Predictive Water Monitoring for Hidden Contaminant Detection
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Solution Overview
Problem
Existing water quality monitoring systems are inadequate in detecting contaminants that do not alter the appearance or aroma of water, leading to unnoticed contamination in distribution systems and fixtures, and filtration systems may not be effective or feasible in all setups.
Innovation Solution
A predictive water condition monitoring system using a network of water sensors and a cognitive engine for machine learning to establish a baseline model, detect contamination patterns, and alert devices when contaminant levels exceed thresholds, integrating with cloud computing for wide-area coverage and data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional water quality monitoring methods are used, then the system is simple and easy to operate, but it cannot detect contaminants that do not alter appearance or aroma
Solution Approach 1:
The monitoring system is divided into multiple independent sensor nodes distributed throughout the water distribution system. Each sensor monitors local water conditions and transmits data to a central platform, enabling comprehensive coverage without requiring a single complex centralized system.
Solution Approach 2:
A cognitive engine acts as an intermediary between raw sensor data and actionable insights. The cognitive engine processes sensor readings, establishes baseline water conditions, detects anomalies, and generates alerts, thereby simplifying the overall system architecture while enhancing detection precision.
2Reliability
If filtration systems are installed to capture contaminants, then some contaminants can be removed, but filters are not fully effective and may not be feasible in all water distribution setups
Solution Approach 1:
The sensor-based monitoring system is designed to be universally applicable across different water distribution configurations including municipal systems, private wells, and building-level distributions. The same core technology adapts to various contexts without requiring physical filtration infrastructure.
Solution Approach 2:
The patent replaces mechanical filtration systems with an electronic sensing and cognitive analysis system. Instead of physically removing contaminants through filters, the system electronically monitors water conditions and predicts contamination events, eliminating the need for physical filtration infrastructure.
3Measurement precision
If water sensors are monitored continuously to establish baseline and detect conditions, then prediction accuracy improves, but energy consumption and data processing requirements increase
Solution Approach 1:
The system implements periodic monitoring intervals rather than continuous sampling. Sensors collect data at optimized intervals based on historical patterns and detected anomalies, reducing energy consumption while maintaining prediction accuracy through strategic sampling moments.
Solution Approach 2:
The cognitive engine autonomously adjusts monitoring intensity based on detected patterns and anomalies. During normal conditions, monitoring operates at lower intensity to conserve energy, while automatically increasing sampling frequency when potential contamination events are detected, making the system self-regulating.
Data Source
AI summary
Techniques for predictive water condition monitoring are described herein. An aspect includes a method that includes monitoring, by one or more processors, at least one water sensor to establish a baseline of a water condition model and monitoring one or more water conditions. A predicted water condition is determined based on the water condition model and the one or more water conditions. An alert is transmitted to one or more devices based on determining that the predicted water condition indicates a predicted contaminant level above a threshold.