Method and apparatus for determining a quantity of a liquid in an oscillatingly suspended container
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Solution Overview
Problem
Existing methods for determining the quantity of liquid in oscillatingly suspended containers, such as those in laundry treatment machines, are laborious, expensive, or inaccurate, leading to variances in liquid application that can result in underdosing or overdosing of cleaning products and inefficient energy use.
Innovation Solution
A method using a combination of sensors to acquire operating data, including an oscillation sensor, current sensor, speed sensor, pressure sensor, and stopwatch, which are connected to a correlation unit trained with learning data to determine the liquid quantity accurately, allowing integration into the liquid application process to optimize it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If flow sensors, pressure sensors, or valve timing measurements are used to determine liquid quantity, then measurement capability is provided, but the method becomes laborious, expensive, or inaccurate
Solution Approach 1:
The patent creates a virtual model that copies the physical container system, including its dynamics and liquid behavior. By training a neural network on data from the physical system, a digital twin is created that can predict liquid quantity from easily measurable operating parameters like motor current and oscillation characteristics, replacing complex physical measurement devices
Solution Approach 2:
The patent replaces mechanical measurement devices (flow sensors, pressure sensors) with a computational approach using neural networks. The system substitutes direct physical measurement with indirect inference through machine learning models that analyze motor current, oscillation data, and timing information to determine liquid quantity
2Reliability
If existing measurement methods are applied, then liquid quantity can be determined, but variance in determination leads to underdosing or overdosing of cleaning products and inefficient energy use
Solution Approach 1:
The system continuously monitors operating parameters (motor current, oscillation characteristics, timing data) and uses the trained neural network to provide real-time feedback on actual liquid quantity. This feedback loop enables dynamic adjustment of the liquid application process, ensuring consistent and accurate dosing while optimizing energy consumption
Solution Approach 2:
The patent changes the approach from direct physical measurement to using multiple indirect parameters (motor current, oscillation frequency, timing data) processed through a neural network. This multi-parameter approach with dynamic parameter adjustment during the washing cycle improves measurement reliability and process efficiency
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 precise determination of liquid quantity with minimal material and data processing, optimizing the liquid application process by adjusting the quantity, duration, and energy expenditure.
Implementation Method 1
an oscillation sensor (25) assigned to the container (3), which sensor measures oscillation signals generated by the rotating component (4)
Implementation Method 2
a pressure sensor (21) for measuring a hydrostatic pressure prevailing in the container (3)
Implementation Method 3
the rotating component (4) is driven by means of a electric motor (14) for rotating at a specific speed by a specific current flowing through the electric motor (14)
Data Source
AI summary
An apparatus carries out a method for determining a first quantity of a liquid in an oscillatingly suspended container. The liquid is supplied into the container with a supply valve and is discharged from the container with a discharging facility. Sensors are provided for continuously acquiring operating data of the container. A control facility is connected to the sensors for transmitting the operating data and controls a supply valve. The operating data is supplied to a correlation unit which on the basis of training data which is stored as learned correlations between the first quantity and the operating data, the correlation unit determines the first quantity from the operating data by the operating data being used as input variables for the correlation unit and the first quantity being an output variable of the correlation unit.