Virtual Dissolved Oxygen Sensor Monitoring for Aquaculture Drift
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Solution Overview
Problem
Conventional sensors for measuring dissolved oxygen in aquaculture farms have high installation costs, complex maintenance, and difficulty in rapid response to failures or errors, leading to reduced operational efficiency due to limitations in real-time data accuracy.
Innovation Solution
A virtual sensor performance monitoring device using an anomaly detection model based on an artificial neural network with an autoencoder to predict dissolved oxygen levels, which updates based on aquaculture farm environment data to maintain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensors are used to measure dissolved oxygen, then real-time data can be obtained, but installation costs are high and maintenance is complex
Solution Approach 1:
The patent creates a virtual sensor that copies the measurement function of conventional dissolved oxygen sensors by using machine learning models to predict dissolved oxygen levels based on environmental parameters (temperature, pH, salinity, flow rate) collected from other sensors, thereby eliminating the need for expensive and complex physical oxygen sensors while maintaining measurement capability
Solution Approach 2:
The patent replaces the mechanical/physical sensor system with an information-processing system consisting of machine learning models and data processing algorithms that compute dissolved oxygen levels from environmental parameter data, substituting physical measurement mechanisms with computational methods
2Measurement precision
If conventional sensors are used, then dissolved oxygen data can be measured, but response time to failures or errors is slow
Solution Approach 1:
The patent implements preliminary action by continuously monitoring multiple environmental parameters (temperature, pH, salinity, flow rate) that influence dissolved oxygen levels, so when a failure occurs, the system can immediately recalculate predictions using updated parameter values without waiting for sensor replacement or repair
Solution Approach 2:
The patent establishes a feedback mechanism where the virtual sensor continuously compares predicted dissolved oxygen values with actual measurements from periodic physical sensor calibrations, automatically detecting discrepancies and triggering model recalibration to maintain accuracy and respond rapidly to any failures or drift
3Device complexity
If virtual sensor technology is used to predict dissolved oxygen, then installation costs are reduced and maintenance is easier, but model accuracy may decrease when environment changes
Solution Approach 1:
The patent applies dynamics by making the virtual sensor model adaptive and dynamic rather than static, allowing the machine learning models to continuously learn from new data and adjust their parameters automatically as environmental conditions change, ensuring sustained prediction accuracy throughout the system's operation
Solution Approach 2:
The patent utilizes parameter changes by monitoring variations in environmental parameters (temperature, pH, salinity, flow rate) and using these changes to trigger model recalibration or retraining, adjusting the virtual sensor's internal parameters to match new environmental conditions and maintain prediction accuracy
4Productivity
If virtual sensors are deployed in flow-through aquaculture farms, then operational efficiency can be improved, but models need continuous updates to match changing environments
Solution Approach 1:
The patent implements self-service by enabling the virtual sensor models to automatically update themselves through continuous learning from incoming environmental parameter data, with the system autonomously detecting when recalibration is needed and executing model updates without requiring manual intervention, thus maintaining adaptability to environmental changes while supporting operational efficiency
Data Source
AI summary
The present invention relates to a virtual sensor performance monitoring device. The device includes a sensor, and a processor configured to monitor performance of a virtual sensor that predicts an amount of dissolved oxygen in an aquaculture farm on the basis of aquaculture farm environment data collected through the sensor.