Microwave Heating Device Adaptive Phase Control
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Solution Overview
Problem
Microwave heating devices face challenges in achieving reproducible and efficient heating of different food products due to varying interactions with microwaves, leading to uneven or inadequate heating results, as each product interacts uniquely with microwaves and positioning affects heating performance.
Innovation Solution
A microwave heating device with a control unit that adjusts frequency and phase shifts of microwaves using multiple radiating portions, employing a learning procedure to map energy efficiency and select optimal operational configurations for specific food products, allowing for adaptive heating based on the product's interaction with microwaves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a same operational configuration of microwave heating device is used for heating different products, then the device operation is simple, but the heating uniformity and efficiency deteriorate
Solution Approach 1:
The patent implements dynamic adjustment of microwave operational parameters (frequency, phase shifts, power levels) based on real-time detection of reflected power and product characteristics. The control unit continuously adapts the operational configuration during heating cycles, transforming the static microwave system into a dynamic one that responds to product-specific requirements, thereby achieving both ease of operation and heating uniformity
Solution Approach 2:
The patent employs a feedback mechanism where the reflected power detecting system monitors the interaction between microwaves and the product, and the control unit uses this information to adjust operational parameters. This closed-loop control ensures that the heating process adapts to different products automatically, maintaining heating uniformity without requiring manual intervention, thus resolving the contradiction between operational simplicity and heating precision
2Device complexity
If standard recipes are used for different product types, then the device complexity is reduced, but the heating efficiency and reproducibility deteriorate
Solution Approach 1:
The patent enables the microwave heating device to automatically characterize and adapt to different products through its learning procedure. The system performs self-testing by exposing the product to multiple microwave configurations, detecting reflected power patterns, and autonomously determining optimal heating parameters. This self-service capability eliminates the need for pre-programmed standard recipes while ensuring reliable and reproducible heating results for any product type
Solution Approach 2:
The patent systematically varies microwave operational parameters (frequency, phase shifts between radiating portions, power levels) during the learning procedure to map the product's interaction with microwaves. By changing these parameters and observing the effects on reflected power, the system builds a comprehensive understanding of product-specific heating characteristics, enabling reliable heating without relying on simplified standard recipes
3Manufacturing precision
If learning procedure with multiple operational configurations is performed, then heating efficiency and uniformity are improved, but the heating time and energy consumption increase
Solution Approach 1:
The patent performs the learning procedure and maps optimal operational configurations in advance, before actual heating cycles. The system stores this pre-acquired knowledge for future use, so that subsequent heating operations can directly apply the determined optimal parameters without repeating the time-consuming learning process. This preliminary action significantly reduces the time penalty associated with characterizing different product types
Solution Approach 2:
The patent implements a periodic learning schedule where the system automatically performs learning procedures for new product types and stores the results. During normal operation, the system retrieves pre-stored operational configurations, only initiating new learning procedures when encountering unrecognized products or at scheduled intervals. This periodic approach balances the need for accurate product characterization with the constraint of minimizing heating time
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
The adaptive algorithm ensures efficient and uniform heating by determining the best operational configurations for each food product, improving cooking performance and reducing the need for standardized recipes, suitable for various food types and fast food applications.
Implementation Method 1
heat is generated directly inside the food product by means of electromagnetic fields or electromagnetic radiations. Amongst these, some techniques use radio frequency (RF) dielectric heating and other techniques use microwaves (MW)
Implementation Method 2
a microwave generating system including at least two radiating portions adapted to radiate microwaves to the heating chamber
Data Source
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AI summary
This disclosure relates to a microwave heating device and a method for operating a microwave heating device. The microwave heating device comprises at least two radiating portions that are adapted to radiate microwaves to the heating chamber and can be operated according to operational configurations that differ in frequency and/or in phase shift(s) between the radiated microwaves. A learning procedure can be executed in relation with at least one product positioned in the heating chamber. The learning procedure can be executed by changing frequency and phase shift(s) to sequentially operate the at least two radiating portions in a plurality of operational configurations, in such a way that, for each frequency, the at least two radiating portions are operated in a number of operational configurations that differ in phase shift(s) from one another. An energy efficiency can be calculated for each of said plurality of operational configurations and the obtained data are saved. A heating procedure may be executed after the learning procedure. In the heating procedure, the at least two radiating portions are operated according to at least one operational configuration that is selected on the basis of the data obtained in the learning procedure.