Method and computer program for controlling a fryer, and fryer arranged for carrying out such method
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Solution Overview
Problem
Existing deep fat fryers for professional use require laborious and error-prone manual input of optimal cooking parameters, making it cumbersome to achieve consistent food quality across multiple cooking cycles.
Innovation Solution
A method and fryer system that includes a learning frying run to determine optimal cooking parameters, using a manual validation interface to communicate the desired frying condition, allowing the logic unit to adjust subsequent frying times based on thermal energy supplied during the learning run, ensuring consistent results across varying food batch sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual input of optimal cooking parameters is used, then cooking precision can be achieved, but operation complexity and time consumption increase significantly
Solution Approach 1:
The system performs preliminary learning runs to automatically determine optimal cooking parameters before actual production cooking. The logic unit stores these learned parameters and automatically applies them in subsequent cooking operations, eliminating the need for manual parameter input while maintaining cooking precision.
Solution Approach 2:
The fryer system performs self-learning and self-adjustment of cooking parameters through the learning run process. The logic unit automatically analyzes cooking results and adjusts parameters without human intervention, making the system self-sufficient and eliminating manual parameter setting requirements.
2Reliability
If manual determination of cooking parameters is used, then optimal cooking can be achieved, but time consumption and error probability increase
Solution Approach 1:
The system performs preliminary learning runs to automatically determine optimal cooking parameters before actual production cooking. The logic unit stores these learned parameters and automatically applies them in subsequent cooking operations, eliminating the need for manual parameter input while maintaining cooking precision.
Solution Approach 2:
The system uses feedback from the learning run where the operator validates the desired frying condition. The logic unit processes this feedback and automatically determines the optimal cooking parameters, creating a closed-loop system that ensures reliable and consistent cooking quality without manual parameter setting time.
3Manufacturing precision
If learning runs are performed to determine optimal parameters, then cooking consistency improves, but initial time investment increases
Solution Approach 1:
The system performs preliminary learning runs to automatically determine optimal cooking parameters before actual production cooking. The logic unit stores these learned parameters and automatically applies them in subsequent cooking operations, eliminating the need for manual parameter input while maintaining cooking precision.
Solution Approach 2:
The system changes the operational mode from manual parameter setting to automatic parameter determination through learning runs. The logic unit transforms the cooking process by automatically adjusting time and temperature parameters based on the learned optimal conditions, ensuring consistent food quality across multiple cooking batches.
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 system simplifies the determination of optimal cooking parameters, reducing human error and ensuring consistent food quality by automatically adjusting frying time based on thermal energy, allowing quick and reliable cooking of different food quantities.
Implementation Method 1
a cooking method wherein food is submerged in relatively large quantities of edible cooking oils at high temperature
Implementation Method 2
food is submerged in relatively large quantities of edible cooking oils at high temperature
Data Source
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AI summary
The present invention is related to a method for controlling a fryer (1) comprising a vat (3), a logic unit (9) and a manual validation interface (70, 72), the method comprising the following steps: S.1) carrying out a learning frying run, in its turn comprising the following steps: S.1.1) placing a first batch of food to be fried in a cooking medium contained in the vat (3), the food to be fried being of a predetermined kind; S.1.2) when the food to be fried reaches a desired frying condition, communicating to the logic unit (9) through the manual validation interface (70, 72) that the food has reached said desired frying condition; S.2) carrying out a successive frying run comprising the following steps: S.2.1) placing a second batch of food to be fried in the cooking medium contained in the vat (3), the food of the second batch being substantially of the same kind as of the food of the first batch; S.2.2) frying the second batch of food for a frying time determined by the logic unit (9) depending on the desired frying condition signalled through the manual validation interface (70, 72) during the learning frying run. The invention is also related to a fryer (1) programmed or however arranged for carrying out the method and comprising a vat (3), a logic unit (9) and a manual validation interface (70, 72), and to a computer program arranged for enabling the fryer (1) to carry out the method