Sensor-Dry Control for Small-Load Clothing Dryers
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Solution Overview
Problem
Conventional clothing dryers face inefficiencies when drying small loads, leading to incomplete drying due to degraded temperature control and sensing inaccuracies, resulting in user errors such as excessive or incomplete drying.
Innovation Solution
A clothing dryer with a dry tub, first and second selecting units, and a control unit that allows users to select a small load option, adjusting the drying algorithm to include adapted temperature, time, and operation rate based on the load size, using sensors to measure dryness and adjust accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a conventional sensor-dry course algorithm is used for small loads, then the dryer operates with standard temperature and time settings, but the drying effectiveness deteriorates due to degraded temperature control and sensing inaccuracies
Solution Approach 1:
The control unit dynamically adjusts drying parameters including temperature, time, and operation rate based on the detected load size. For small loads, the system modifies the course algorithm to use adapted temperature and time settings, ensuring effective drying despite sensing inaccuracies. This dynamic adaptation resolves the contradiction by making the system responsive to load conditions rather than using fixed conventional algorithms.
Solution Approach 2:
The system changes key drying parameters (temperature, time, operation rate) based on load size detection. When a small load is detected, the control unit applies a modified course algorithm with adjusted parameters specifically suited for small loads, thereby maintaining drying effectiveness despite the inherent limitations of conventional sensing accuracy.
2Adaptability or versatility
If a manual dry course is used to avoid conventional algorithm limitations, then users can set custom time and temperature, but user error increases leading to excessive or incomplete drying
Solution Approach 1:
The system provides self-service by automatically detecting load size and selecting appropriate course algorithms. Users simply input the substance type and load size, and the control unit autonomously determines the optimal drying parameters. This eliminates user error while maintaining adaptability, as the system serves itself by making intelligent decisions based on sensor data and pre-programmed algorithms for different load conditions.
Solution Approach 2:
The control unit incorporates feedback mechanisms that monitor drying progress and adjust parameters accordingly. By continuously sensing drying conditions and comparing them against target values, the system can automatically correct deviations, ensuring reliable drying outcomes without requiring manual intervention or user expertise.
3Productivity
If the course algorithm is adapted for small loads by changing temperature, time, and operation rate, then drying efficiency improves, but the control system complexity increases
Solution Approach 1:
The control system segments the drying process into distinct course algorithms based on load size categories (small load, normal load, large load). Each segment has pre-configured optimal parameters, simplifying the overall control structure. Rather than implementing a single complex continuous adjustment system, the patent divides the control space into manageable segments, each handling specific load conditions with dedicated algorithms.
Solution Approach 2:
While the system dynamically adapts to different load sizes, it does so by selecting from a finite set of pre-defined course algorithms rather than continuously generating new parameter combinations. This dynamic selection approach maintains drying efficiency across varying loads while keeping the control logic manageable through algorithm selection rather than complex real-time calculation.
Data Source
AI summary
A clothing dryer capable of effectively drying a small amount of substance, and a control method thereof by changing an algorithm of a sensor-dry course, which is configured to control an operation rate, a degree of drying or a temperature of drying, to be adapted to the small load of substance in a case where the substance to be dried has a small load when compared to the entire volume of the clothing dryer, so that the drying efficiency is enhanced regardless of the load of the substance, and a separate option button is provided for a sensor-dry course, so that a small load of substance is dried adaptively to the material characteristic of the substance for a respective dry course, thereby enhancing the drying efficiency.