Multi-Zone Sensor System for Predicting Mold Growth in Hidden Areas
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Solution Overview
Problem
Existing systems fail to detect mold growth in hidden areas of homes until it becomes visible, and they lack the ability to predict mold growth likelihood and timeframe, leading to costly remediation and health risks.
Innovation Solution
A system with multiple sensors placed in overlooked locations like attics and HVAC systems to monitor temperature, humidity, and volatile organic compounds (VOCs), using prediction algorithms and digital fingerprints to detect mold early and provide risk notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are placed in hidden areas to detect mold early, then detection capability is improved, but device complexity increases
Solution Approach 1:
The system divides the home into multiple monitoring zones (attic, crawl space, HVAC system, living spaces) with separate sensors in each location. Each sensor independently monitors its local environment, and the system aggregates data from all zones to provide comprehensive mold detection coverage.
Solution Approach 2:
The patent introduces volatile organic compound (VOC) sensors as intermediary detectors that can sense mold-related chemical signatures before visible mold growth occurs. These VOC sensors act as early warning indicators, detecting molecular evidence of mold metabolism in hidden areas before spores become visible.
2Measurement precision
If multiple sensors are deployed to monitor hidden areas, then detection coverage is improved, but cost increases
Solution Approach 1:
The system employs multi-functional sensors that simultaneously monitor multiple parameters including temperature, humidity, and VOC concentrations. This allows a single sensor deployment to serve multiple detection purposes, reducing the total number of devices needed while maintaining comprehensive monitoring coverage across all hidden areas.
3Loss of time
If prediction algorithms are implemented to forecast mold growth, then proactive remediation capability is improved, but computational requirements increase
Solution Approach 1:
The system pre-calculates and stores mold growth prediction models and algorithms in its database before actual mold detection is needed. When sensor data is collected, the system retrieves pre-prepared prediction algorithms that quickly process the data to forecast mold growth timing and locations, avoiding the need for complex real-time computations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables early detection and prediction of mold growth, allowing proactive remediation, reducing costs and health risks, and maintaining HVAC efficiency by identifying conditions conducive to mold growth.
Implementation Method 1
a first sensor disposed in said housing and in fluid communication with the air intake passage, the first sensor being capable of detecting the concentration of volatile organic compounds given off by mold
Data Source
AI summary
The present disclosure provides a system for detecting and predicting mold growth comprising a housing having at least one air intake passage defined in the housing; a first sensor disposed in said housing and in fluid communication with the air intake passage, the first sensor being capable of determining the temperature and humidity of the surrounding air; a database containing a plurality of mold growth predication algorithms, each being associated with a predefined temperature range; a computer system in communication with the first sensor and being adapted to receive from the first sensor a temperature reading and a humidity reading; retrieve from the database one of the mold growth prediction algorithms that is associated with the predefined temperature range in which the temperature reading falls; input into the retrieved mold prediction algorithm the humidity reading and calculate a value representing the number of days until mold growth occurs; and create a user notification representing a risk level associated with the likelihood of mold growth occurring.