TURNING DATA INTO INFORMED DECISIONS
Things are not always as they appear, one slowly escalating parameter may not be problematic but combined with other factors, could lead to an incident. With EMOS® Advisory System, all plant parameters and relationships are monitored, covering all possible phases of electrolyser operation: from start-up/ filling, to operating at full load, and back to shutdown / draining.
If a situation occurs that could lead to an incident, operators are advised which action(s) to perform in order to reduce or eliminate the threat.
Key Features and Benefits
Fewer Unplanned Shutdowns
Actionable Insights to Prevent and Fix Cell Room Incidents
Shorter Downtime when Resolving Incidents
Root Cause Identification of Each Fault
Improved Operator Effectiveness and Efficiency
66 Detectable Hazard Types (Advisory AI)
Recommended Corrective Action for Each Incident
Reduced Training Time
Designed for the Chlor-Alkali Industry
Fewer Incidents Escalated to Shift Supervisors/Engineers
Covers All Operation Modes (Startup, Normal, Shutdown, etc.)
Advisory AI: Early Detection of Faults
EMOS® Advisory is offered in two product tiers: Advisory and Advisory AI. Leveraging machine learning, Advisory AI can detect faults hours before they occur by comparing, in real-time, the actual cell voltage with the predicted voltage, for the specific process conditions. Any important discrepancy between the two prompts an Advisory alarm, which enables operators to plan their intervention before an emergency shutdown is set off. This early detection of faults is unmatched in the industry!
Number of Faults Detected
Early Detection of Some Faults, Hours Before they Occur
EMOS® Voltage Measuring Hardware
EMOS® Advisory AI protects chlor-alkali plants against many faults such as brine impurities or insufficient electrolyte flow to an individual cell, which are impossible to uncover using other vendor equipment. Based on its unique experience of analyzing over 67 000 electrolyser cells worldwide, R2 has developed best-in-class proprietary algorithms that increases plant safety and decreases the number of unplanned shutdowns.