Advanced real time alarm management

Our goal is to eliminate preventable XV valve closures, unplanned equipment shutdowns and unplanned facility outages/shutdowns caused by alarm flooding and alarm suppression. Our solution is to focus panel operators on looming events, increasing time to respond and by providing recommendations to prevent outages.

The recommendations are based on previous actions that were successful or unsuccessful in preventing an event.

Alarm rationalization and management of change

Many petrochemical facilities spend significant senior resources on alarm rationalization followed by the management of change process. Our goal is to increase the effectiveness of this work by increasing the efficiency of implementation. We do this through evidence based operating data, providing clear history and recommendations on nuisance alarms and nuisance trips.

Our systems aim to provide evidence needed to identify categorize to change or eliminate set points.



Background: Alarms in industry

No cost to add additional alarms in modern DCS systems and Regulations from EEMUA 191 and ISA - 18.2 have led to the addition of alarms, with little consideration of the effect on alarm loads at the system level. Even the skilled operators can’t respond to number of alarms on the spot. As a result, Abnormal Situations are costing industry millions of dollars every year. Our solution will address this issue; have the ways to reduce the operator loadings and improve plant profitability
Visualization and Analysis
Provide insight into alarm frequency, statistics, user acknowledgements, priority distributions and hidden correlations. The information will assist process engineer to identify alarm floods, classification of alarms and root cause of alarms.
Alarm rationalization and management of change
Many petrochemical facilities spend significant senior resources on alarm rationalization followed by the management of change process. Our goal is to increase the effectiveness of this work by increasing the efficiency of implementation. We do this through evidence based operating data, providing clear history and recommendations on nuisance alarms and nuisance trips. 
Our systems aim to provide evidence needed to identify categorize to change or eliminate set points.
Advanced real time alarm management
Plant-wide disturbances detection and diagnosis remain an off-line method so far and cannot utilize process information automatically. Our Artificial Intelligence learning based models is to eliminate preventable XV valve closures, unplanned equipment shutdowns and unplanned facility outages/shutdowns caused by alarm flooding. Our solution is to focus panel operators on looming events, increasing time to respond and by providing recommendations to prevent outages.
The recommendations are based on previous actions that were successful or unsuccessful in preventing an event.