Fuel Dispenser Meter Drift Detection Using Fluid Balance Models
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Solution Overview
Problem
Fuel dispensers at fueling stations experience meter drift between calibrations, leading to inaccurate fuel dispensing, which can result in customer or station losses, inventory discrepancies, environmental contamination, and regulatory non-compliance, with current monitoring systems failing to detect these issues in a timely manner.
Innovation Solution
A system and method for real-time determination of meter drift using predictive modeling and physics-based fluid balancing, incorporating sensors and data processing to estimate and quantify meter drift through a fluid balance model and error correction, enabling continuous monitoring and notification of deviations from predetermined calibration parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If annual or periodic calibration of flow meter is used, then manufacturing precision is improved, but reliability deteriorates due to meter drift between calibrations
Solution Approach 1:
The system performs preliminary detection of meter drift conditions by continuously monitoring fuel storage data and comparing actual fuel levels against expected levels based on dispensing transactions. This early detection allows for identifying calibration issues before they result in significant measurement errors, effectively performing the calibration function in advance rather than waiting for annual schedules.
Solution Approach 2:
The system establishes a feedback loop by continuously monitoring fuel storage facility data, comparing actual fuel levels with expected levels calculated from dispensing transactions, and generating alerts when discrepancies indicate meter drift. This closed-loop feedback enables real-time detection and response to calibration degradation, maintaining reliability between periodic calibrations.
2Device complexity
If conventional monitoring systems are used, then device complexity is reduced, but loss of information increases due to delayed detection of meter drift
Solution Approach 1:
The system uses existing fuel storage facility sensors and data infrastructure for multiple purposes: traditional fuel level monitoring, inventory management, and now also meter drift detection. By making the monitoring system multi-functional, it detects meter drift information without adding significant complexity, as the same sensors and data collection mechanisms serve both traditional and new detection purposes.
Solution Approach 2:
The system introduces an intermediary data processing layer that analyzes fuel storage data to detect meter drift conditions. This intermediary layer processes existing sensor data and transaction information to extract calibration status information, acting as a mediator between the existing monitoring infrastructure and the calibration management function, thereby detecting meter drift without requiring direct additional sensing hardware.
3Reliability
If real-time detection of meter drift is implemented, then reliability is improved, but device complexity increases due to predictive modeling requirements
Solution Approach 1:
The system performs self-service by using its own operational data (fuel dispensing transactions and storage level measurements) to detect its own calibration status. The fuel dispenser system monitors its own meter performance through the fuel storage facility data, eliminating the need for external complex monitoring equipment. The system essentially detects its own meter drift conditions using internally generated data.
Solution Approach 2:
The system detects meter drift by monitoring changes in the relationship between expected fuel levels (calculated from dispensing transactions) and actual fuel levels (measured by storage sensors). Instead of directly measuring meter calibration parameters, it infers drift conditions from changes in fuel balance parameters over time, using parameter changes as indicators of calibration degradation without requiring direct calibration measurement equipment.
4Manufacturing precision
If frequency of meter calibration is increased, then manufacturing precision is maintained, but loss of time increases due to more frequent calibration interruptions
Solution Approach 1:
The system performs preliminary detection of meter drift conditions through continuous monitoring of fuel storage data and comparison with expected levels. By detecting calibration degradation early, before it reaches critical thresholds, the system allows for planned, minimal-intervention calibration events rather than emergency calibrations or frequent preventive calibrations, reducing overall calibration downtime while maintaining accuracy.
Solution Approach 2:
The continuous feedback mechanism provides real-time information about meter performance, allowing calibration activities to be performed only when actually needed rather than on fixed schedules. This demand-driven calibration approach, enabled by feedback monitoring, reduces unnecessary calibration interruptions while ensuring calibration is performed promptly when drift is detected, optimizing the balance between precision and operational time.
Data Source
AI summary
In one aspect, data characterizing a fuel storage facility can be received from a sensor in operable communication with the fuel storage facility. An estimate of meter drift of a flow meter of a fuel dispenser in fluid communication with the fuel storage facility can be determined based on the received data. The estimate of meter drift can be determined based on at least one predictive model that predicts whether a calibration parameter characterizing a calibration of the flow meter has deviated from a predetermined flow meter calibration parameter. The estimate of meter drift can be provided.