Wetstock Monitoring with AI Anomaly Detection and Risk Workflows
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Solution Overview
Problem
Traditional wetstock management relying on manual monitoring of sensor data is error-prone, leading to potential catastrophic consequences such as environmental contamination, loss of revenue, and public health risks due to undetected operational issues in fuel storage facilities.
Innovation Solution
An automated wetstock management system using sensors, a wetstock management server, and AI/ML techniques to process fuel data, detect anomalies, assign risk categories, and generate workflows for resolving issues, with real-time notifications and learning capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring of sensor data is used, then operational simplicity is maintained, but detection reliability deteriorates due to human error
Solution Approach 1:
The system enables automated self-monitoring of wetstock conditions through sensors and AI/ML algorithms that automatically detect anomalies, assign risk categories, and generate workflows without human intervention, eliminating human error while maintaining operational simplicity
Solution Approach 2:
The patent replaces manual mechanical monitoring with an automated electronic system comprising sensors, servers, and software algorithms that process sensor data and generate alerts, substituting human operators with intelligent automated detection mechanisms
2Measurement precision
If automated anomaly detection is implemented, then detection precision is improved, but device complexity increases due to multiple sensors and processing systems
Solution Approach 1:
The wetstock management server performs multiple functions including data collection, AI/ML-based anomaly detection, risk category assignment, workflow generation, and notification delivery, consolidating diverse operations into a single multi-functional platform that reduces overall system complexity
Solution Approach 2:
The patent introduces a wetstock management server as an intermediary component that bridges sensors and users, centralizing data processing and decision-making logic to manage complexity while improving detection precision through sophisticated AI/ML algorithms
3Speed
If real-time automated monitoring is deployed, then response speed is improved, but energy consumption increases due to continuous sensor operation and data processing
Solution Approach 1:
The system employs periodic sampling of sensor data rather than continuous monitoring, with the wetstock management server collecting and processing data at optimized intervals, maintaining rapid response capability while reducing energy consumption from constant sensor operation and data transmission
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.