Predictive Alarm Handling for Dynamic Process Setpoints
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Solution Overview
Problem
Existing alarm handling systems in plant process automation do not dynamically adapt alarm setpoints to the actual rate of change of process variables, leading to inefficient and unrealistic alarm assessments.
Innovation Solution
An alarm handling system that includes a data processing device to determine the current rate of change of process variables, predict when setpoints will be reached, and provide dynamic feedback using linear or complex prediction models, with configuration parameters derived from historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alarm setpoints are calculated based on worst case scenario with maximum alternation rate, then operator safety is ensured, but alarm handling efficiency deteriorates due to unrealistic alarm assessments
Solution Approach 1:
The patent applies dynamics by transitioning from static alarm setpoints (based on fixed worst-case assumptions) to dynamic alarm setpoints that adapt in real-time to actual process conditions. The system continuously monitors the alternation rate of process variables and adjusts alarm setpoints accordingly, making the alarm system both safe and efficient by matching the dynamic nature of actual industrial processes.
Solution Approach 2:
The patent changes the parameter of alarm setpoint calculation from fixed worst-case values to variable values that depend on actual process alternation rates. By monitoring the rate of change of process variables and using this information to dynamically adjust alarm setpoints, the system maintains safety while improving alarm handling efficiency through more realistic thresholds.
2Reliability
If alarm setpoints are fixed based on maximum alternation rate, then safety margin is maintained, but adaptability to actual process conditions deteriorates
Solution Approach 1:
The patent implements feedback by continuously monitoring actual process variable alternation rates and using this information to adjust alarm setpoints in real-time. The system creates a closed-loop control where alarm thresholds adapt based on feedback from actual process behavior, maintaining safety margins while becoming adaptable to real operating conditions rather than relying on fixed worst-case assumptions.
3Measurement precision
If prediction device uses complex prediction models, then prediction accuracy improves, but device complexity increases
Solution Approach 1:
The patent applies partial action by implementing a tiered prediction approach where simple linear extrapolation is used as the baseline method, and more complex prediction models (such as Hidden Markov Models) are optionally applied based on specific needs. This allows the system to achieve sufficient prediction accuracy for most applications while avoiding unnecessary complexity, and only invoking advanced models when the situation warrants higher precision.
Data Source
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AI summary
Alarm handling method and system in plant process automation Alarm handling method and system in plant process automation including a data processing device comprising • · at least one interface (10), accessing or processing one or more process signals and determining corresponding process variables (24), • · an alarm configuration device (20), accessing or providing alarm configuration information comprising at least one setpoint for one or more determined process variables, • · a prediction device (30) determining and processing the current rate of change of at least one process variable to predict how long it will take or predict at which time a provided setpoint or threshold, in particular a predefined setpoint or threshold and in particular a consequence threshold (28), is reached or crossed, or determines whether and when at least one of the monitored or determined process variable values will cross the respective setpoint, in particular the alarm setpoint (26), for example when indicating a return-to-normal scenario.