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Use case
The Challenge
Each fluidised bed burner is fed by two paired conveyor belts (four per furnace). When one conveyor stops, the paired belt compensates by increasing speed. However, this operational behavior was not structured in a way that allowed systematic analysis.
To optimize burner runtime (typically 6–9 months) and understand deviations, the plant needed to:
- Distinguish between four operational states (none running, left only, right only, both running)
- Capture runtime patterns over long periods
- Quantify how conveyor configurations influence burner performance and lifetime
- Enable event counting and duration analysis
Without structured state logic, conveyor behavior could not be linked to burner efficiency or maintenance planning.
The Approach
A runtime classification framework was implemented using analog speed tags and formula logic:
- Analog speed tags (0–100%) were used instead of discrete ON/OFF signals to ensure formula compatibility
- A custom formula was created to categorize four operating states: (0 → Both conveyors not running; 1 → Only left conveyor running; 2 → Only right conveyor running; 3 → Both conveyors running)
- Conditional logic was applied to detect speed thresholds and assign the appropriate state
- Value-Based Searches were prepared for each operational case
- Context items were generated to annotate runtime configurations
- Monitoring logic was prepared to track future state transitions automatically
This converted raw speed data into structured operational intelligence.
The Results
The Takeaway
By transforming conveyor speed data into a structured runtime state indicator, the plant gained full visibility into feed configurations and their operational impact. This enables improved comparison of burner running times, identification of imbalance patterns, better maintenance planning, and a scalable framework for feed-system performance analysis.
The Challenge
Each fluidised bed burner is fed by two paired conveyor belts (four per furnace). When one conveyor stops, the paired belt compensates by increasing speed. However, this operational behavior was not structured in a way that allowed systematic analysis.
To optimize burner runtime (typically 6–9 months) and understand deviations, the plant needed to:
- Distinguish between four operational states (none running, left only, right only, both running)
- Capture runtime patterns over long periods
- Quantify how conveyor configurations influence burner performance and lifetime
- Enable event counting and duration analysis
Without structured state logic, conveyor behavior could not be linked to burner efficiency or maintenance planning.
The Approach
A runtime classification framework was implemented using analog speed tags and formula logic:
- Analog speed tags (0–100%) were used instead of discrete ON/OFF signals to ensure formula compatibility
- A custom formula was created to categorize four operating states: (0 → Both conveyors not running; 1 → Only left conveyor running; 2 → Only right conveyor running; 3 → Both conveyors running)
- Conditional logic was applied to detect speed thresholds and assign the appropriate state
- Value-Based Searches were prepared for each operational case
- Context items were generated to annotate runtime configurations
- Monitoring logic was prepared to track future state transitions automatically
This converted raw speed data into structured operational intelligence.
The Results
The Takeaway
By transforming conveyor speed data into a structured runtime state indicator, the plant gained full visibility into feed configurations and their operational impact. This enables improved comparison of burner running times, identification of imbalance patterns, better maintenance planning, and a scalable framework for feed-system performance analysis.
Access now
Share with a co-worker
The Challenge
Each fluidised bed burner is fed by two paired conveyor belts (four per furnace). When one conveyor stops, the paired belt compensates by increasing speed. However, this operational behavior was not structured in a way that allowed systematic analysis.
To optimize burner runtime (typically 6–9 months) and understand deviations, the plant needed to:
- Distinguish between four operational states (none running, left only, right only, both running)
- Capture runtime patterns over long periods
- Quantify how conveyor configurations influence burner performance and lifetime
- Enable event counting and duration analysis
Without structured state logic, conveyor behavior could not be linked to burner efficiency or maintenance planning.
The Approach
A runtime classification framework was implemented using analog speed tags and formula logic:
- Analog speed tags (0–100%) were used instead of discrete ON/OFF signals to ensure formula compatibility
- A custom formula was created to categorize four operating states: (0 → Both conveyors not running; 1 → Only left conveyor running; 2 → Only right conveyor running; 3 → Both conveyors running)
- Conditional logic was applied to detect speed thresholds and assign the appropriate state
- Value-Based Searches were prepared for each operational case
- Context items were generated to annotate runtime configurations
- Monitoring logic was prepared to track future state transitions automatically
This converted raw speed data into structured operational intelligence.
The Results
The Takeaway
By transforming conveyor speed data into a structured runtime state indicator, the plant gained full visibility into feed configurations and their operational impact. This enables improved comparison of burner running times, identification of imbalance patterns, better maintenance planning, and a scalable framework for feed-system performance analysis.
Access now
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