Automated Wetstock Monitoring for Fuel Leak and Excess Detection
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Solution Overview
Problem
Manual wetstock management in fuel storage facilities is error-prone, leading to potential catastrophic consequences such as environmental contamination, revenue loss, and public health risks due to delayed detection and resolution of operational issues like fuel leaks and excesses.
Innovation Solution
An automated wetstock management system utilizing sensors, a wetstock management server, and AI/machine learning to process fuel data, detect anomalies, generate workflows, and send alerts through selected communication channels, thereby enabling timely and efficient corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring of fuel stock measurements is used, then operational flexibility and human judgment can be applied, but error rates increase and detection speed decreases leading to potential catastrophic consequences
Solution Approach 1:
The patent replaces manual mechanical monitoring with an automated electronic system that collects data from sensors (ATGs, leak detection sensors, magnetostrictive probes) and processes measurements automatically. This substitution eliminates human error while maintaining continuous monitoring capability, directly improving both detection accuracy and response time.
Solution Approach 2:
The system enables self-service through automated anomaly detection and workflow generation. When measurements indicate abnormal events (fuel losses, excesses, tank defects), the system automatically evaluates the data, identifies issues, and generates corrective action workflows without requiring constant human intervention, thereby improving reliability and reducing response time.
2Productivity
If automated processing of fuel data is implemented, then detection speed and consistency improve, but system complexity increases
Solution Approach 1:
The automated system is designed to handle multiple sensor types (ATGs, leak detection sensors, magnetostrictive probes) and various measurement parameters through a single unified platform. This multi-functional approach improves processing speed and consistency while managing complexity through integration rather than separate systems for each sensor type.
Solution Approach 2:
The system introduces an intermediary processing layer that collects data from diverse sensors, applies evaluation criteria, and generates standardized workflows. This intermediary layer simplifies the overall system architecture by providing a consistent interface between sensor inputs and corrective actions, managing complexity while maintaining high processing speed.
Data Source
AI summary
An automated wetstock management system can include a plurality of sensors disposed in a fuel storage facility, the plurality of sensors configured to sense fuel data characterizing one or more aspects of the fuel storage facility, and a wetstock management server communicatively coupled to the plurality of sensors. The wetstock management server can process the fuel data to detect whether the fuel data satisfies an exception indicative of an operational issue of the fuel storage facility based on one or more predefined rules or models stored in the wetstock management server. In some embodiments, the wetstock management server can generate a workflow for assisting a user of the fuel storage facility to resolve the operational issue. In some embodiments, the wetstock management server can assign a risk category to the exception and electronically transmit an alert characterizing the operational issue to the user.