Condition-Based Monitoring for Tank Farms
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Solution Overview
Problem
Current manual maintenance scheduling of tank farms is inefficient, leading to unnecessary shutdowns, potential human errors, and exposure to hazardous conditions, as it relies on fixed-cycle inspections that fail to detect underlying issues like corrosion and aging.
Innovation Solution
An online condition-based monitoring (CBM) system that uses diagnostic and predictive tank models based on historical and real-time data to generate failure indicia for scheduling maintenance tasks, minimizing disruptions and identifying potential problems before they become critical.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspections are performed on a fixed-cycle schedule, then safety and environmental compliance is ensured, but revenue is lost due to unnecessary shutdowns and operational interruptions
Solution Approach 1:
The patent transitions from static fixed-cycle inspection schedules to dynamic condition-based monitoring that continuously adapts maintenance scheduling based on real-time tank condition data, operational metrics, and predictive analytics, allowing inspections to occur only when actually needed
Solution Approach 2:
The system performs preliminary predictive analytics and risk assessments to identify potential tank failures before they occur, enabling proactive maintenance scheduling that prevents catastrophic failures while avoiding unnecessary shutdowns of healthy tanks
2Ease of operation
If human technicians perform manual inspections, then visual assessment of tank conditions is obtained, but human errors and limited detection capability occur
Solution Approach 1:
The patent replaces manual human inspection with automated sensor networks, data acquisition systems, and predictive analytics algorithms that continuously monitor tank conditions, eliminating human error and providing superior detection precision for both visual and non-visual defects
Solution Approach 2:
The system introduces intermediate sensors, data loggers, and predictive models that act as mediators between the tank and human operators, capturing and analyzing conditions that would be invisible or undetectable during manual inspections
3Measurement precision
If human technicians enter storage tanks for inspection, then direct visual assessment is possible, but exposure to toxic gases and hazardous conditions occurs
Solution Approach 1:
The patent employs sensors, robotic inspection devices, and remote monitoring systems as intermediaries to collect data from within tanks, eliminating the need for human technicians to enter hazardous environments while maintaining or improving inspection quality
Solution Approach 2:
The tank monitoring system performs self-inspection through embedded sensors and automated diagnostic capabilities, continuously assessing its own condition without requiring human entry or intervention
4Object-affected harmful factors
If safety equipment is worn during manual inspections, then technician protection is provided, but inspection capability and equipment usage is impaired
Solution Approach 1:
The system enables self-monitoring and self-diagnosis capabilities in tanks, eliminating the need for human technicians to wear safety equipment and perform risky inspections, while automated systems maintain full inspection capability without restriction
5Duration of action of stationary object
If fixed-cycle maintenance scheduling is used, then regular inspection intervals are maintained, but underlying non-visual factors like corrosion and aging are not detected
Solution Approach 1:
The patent replaces periodic manual inspection with continuous automated monitoring using sensors that detect non-visual parameters such as corrosion rates, material degradation, and structural integrity changes in real-time, providing superior detection capability regardless of inspection interval
Solution Approach 2:
The system implements continuous feedback loops where sensor data from operational conditions and environmental factors is constantly analyzed to detect early signs of corrosion and aging, triggering alerts before visible damage occurs
Data Source
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AI summary
A method for online condition-based monitoring (CBM) of a tank farm (115) including a plurality of storage tanks (120) includes providing a tank model (190) including a diagnostic (190(a)) and/or predictive tank model (190(b)) based on calculated tank metrics (195) that is derived from historical data including tank operational data (130). The calculated tank metrics include tank operational metrics (195(a)) based on tank operational data (130) for the storage tanks and tank condition metrics (195(b)) based on tank inspection or maintenance data for the storage tanks. The tank model provides relationships between the tank condition metrics and the tank operational metrics. Results are generated using the tank model including at least one failure indicia for at least a first of the storage tanks using the calculated tank metrics and current measured data for the first tank as inputs to the tank model. The failure indicia is processed for scheduling at least one maintenance task for the first tank.