could be produced and doing so should help increase process operator's Situational Awareness (SA) and performance during abnormal operations. Background: In the process industries failure to manage process disturbances may end inacincidents, mainly when the operator is flooded with information that can cause a loss of situational awareness or poor decision-making. Advisory systems aim to increase the perception of the current state of operations and to reduce decision-making error through providing supportive information about how to predict and manage the abnormal events. Methods: The proposed advisory system is designed based on the enhanced causal modeling with correlation and directed graphs. In order to develop an advisory system, first, the abnormal events are identified and modeled with a causal relationship. Then, the model is converted to a directed graph, and a sequencing technique is implemented to study the predecessor and successor of each abnormal event. The study is benchmarked on the Tennessee Eastman Process (TEP) model. Contribution: The research contribution is to develop the idea of an intelligent advisory system based on the footpath of abnormal events to support operator decision-making and increase situational awareness. Such an advisory system can support an operator through the application of real-time event analysis and data-processing, and it can support the decision-making process through the provision of accurate data related to the current state of operation.