Smart Filter Container Sensing for Accurate Filter Replacement
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Solution Overview
Problem
Existing filtering containers lack an effective and integrated system for monitoring the flow of liquids and determining when filters need replacement, as they often rely on inaccurate methods such as time-based notifications or vertical capacitive sensor strips that can contaminate water and require complex calibration.
Innovation Solution
A network of smart filtering containers with horizontal capacitive sensor strips that measure liquid flow and volume changes over time, allowing for accurate calculation of flow metrics like flow rate and cumulative volume, and a centralized system for data analysis to determine filter replacement needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vertical capacitive sensor strips are used to monitor water level, then measurement capability is improved, but water contamination and sensor degradation occur
Solution Approach 1:
The sensor system is divided into two separate components: horizontal sensor strips mounted on the exterior of the hopper for measurement, and interior sensor strips for flow detection. This segmentation allows the exterior strips to remain isolated from water contamination while still providing accurate water level measurements through capacitive coupling with the water inside the hopper.
Solution Approach 2:
The hopper wall acts as an intermediary medium that transmits capacitive signals from the water inside to the exterior sensor strips. This allows the sensors to measure water level without direct contact with the water, preventing contamination while maintaining measurement accuracy.
2Measurement precision
If vertical sensor strips are used inside the hopper, then water level sensing is improved, but system complexity increases due to housing requirements and feedthroughs
Solution Approach 1:
Instead of placing sensors inside the hopper as conventionally done, the sensor strips are inverted to the exterior surface of the hopper. This reversal eliminates the need for complex housing arrangements and electrical feedthroughs, while still enabling water level measurement through the hopper wall.
3Measurement precision
If vertical sensor strips are used, then water level measurement is achieved, but calibration complexity and sensitivity to position changes increase
Solution Approach 1:
The sensor configuration changes from vertical strips to horizontal strips, fundamentally altering the measurement parameter from continuous capacitance variation along a vertical axis to discrete capacitance measurements at specific horizontal levels. This parameter change simplifies calibration by reducing sensitivity to small position variations and environmental drift.
4Device complexity
If time-based notifications are used for filter replacement, then system simplicity is maintained, but notification accuracy deteriorates
Solution Approach 1:
The system continuously monitors water flow through the filter using sensor data and provides feedback to determine when the filter needs replacement. This feedback mechanism replaces time-based estimates with actual flow-based measurements, significantly improving notification accuracy while maintaining reasonable system complexity.
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
This solution provides a robust and accurate method for determining when filters need replacement, reducing contamination risks and eliminating the need for complex calibration, while enabling networked data analysis for improved filter management and water quality monitoring across multiple containers.
Implementation Method 1
one or more horizontal capacitive sensor strips may be proximal to the hopper... These strips may detect changes in liquid level in the hopper
Implementation Method 2
A processor may calculate one or more flow metrics based on the capacitance data, such as a flow rate through the filter, or a cumulative volume of liquid that has passed through the filter
Data Source
AI summary
System that monitors and manages a network of smart filtering containers, such as water pitchers with integrated sensors. Measurements are collected from the containers and forwarded to a centralized system, such as an Internet server; data may be analyzed to determine performance modifications or recommendations for selected containers. Centralizing the data enables discovery of patterns and correlations across containers; for example, abnormal measurements from multiple pitchers in an area may suggest contamination of the area's water supply. The centralized system may automatically update settings of containers to optimize their performance. It may send messages to users suggesting different usage patterns or configurations. It may automatically order components such as replacement filters or upgrades. A water testing capability may also be provided; users may be sent water test strips that can be imaged using an associated mobile device app, and results may be forwarded to the central database for analysis.